Establishing a method for predicting tree down specification based on historical meteorological data and post-disaster inspection investigation by unmanned aerial vehicle

Abstract

This invention proposes a method for predicting tree fall specifications based on historical meteorological data and UAV post-disaster inspection surveys. It integrates historical typhoon data, UAV inspection data, and geographic information data to form a comprehensive and multi-dimensional dataset. Through in-depth analysis of various influencing factors, key features are identified, and advanced machine learning methods such as regression and deep learning models are used to construct the model. Simulation optimization is employed to handle complex data relationships, improving prediction accuracy and reliability. Scientific evaluation indicators such as accuracy, recall, F1 score, and mean squared error are set, and k-fold cross-validation is used to comprehensively evaluate the model's generalization ability. Grid search and random search methods are used to fine-tune the model parameters. The constructed real-time monitoring and early warning system, along with post-disaster response and recovery strategies, effectively reduces tree fall damage and plays a positive role in protecting urban greening and the ecological environment.

Description

Technical Field

[0001] This invention relates to the field of target detection technology, and more specifically, to a method for predicting tree fall specifications based on historical meteorological data and UAV post-disaster inspection and survey. Background Technology

[0002] Trees play a crucial role in urban greening and natural ecosystems. They not only add vitality and beauty to cities but also play an indispensable role in purifying the air, regulating the climate, and protecting the soil. However, the phenomenon of fallen trees poses a serious threat to urban greening and the natural environment. When trees fall, they often cause traffic disruptions, affecting people's daily travel and logistics; they may also damage infrastructure such as electricity and communications, leading to widespread power outages and communication interruptions; and they may even directly harm the lives and property of surrounding buildings, vehicles, and pedestrians.

[0003] Traditional methods for predicting tree fall mainly rely on human experience and simple statistical analysis. Human experience is highly subjective and limited; different individuals may arrive at different conclusions due to differences in experience, making it difficult to establish a unified and accurate prediction standard. Simple statistical analysis methods often only consider a limited number of factors, such as tree species and age, while ignoring numerous complex and interrelated environmental factors and the tree's own characteristics. This significantly reduces the accuracy of the prediction results, making it difficult to meet the needs of modern, sophisticated disaster management and prevention.

[0004] Existing methods for predicting tree fall have many limitations.

[0005] When making predictions based solely on historical meteorological data, only the impact of meteorological factors such as wind speed and rainfall on tree lodging is often considered, while neglecting the tree's own characteristics, such as species, height, diameter, and health condition. Different tree species have different root systems, wood strength, and other characteristics, and their tolerance to meteorological disasters such as wind also varies. For example, some tree species with shallow root systems are more prone to lodging under strong winds; the height and diameter of a tree affect its center of gravity and stress distribution, with taller trees with smaller diameters being relatively more likely to lodging; the health condition of the tree, such as the presence of pests and diseases, and the degree of trunk decay, also has a significant impact on its risk of lodging. It is difficult to comprehensively and accurately predict the size of tree lodging based solely on historical meteorological data.

[0006] Prediction methods based on single-source data, whether considering only tree characteristics or environmental factors, suffer from incomplete information. Single-source data cannot comprehensively account for the complex interactions between trees and the environment, leading to significant uncertainty and inaccuracy in the prediction results. In reality, tree lodging is the result of multiple factors working together. For example, under the same wind speed conditions, trees growing in loose soil with poorly developed root systems may lodging, while trees growing in compact, well-drained soil with healthy, well-developed root systems may remain unharmed. Multi-source data fusion can integrate historical meteorological data, tree characteristic data, and geographic information, thereby providing a more comprehensive and accurate analysis of tree lodging risk and improving the accuracy and reliability of predictions.

[0007] Existing patent CN 117765466 A discloses a method for detecting fallen trees, but it is also a prediction method based on single-source data, only considering the characteristics of the trees themselves; Existing patent CN 114078211 A discloses a method for intelligent detection of fallen trees based on lidar, which performs growth based on ground seed points for suspected fallen trees, sets a growth bending threshold, and determines that the tree has fallen if its height is less than a minimum tree height threshold during growth. However, since the tree growth time is long and there are many influencing factors, the prediction results have great uncertainty and inaccuracy.

[0008] Therefore, research on multi-source data fusion prediction methods based on historical meteorological data combined with UAV post-disaster inspections has extremely important practical significance. Summary of the Invention

[0009] In view of this, to address the aforementioned problems, this invention proposes a method for predicting tree fall specifications based on historical meteorological data combined with post-disaster drone inspections. This method aims to fully utilize the rich information in historical meteorological data, such as typhoon paths, wind speeds, and rainfall, as well as detailed data on tree health status, species, height, and diameter obtainable through drone inspections. Through advanced data analysis techniques and modeling methods, a high-precision tree fall specification prediction model is constructed. This model can predict the possible fall specifications and areas of trees before a disaster strikes, providing a strong basis for relevant departments to formulate scientific and reasonable disaster response strategies. This enables the early deployment of protective measures, such as reinforcing high-risk trees and evacuating people and managing traffic in disaster-prone areas. This effectively reduces the various risks and losses caused by tree fall, ensures the stability and safety of urban greening and the natural environment, enhances the disaster response capacity and resilience of urban and natural ecosystems, and helps to fill the gaps in existing prediction methods, providing a more scientific and accurate solution for tree fall prediction.

