Multi-source data fusion flood risk assessment early warning method

Abstract

The invention provides a multi-source data fusion flood risk assessment early warning method, and relates to the technical field of natural disaster early warning. The multi-source data fusion flood risk assessment early warning method comprises the following steps: data acquisition: acquiring meteorological data including but not limited to rainfall, air temperature, air pressure and wind speed; hydrological data including river flow, water level and soil humidity are collected; collecting geographic data including terrain elevation, land utilization type and vegetation coverage; social data including population density, building distribution and infrastructure conditions are collected; and data preprocessing: cleaning the collected data, removing noise and abnormal values, and carrying out standardization processing on the data to ensure that the data of different data sources has comparability. According to the flood risk assessment method, multi-dimensional features of flood risks can be comprehensively reflected by integrating multi-source data of meteorology, hydrology, geography, society and the like, and richer information support is provided by integrating different types of data, so that risk assessment is more comprehensive and accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of natural disaster early warning, and specifically to a multi-source data fusion flood risk assessment and early warning method. Background Technique

[0002] Flood is one of the most destructive natural disasters globally, having a profound impact on human society, economy, and environment. According to data from the United