[0010] A method for predicting tree fall sizes based on historical meteorological data and post-disaster drone inspections includes the following steps:

[0011] Step S1: Data collection and integration, collecting and integrating historical typhoon data, drone inspection data, and geographic information data;

[0012] Step S2, Data Analysis and Feature Selection: Based on the data processed in Step S1, analyze the factors affecting tree fall and determine key features;

[0013] Step S3, Machine learning model construction: Based on the key features determined in step S2, a regression model is built and trained in TensorFlow, and CNN and RNN / LSTM models are used to predict tree fall.

[0014] Step S4: Simulation and model optimization. A physical model is established to simulate the stress distribution of trees under different wind speeds and directions. The simulation results of the physical model are combined with the machine learning model in step S3 to optimize the machine learning model.

[0015] Step S5, Model Evaluation and Continuous Improvement: Set evaluation metrics and perform cross-validation on the machine learning model optimized in Step S4; adjust model parameters by using grid search or random search methods; expand the data sources of tree root structure data; update the machine learning model; and fuse different algorithms.

[0016] Step S6: Based on the updated machine learning model from step S5, monitor and predict tree fall specifications in real time, construct a pre-disaster real-time monitoring and early warning system, and formulate post-disaster response and recovery strategies.

[0017] Furthermore, in step S1, the data collection and integration steps mainly include:

[0018] Step S11: Historical meteorological data collection and organization. Obtain historical typhoon data by accessing the meteorological database through the API interface and acquiring information on typhoon paths, wind speeds, and rainfall over the years.

[0019] Step S12: Data collection and processing of drone inspections. Data on the health status (fallen), species, height, and diameter of trees are collected multiple times by drones before and after the disaster to obtain drone inspection data.

[0020] Step S13: Integration of geographic information data. Geographic information system (GIS) technology is used to collect information on topography, soil type, and vegetation coverage, and integrate the geographic information data.

[0021] Furthermore, in step S1, satellite remote sensing data or ground sensor data can be introduced to enrich the data sources.

[0022] Furthermore, the specific process of step S2 is as follows:

[0023] Step S21: Based on the data collected in step S1, analyze the factors that affect tree falling, such as meteorological factors, tree characteristics, and geographical environment factors.

[0024] Step S22: Using statistical analysis methods, such as correlation analysis and regression analysis, calculate the correlation coefficient between each feature and tree fall, and select the features with high correlation as key features.

[0025] Furthermore, the specific process of step S3 is as follows:

[0026] Step S31: Train a regression model to predict the size of fallen trees. When training the regression model, use actual tree fall data after previous typhoons as labels. The regression model includes a linear regression model, a decision tree model, and a random forest model.

[0027] Step S32: Use a convolutional neural network (CNN) to process the drone image data and identify the health status and structural features of the trees;

[0028] Step S33: Process time series weather data using a recurrent neural network (RNN) or a long short-term memory network (LSTM).

[0029] Furthermore, in step S3, other machine learning algorithms, such as support vector machines (SVM) and gradient boosting trees (GBDT), can be used for model replacement and optimization.

[0030] Furthermore, the specific process of step S4 is as follows: establish a physical model to simulate the stress distribution of trees under different wind speeds and directions, combine the simulation results of the physical model with the machine learning model, and optimize the prediction model.

[0031] Furthermore, the specific process of step S5 is as follows:

[0032] Step S51: Set evaluation metrics to evaluate the tree fall size prediction model, including accuracy, recall, F1 score, and mean squared error (MSE).

[0033] Step S52: Use k-fold cross-validation to evaluate the accuracy of the model;

[0034] Step S53: Adjust the model parameters using either Grid Search or Random Search, introduce data sources of tree root structure data, update the machine learning model, and fuse different algorithms.

[0035] Furthermore, in step S53, the grid search uses an exhaustive method to traverse different combinations of parameter values ​​within a pre-defined range of parameter values, and uses cross-validation to evaluate the performance of the model under each combination, thereby finding the optimal parameter combination; the random search evaluates the parameter combinations by randomly selecting them within the range of parameter values.

[0036] Furthermore, the specific process of step S6 is as follows:

[0037] Step S61: Develop a web-based real-time monitoring platform to quickly update tree status data and make real-time predictions before a typhoon arrives, integrate prediction models, and build a real-time monitoring and early warning system.

[0038] Step S62: Based on the forecast data, formulate a scientific and reasonable post-disaster relief plan, quickly locate and deal with severely affected areas, and improve recovery efficiency.

[0039] The beneficial effects of this invention are:

[0040] 1. Data diversity and comprehensiveness: It integrates historical typhoon data, drone inspection data and geographic information data to form a comprehensive and multi-dimensional dataset, providing a multi-source data fusion prediction method that can more comprehensively analyze the risk of tree fall.

[0041] 2. High-precision prediction: Utilizing advanced machine learning and deep learning models (such as CNN, RNN / LSTM), we conduct in-depth analysis of various influencing factors, identify key features, and optimize through simulation to effectively handle complex data relationships, thereby improving the accuracy and reliability of predictions.

[0042] 3. Real-time performance and dynamism: Scientific evaluation indicators such as accuracy, recall, F1 score and mean squared error are set, k-fold cross-validation technology is used to comprehensively evaluate the model's generalization ability, and grid search, random search and other methods are used to finely adjust the model parameters, which significantly improves the model performance;

[0043] 4. Wide applicability: The real-time monitoring and early warning system, as well as the disaster response and recovery strategy formulation, have been verified in real-world scenarios. It is not only applicable to typhoon disasters, but can also be extended to the prediction and management of tree fall in other natural disasters, effectively reducing tree fall disaster losses and playing a positive role in the protection of urban greening and the ecological environment. Attached Figure Description

[0044] Figure 1 This is a flowchart of the data collection and integration process of the present invention.

[0045] Figure 2 This is a structural diagram of the high-precision machine learning model of the present invention.

[0046] Figure 3 This is a flowchart of the Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) / Long Short-Term Memory Network (LSTM) of the present invention.

[0047] Figure 4 This is a diagram of the real-time monitoring and early warning system architecture of the present invention.

[0048] The following detailed description, in conjunction with the accompanying drawings, will further illustrate the present invention. Detailed Implementation Example 1

[0049] A method for predicting tree fall sizes based on historical meteorological data and post-disaster drone inspections includes the following steps:

[0050] Step S1: Data collection and integration, collecting and integrating historical typhoon data, drone inspection data, and geographic information data;

[0051] like Figure 1 As shown, firstly, historical meteorological data is collected and organized. The meteorological database is obtained through the API interface to obtain historical typhoon data, including typhoon paths, wind speeds, and rainfall information over the years.

[0052] Specifically, the China Meteorological Administration's typhoon website is a crucial data source. This website meticulously records key information such as the path, intensity, wind speed, and rainfall of typhoons over the years, ensuring the data's authority and accuracy. The typhoon information release platform, hosted by the Zhejiang Provincial Department of Water Resources and the Zhejiang Provincial Water Resources Information Management Center, also provides abundant typhoon-related data, including detailed information on typhoon movement paths, impact areas, and wind force changes within Zhejiang Province. This data is of significant reference value for studying the relationship between tree fall and typhoons in specific regions. Using web crawling and data download tools, a large amount of historical typhoon data was obtained from these data sources and organized and stored in structured data formats, such as CSV files or database tables, for subsequent data analysis and processing. During data organization, data cleaning is necessary to remove duplicate, erroneous, or missing records, ensuring data quality and completeness. For missing data, data interpolation or other suitable methods can be used to supplement the data, ensuring its continuity and usability.

[0053] Secondly, collect and process drone inspection data. Use drones to collect data on the health status (fallenness), species, height, and diameter of trees multiple times before and after the disaster to obtain drone inspection data.

[0054] Specifically, drone inspection data collection is mainly conducted in two phases: before and after the typhoon. Before the disaster, drones equipped with advanced equipment such as multispectral cameras and LiDAR are used to comprehensively scan trees in the target area. The collected data includes the trees' health status, analyzed through multispectral image analysis of leaf color, texture, and other characteristics to determine the presence of pests, diseases, or malnutrition; tree species information, identified using image recognition technology and relevant tree species databases; and precise measurements of tree height and diameter using LiDAR technology. This data is crucial for assessing tree stability and wind resistance. After the disaster, drones are used again to inspect trees in the same area, focusing on fallen trees, including their location, direction, and extent of fall, as well as the location and extent of damage, such as broken trunks and branches. The collected pre- and post-disaster drone data requires a series of processing steps. The first step is data preprocessing, including image correction, noise reduction, and cropping to improve image quality and clarity. Then, using specialized image processing software and algorithms, the feature data of the trees are extracted and analyzed. For example, image segmentation algorithms are used to extract the tree outlines, and then parameters such as tree height and crown width are calculated. Machine learning algorithms are used to classify and evaluate the health status of the trees, and a model for distinguishing healthy and damaged trees is established. The processed data is then correlated and integrated with historical meteorological data to provide comprehensive data support for subsequent model training and analysis.

[0055] Finally, geographic information data is integrated by using geographic information system (GIS) technology to collect information on topography, soil type, and vegetation coverage, and then integrating the geographic information data.

[0056] Specifically, the collection of geographic information data is primarily achieved through Geographic Information System (GIS) technology. Topographic data, including altitude, slope, and aspect, is obtained using satellite remote sensing imagery. These topographic factors significantly impact the growth environment and stress conditions of trees. For example, in mountainous areas, trees on steeper slopes are more susceptible to falling due to gravity and wind force. Different slope aspects also affect the amount of sunlight, water, and wind received by trees, thus influencing their growth and resistance to lodging. Soil type data can be obtained through field sampling and soil surveys, combined with existing soil databases. Different soil types vary in fertility, aeration, and water retention, thereby affecting the development and anchoring ability of tree roots. Vegetation cover data can be estimated using satellite remote sensing imagery or aerial photogrammetry. Higher vegetation cover may mitigate wind speed to some extent, protecting trees, but it may also increase competitive pressure among trees, affecting their growth. The collected geographic information data, including topography, soil type, and vegetation cover, are then integrated to construct a geographic information database. In the database, different types of data are classified, stored, and managed, and relationships between data are established so that when analyzing factors affecting tree fall, these geographic information data can be quickly accessed and comprehensively considered to conduct in-depth research on the intrinsic connection and interaction mechanism between the geographic environment and tree fall.

[0057] Step S2, Data Analysis and Feature Selection: Based on the data collected in Step S1, analyze the factors affecting tree fall and determine key features;

[0058] First, analyze the factors that influence tree falling, such as meteorological factors, tree characteristics, and geographical environment factors;

[0059] Specifically, strong winds are one of the key meteorological factors causing trees to fall. When wind speeds exceed a tree's tolerance limit, the tree will fall due to excessive wind force. Studies show that different wind speeds have significantly different effects on trees. For example, in some coastal areas hit by typhoons, wind speeds often exceed 30 meters per second, and such strong winds can generate enormous lateral forces on trees. Trees with shallow roots, such as banyan trees, are prone to falling because their root systems cannot provide sufficient anchoring force to resist the wind. Heavy rain is also closely related to tree falling. Large amounts of rainfall cause a sharp increase in soil moisture content, making the soil soft and reducing its support for tree roots. In some mountainous areas, after heavy rain, the shear strength of the soil decreases significantly, and the originally stable tree root system is prone to slippage under the combined action of gravity and wind, leading to tree falling. While lightning strikes are relatively less likely to directly cause tree falling, lightning strikes can cause internal damage to the trunk, weakening the tree's structural strength and making it more susceptible to falling in subsequent windy and rainy weather. For example, some tall pine trees, after being struck by lightning, have their internal xylem damaged, making them more prone to breaking and falling during subsequent strong winds.

[0060] Specifically, tree species have a significant impact on lodging risk. Different tree species have varying physical properties of wood, such as hardness and toughness, as well as different root systems. For example, poplars grow rapidly, have relatively soft wood, and shallow root systems, making them more prone to lodging in strong winds compared to some hardwoods like oak. Tree height and diameter are also crucial factors. Generally, taller trees have a higher center of gravity, resulting in greater bending moments under wind force and a higher risk of lodging. Trees with smaller diameters have relatively weaker trunk load-bearing capacity. A tree 20 meters tall and 30 centimeters in diameter may be more likely to lodging in strong winds than a tree 15 meters tall and 40 centimeters in diameter. The health of the tree also significantly affects the risk of lodging. Trees infested with pests and diseases may have hollowed-out or eroded trunks and branches, greatly reducing their structural strength. Trees damaged by termites may break and fall even under normal wind conditions.

[0061] Specifically, topography has a significant impact on tree stability. On hillsides, especially steep slopes, trees are more prone to falling downhill due to the component of gravity. For example, on steep slopes beside mountain roads, trees often fall towards the road after heavy rain due to soil erosion and gravity. Different soil types have different physical properties such as fertility, permeability, water retention, and shear strength, which affect the growth and anchoring ability of tree roots. For example, sandy soils have poor water retention and low interparticle cohesion, making it difficult for tree roots to form a stable anchoring system. In contrast, clay soils, while having good water retention, have poor permeability, which may affect root growth and function, and under certain meteorological conditions, increase the risk of tree falling. In areas with high vegetation cover, trees compete for resources such as sunlight, water, and nutrients, which may lead to poor growth and underdeveloped root systems in some trees, thus increasing the likelihood of falling. Moreover, dense vegetation can alter the movement patterns of local airflow, which in some cases may make the wind force on trees more complex and concentrated, further increasing the risk of them falling over.

[0062] Secondly, statistical analysis methods were used to determine the key characteristics affecting tree lodging, such as the relationship between tree height, diameter and lodging risk.

[0063] Specifically, by analyzing a large amount of historical data, key characteristics influencing tree lodging are identified. For example, tree height is positively correlated with lodging risk; generally, for every meter increase in height, the risk of lodging may increase by a certain proportion, the specific value of which can be calculated through data analysis and models. Tree diameter is negatively correlated with lodging risk; the larger the diameter, the stronger the trunk's load-bearing capacity, and the relatively lower the risk of lodging. This relationship can be quantified by establishing mathematical models, such as a lodging risk index that is directly proportional to tree height and inversely proportional to the square of the tree diameter. Besides height and diameter, tree species, health status, terrain slope, and soil type are also considered in the model. Statistical analysis methods, such as correlation analysis and regression analysis, are used to calculate the correlation coefficients between each characteristic and tree lodging, and features with high correlation are selected as key features. For example, analysis of a large amount of tree data in a certain region reveals that under specific terrain and soil conditions, when a certain type of tree exceeds a certain height and its health status is poor, the risk of lodging increases significantly. These characteristics are thus identified as key features for predicting tree lodging in that region, providing an important basis for subsequent model construction.

[0064] Step S3, Machine learning model construction: Based on the key features determined in step S2, a regression model is built and trained in TensorFlow, and CNN and RNN / LSTM models are used to predict tree fall.

[0065] like Figure 2 As shown, firstly, a regression model is trained to predict the size of fallen trees. When training the regression model, actual tree fall data after previous typhoons are used as labels. The regression model includes a linear regression model, a decision tree model, and a random forest model.

[0066] Specifically, linear regression models can be used to establish a linear relationship between tree lodging specifications and influencing factors. For example, using tree height, diameter, and wind speed as independent variables, and lodging angle and distance as dependent variables, a linear equation is fitted using the least squares method to predict tree lodging specifications under different conditions. However, linear regression models assume a linear relationship between variables, which may result in poor fitting for some complex nonlinear relationships. Decision tree models, on the other hand, can handle nonlinear relationships. They construct a tree-like decision structure by continuously dividing data features. For example, based on tree species, height, diameter, and meteorological conditions, the range of values ​​for each feature is determined sequentially to determine the tree's lodging risk level or predict its lodging specifications. Decision tree models are easy to understand and interpret, but they are prone to overfitting and are highly dependent on training data. Random forest models are an ensemble learning method based on decision trees. By constructing multiple decision trees and averaging or voting on the results of these trees, they improve the model's accuracy and generalization ability. Random forest models can handle large amounts of features and data, and are well adapted to complex data distributions. In tree fall prediction, they can comprehensively consider the complex interactions of multiple factors, reduce the risk of overfitting of single decision tree models, and improve the stability and reliability of predictions.

[0067] Secondly, convolutional neural networks (CNNs) are used to process drone imagery data to identify the health status and structural features of trees;

[0068] Finally, recurrent neural networks (RNNs) or long short-term memory networks (LSTMs) are used to process time-series weather data;

[0069] Specifically, such as Figure 3As shown, Convolutional Neural Networks (CNNs) have unique advantages in processing drone imagery data. Through structures such as convolutional layers, pooling layers, and fully connected layers, they automatically extract features from images, such as tree outlines, textures, and foliage density, to determine the health and structural characteristics of trees. For example, CNNs can identify color changes in tree leaves to determine if there are pests or diseases causing yellowing; by analyzing tree outlines and textures, they can assess whether the tree's growth pattern is normal and whether there are potential risks such as trunk tilting or branch breakage. Recurrent Neural Networks (RNNs) and their variant, Long Short-Term Memory Networks (LSTMs), are suitable for processing time-series weather data. They can learn temporal dependencies in meteorological data, such as the changing trends of wind speed and rainfall at different times and their impact on tree lodging. For example, an RNN can predict the probability of tree lodging under future weather conditions based on wind speed and rainfall data from the previous few days because it can "remember" relevant information from past weather data and combine it with current data for analysis, thereby better capturing the long-term and short-term effects of meteorological factors on tree lodging and providing more comprehensive information support for tree lodging prediction.

[0070] Step S4: Simulation and model optimization. A physical model is established to simulate the stress distribution of trees under different wind speeds and directions. The simulation results of the physical model are combined with the machine learning model in step S3 to optimize the machine learning model.

[0071] Specifically, physical models are established to simulate the stress distribution of trees under different wind speeds and directions. For example, using the finite element method (FEM), the tree's trunk, branches, and root system are discretized into a finite number of elements. Each element is assigned corresponding mechanical properties based on the tree's material characteristics and geometry, simulating the stress and strain of different parts of the tree under different wind speeds and directions. Simulation analysis reveals that under strong winds, the bending moment and shear force at the tree roots are large, and the upper and middle parts of the trunk are prone to significant bending stress; these areas are key weak points where trees are prone to falling. The simulation results from the physical model are combined with machine learning models to optimize the prediction model. For example, the tree stress distribution characteristics obtained from the physical model can be used as new feature variables in the machine learning model, or the results of the physical model can be used to correct and verify the prediction results of the machine learning model. If the machine learning model predicts that a tree will not fall under a specific wind speed, but the physical model shows that the root stress is close to its limit under the corresponding conditions, potentially leading to falling, then the parameters or features of the machine learning model need to be adjusted to improve the accuracy of the prediction. By combining the advantages of physical models in terms of mechanical principles and machine learning models in terms of big data processing and complex relationship modeling, the predictive model can more accurately predict the specifications of tree fall, providing a more reliable basis for disaster prevention and response.

[0072] Step S5, Model Evaluation and Continuous Improvement: Set evaluation metrics and perform cross-validation on the machine learning model optimized in Step S4; adjust model parameters by using grid search or random search methods; expand the data sources of tree root structure data; update the machine learning model; and fuse different algorithms.

[0073] First, establish evaluation indicators to evaluate the tree lodging specification prediction model;

[0074] Specifically, in tree fall size prediction models, commonly used evaluation metrics include accuracy, recall, F1 score, and mean squared error (MSE). Accuracy refers to the proportion of correctly predicted samples out of the total sample size; it reflects the overall predictive accuracy of the model. In tree fall prediction, high accuracy means the model can accurately determine which trees will fall and their size from a large sample size, thus providing a reliable basis for disaster prevention. For example, when predicting the fall of an area containing 1000 trees, if the model accurately predicts the fall of 800 trees, the accuracy is 800 / 1000 = 0.8. Recall refers to the proportion of trees that actually fall that the model correctly predicts; it measures the model's ability to capture positive samples (fallen trees). If 200 trees actually fall, and the model correctly predicts 160, then the recall rate is 160 / 200 = 0.8. The F1 score is a comprehensive indicator that considers both precision and recall. It provides a more holistic evaluation of model performance, avoiding misjudgments based solely on high or low precision or recall. For scenarios with low tolerance for missed reports of fallen trees, such as predicting tree falls around important transportation hubs, the importance of recall and the F1 score may be even more pronounced. Mean squared error (MSE) is primarily used to assess the average difference between the predicted and actual values ​​of a regression model. In predicting tree fall sizes, such as continuous variables like the fall angle or distance, MSE reflects the accuracy of the model's predictions. A smaller MSE indicates that the model's predicted fall size is closer to the actual situation, and the better the model's prediction performance. For example, if a model predicts that a tree will fall at an angle of 35 degrees, but the actual angle is 33 degrees, the calculated mean square error can reflect the average level of this prediction bias across the entire dataset, thus helping to assess the model's accuracy in predicting fall specifications.

[0075] Secondly, the accuracy of the model is evaluated using the K-fold cross-validation method;

[0076] Specifically, cross-validation is a commonly used model evaluation technique. Its principle is to divide the dataset into multiple subsets and comprehensively evaluate the model's generalization ability through multiple different partitions and training-validation processes. In this study, the k-fold cross-validation method is used, with the following steps: First, the collected dataset, including historical meteorological data, UAV inspection data, and geographic information data, is randomly divided into k subsets of similar size, for example, k can be 5 or 10. Then, one subset is selected sequentially as the validation set, and the remaining k-1 subsets are used as the training set. After each partition, the model is trained using the training set. For example, tree feature data and corresponding lodging data in the training set are used to train machine learning models such as decision trees and random forests, adjusting the model's parameters to adapt to the training data. Next, the trained model is evaluated using the validation set, calculating the aforementioned evaluation metrics such as accuracy, recall, F1 score, or mean squared error to understand the model's performance on data not used in training. Through k iterations, each using a different subset as the validation set, k evaluation results are obtained. Finally, by combining these k evaluation results, for example by calculating their average, we obtain the final evaluation metric for the model. This cross-validation process can effectively avoid model evaluation bias caused by unreasonable dataset partitioning, more comprehensively and accurately evaluate the model's generalization ability on different data subsets, and ensure that the model can also have good predictive performance when faced with new and unseen data, thereby improving the reliability and stability of the model in practical tree fall prediction applications.

[0077] Next, the model parameters are adjusted using either a grid search or a random search method;

[0078] Specifically, model parameters have a significant impact on prediction results. Taking the random forest model as an example, the learning rate determines the magnitude of error adjustment in each iteration. An excessively large learning rate may cause the model to skip optimal solutions during training, while an excessively small learning rate will slow down the training process and make it prone to getting stuck in local optima. The number of iterations controls the number of training rounds. Too many iterations may lead to overfitting, while too few iterations may prevent the model from fully learning the patterns in the data, resulting in underfitting. In decision tree models, parameters such as tree depth and node splitting criteria also affect model complexity and prediction performance. For deep learning models such as convolutional neural networks, parameters such as kernel size of convolutional layers, stride of pooling layers, and number of neurons in fully connected layers all affect the model's ability to extract features from UAV imagery data and the final prediction results. To optimize the model, methods such as grid search or random search can be used. Grid search uses an exhaustive approach, traversing different combinations of parameter values ​​within a predefined range. Cross-validation is then used to evaluate the model's performance under each combination, ultimately finding the optimal parameter combination. For example, the learning rate for a random forest model can be set to a range such as [0.01, 0.1, 0.2], with iterations set to [50, 100, 150], and then a comprehensive search is performed on these combinations. Random search, on the other hand, randomly selects parameter combinations within the parameter range for evaluation. It is more flexible than grid search and may have lower computational costs, making it particularly suitable for scenarios with large parameter spaces. By continuously optimizing the model's parameter settings through these parameter adjustment methods, the model's prediction accuracy and generalization ability are improved, enabling it to more accurately predict tree fall sizes and providing more effective support for disaster management and prevention.

[0079] Next, we introduce the data source for tree root structure data;

[0080] Specifically, building upon current research, introducing new data sources can significantly improve the accuracy and reliability of tree lodging size prediction. Tree root structure data is a highly promising supplementary data source. Factors such as root depth, breadth, diameter, number, and health status are all closely related to tree stability. For example, trees with deep and extensive root systems are better anchored in the soil when facing strong winds, thus reducing the risk of lodging. Detecting tree roots using specialized equipment such as ground-penetrating radar can obtain detailed root structure data, and incorporating this data into predictive models helps to more accurately assess a tree's resistance to lodging. Long-term tree growth monitoring data also plays a crucial role in improving predictive models. This type of data includes information on changes in tree height, diameter growth, and crown expansion over many years, reflecting long-term changes in tree growth trends and health status. For example, a rapidly growing tree with low xylem density may appear healthy in the short term, but long-term growth data suggests its risk of lodging may be gradually increasing. By combining long-term tree growth monitoring data with historical meteorological data, drone inspection data, and geographic information data, we can gain a more comprehensive understanding of how trees respond to various environmental factors at different growth stages. This will help optimize tree lodging size prediction models, improve the accuracy and timeliness of predictions, and provide a more scientific and effective basis for disaster prevention and tree management.

[0081] Next, the machine learning model is updated, and different algorithms are merged;

[0082] Specifically, with the continuous development of machine learning and deep learning, advanced algorithms have provided new opportunities to improve the performance of tree fall prediction models. Reinforcement learning is a noteworthy machine learning algorithm that uses an agent to continuously experiment and learn in the environment to obtain optimal decision-making strategies. In tree fall prediction, trees can be considered agents, and environmental factors (such as weather conditions and geographical environment) can be considered environmental states. Trees continuously "perceive" changes in environmental states and receive corresponding "rewards" (such as maintaining uprightness and reducing the risk of falling) based on their own "actions" (such as adjusting growth direction and strengthening root anchorage). Through reinforcement learning algorithms, tree fall prediction models can more dynamically adapt to environmental changes and predict the likelihood of trees falling in different environments. Generative Adversarial Networks (GANs) are also an innovative deep learning algorithm. They consist of a generator and a discriminator. Through adversarial training between the two, the generator can learn the potential distribution patterns of data, thereby generating new data similar to real data. In tree fall prediction, GANs can be used to generate simulated tree data, including tree characteristics and fall conditions under different species, growth stages, and environmental conditions. This simulated data can be used to expand the training dataset, increase the model's generalization ability, and enable the model to better cope with various complex situations. Furthermore, the integration of different algorithms can be explored, such as combining convolutional neural networks (CNNs) and recurrent neural networks (RNNs). This leverages the advantages of CNNs in image feature extraction and the strengths of RNNs in processing time-series data to construct a more powerful hybrid model, thereby improving the accuracy and reliability of tree fall size prediction and providing stronger technical support for disaster management and urban greening protection.

[0083] Step S6: Based on the updated machine learning model from step S5, monitor and predict tree fall specifications in real time, construct a pre-disaster real-time monitoring and early warning system, and formulate post-disaster response and recovery strategies.

[0084] First, develop a web-based real-time monitoring platform to quickly update tree status data and make real-time predictions before a typhoon arrives, integrate prediction models, and build a real-time monitoring and early warning system.

[0085] Specifically, build a real-time monitoring and early warning system, such as Figure 4As shown in the diagram, the real-time monitoring and early warning system architecture includes a data acquisition layer, a data transmission layer, a data processing layer, and an early warning release layer. The data acquisition layer acquires tree characteristic data and meteorological data through regular drone inspections and data integration with meteorological departments. Drones, equipped with high-definition cameras and multispectral sensors, scan trees within the monitoring area along predetermined routes, collecting images, spectra, and other information. After data processing, characteristic data such as tree species, height, diameter, and health status are extracted and transmitted in real-time to the data processing center. The meteorological department monitors meteorological data such as wind speed, rainfall, and wind direction in real-time through a network of meteorological monitoring stations and transmits this data synchronously to the data processing center. The data transmission layer uses wireless communication technologies such as 4G / 5G to ensure efficient and stable data transmission, transmitting the collected tree characteristic data and meteorological data from the acquisition point to the data processing center. In the data processing layer, the received data undergoes preprocessing, including data cleaning and format conversion. Then, a trained tree fall prediction model is used to analyze and predict the data, calculating the probability of tree fall and the magnitude of fall. Based on the prediction results of the data processing layer, when the probability of tree fall risk exceeds a preset threshold, the early warning release layer can issue early warning information to relevant management departments, garden maintenance personnel, and surrounding residents through various scalable methods. The early warning information includes the location of the tree, the fall risk level, and the expected direction and range of fall, so that corresponding protective measures can be taken in a timely manner, such as supporting and reinforcing high-risk trees and evacuating surrounding personnel and vehicles. This enables real-time monitoring and early warning of tree fall disasters, effectively reducing disaster losses.

[0086] Secondly, based on the forecast data, formulate a scientific and reasonable post-disaster relief plan, quickly locate and deal with severely affected areas, and improve recovery efficiency;

[0087] Specifically, a scientific post-disaster relief plan should be developed based on forecast data. First, based on information provided by the tree fall prediction model, priority areas for clearing should be identified. Priority should be given to clearing fallen trees in key areas such as main roads, around power facilities, and densely populated residential areas to restore traffic, power supply, and residents' daily lives as quickly as possible. For example, on main urban roads, fallen trees severely obstruct traffic, and manpower and equipment should be prioritized for clearing. For fallen trees near substations and high-voltage lines, timely clearing is necessary to ensure the safe and stable operation of power facilities and prevent secondary disasters caused by trees contacting power lines. A reasonable resource allocation plan should be developed based on the tree fall situation and resource needs in different areas. The required number of manpower and machinery, such as cranes, chainsaws, and transport vehicles, should be calculated and rationally allocated to each affected area. For example, in areas with a high density of fallen trees, the number of cranes and transport vehicles should be increased to improve clearing efficiency; for areas with many large fallen trees, professional loggers and high-powered chainsaws should be deployed. By integrating Geographic Information System (GIS), the cleared areas are planned to determine the species, quantity, and location of trees to be replanted in order to restore urban greening and the ecological environment. Based on local climate, soil conditions, and urban planning needs, suitable tree species are selected for replanting. For example, wind-resistant species are chosen in coastal areas, while species with both ornamental and ecological functions are selected in parks and residential areas. A rational planting layout is then planned to promote the sustainable development of urban greening and the ecosystem.

[0088] The beneficial effects of this invention are:

[0089] 1. Data diversity and comprehensiveness: It integrates historical typhoon data, drone inspection data and geographic information data to form a comprehensive and multi-dimensional dataset, providing a multi-source data fusion prediction method that can more comprehensively analyze the risk of tree fall.

[0090] 2. High-precision prediction: Utilizing advanced machine learning and deep learning models (such as CNN, RNN / LSTM), we conduct in-depth analysis of various influencing factors, identify key features, and optimize through simulation to effectively handle complex data relationships, thereby improving the accuracy and reliability of predictions.

[0091] 3. Real-time performance and dynamism: Scientific evaluation indicators such as accuracy, recall, F1 score and mean squared error are set, k-fold cross-validation technology is used to comprehensively evaluate the model's generalization ability, and grid search, random search and other methods are used to finely adjust the model parameters, which significantly improves the model performance;

[0092] 4. Wide applicability: The real-time monitoring and early warning system, as well as the disaster response and recovery strategy formulation, have been verified in real-world scenarios. It is not only applicable to typhoon disasters, but can also be extended to the prediction and management of tree fall in other natural disasters, effectively reducing tree fall disaster losses and playing a positive role in the protection of urban greening and the ecological environment.

[0093] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method for predicting tree down specification based on historical meteorological data and post-disaster UAV inspection investigation, characterized in that: The method includes the following steps: Step S1, Data Collection and Integration: Collect and integrate historical typhoon data, drone inspection data, and geographic information data; Step S2, Data Analysis and Feature Selection: Based on the data processed in Step S1, analyze the factors affecting tree fall and determine key features; Step S3: Machine learning model construction. Based on the key features determined in step S2, a regression model is built and trained in TensorFlow, and CNN and RNN / LSTM models are used to predict tree fall. Step S4: Simulation and model optimization. A physical model is established to simulate the stress distribution of trees under different wind speeds and directions. The simulation results of the physical model are combined with the machine learning model in step S3 to optimize the machine learning model. Step S5, Model Evaluation and Continuous Improvement: Set evaluation metrics and perform cross-validation on the machine learning model optimized in Step S4; adjust model parameters by using grid search or random search methods; expand the data sources of tree root structure data; update the machine learning model; and fuse different algorithms. Step S6 involves real-time monitoring and prediction of tree fall specifications based on the updated machine learning model from step S5, constructing a pre-disaster real-time monitoring and early warning system, as well as a post-disaster response and recovery strategy.

2. The method for predicting tree fall specifications based on historical meteorological data and UAV post-disaster inspection and survey as described in claim 1, characterized in that: In step S1, the data collection and integration steps mainly include: Step S11: Historical meteorological data collection and organization. Obtain historical typhoon data by accessing the meteorological database through the API interface and acquiring information on typhoon paths, wind speeds, and rainfall over the years. Step S12: Data collection and processing of drone inspections. The drone inspection data is obtained by collecting data on the health status (fallen trees), species, height, and diameter of trees multiple times before and after the disaster. Step S13: Integration of geographic information data. Geographic information system (GIS) technology is used to collect information on topography, soil type, and vegetation coverage, and integrate the geographic information data. 3.The method of claim 1, wherein the method further comprises: determining a tree fall direction of the tree based on the historical meteorological data and the UAV post-disaster inspection data. In step S1, satellite remote sensing data or ground sensor data can be introduced to enrich the data sources. 4.The method of claim 1, wherein the method further comprises: determining a tree fall direction of the tree based on the historical meteorological data and the UAV post-disaster inspection data. The specific process of step S2 is as follows: Step S21: Based on the data collected in step S1, analyze the factors that affect tree falling, such as meteorological factors, tree characteristics, and geographical environment factors. Step S22: Using statistical analysis methods, such as correlation analysis and regression analysis, calculate the correlation coefficient between each feature and tree fall, and select the features with high correlation as key features.

5. The method for predicting tree fall specifications based on historical meteorological data and UAV post-disaster inspection and survey as described in claim 1, characterized in that: The specific process of step S3 is as follows: Step S31: Train a regression model to predict the size of fallen trees. When training the regression model, use actual tree fall data after previous typhoons as labels. The regression model includes a linear regression model, a decision tree model, and a random forest model. Step S32: Use a convolutional neural network (CNN) to process the drone image data and identify the health status and structural features of the trees; Step S33: Process time series weather data using a recurrent neural network (RNN) or a long short-term memory network (LSTM).

6. The method for predicting tree fall specifications based on historical meteorological data and UAV post-disaster inspection and survey as described in claim 1, characterized in that: In step S3, other machine learning algorithms, such as support vector machines (SVM) and gradient boosting trees (GBDT), can be used for model replacement and optimization.

7. The method for predicting tree fall specifications based on historical meteorological data and UAV post-disaster inspection and survey as described in claim 1, characterized in that: The specific process of step S4 is as follows: establish a physical model to simulate the stress distribution of trees under different wind speeds and directions, combine the simulation results of the physical model with the machine learning model, and optimize the prediction model.

8. The method for predicting tree fall specifications based on historical meteorological data and UAV post-disaster inspection and survey as described in claim 1, characterized in that: The specific process of step S5 is as follows: Step S51: Set evaluation metrics to evaluate the tree fall size prediction model, including accuracy, recall, F1 score, and mean squared error (MSE). Step S52: Use k-fold cross-validation to evaluate the accuracy of the model; Step S53: Adjust the model parameters using either Grid Search or Random Search, introduce data sources of tree root structure data, update the machine learning model, and fuse different algorithms.

9. The method for predicting tree fall specifications based on historical meteorological data and UAV post-disaster inspection and survey as described in claim 1, characterized in that: In step S53, the grid search uses an exhaustive method to traverse different combinations of parameter values ​​within a pre-defined range of parameter values, and uses cross-validation to evaluate the performance of the model under each combination, thereby finding the optimal parameter combination; the random search evaluates the parameter combinations by randomly selecting them within the range of parameter values.

10. The method for predicting tree fall specifications based on historical meteorological data and UAV post-disaster inspection and survey as described in claim 1, characterized in that: The specific process of step S6 is as follows: Step S61: Develop a web-based real-time monitoring platform to quickly update tree status data and make real-time predictions before a typhoon arrives, integrate prediction models, and build a real-time monitoring and early warning system. Step S62: Based on the forecast data, formulate a scientific and reasonable post-disaster relief plan, quickly locate and deal with severely affected areas, and improve recovery efficiency.

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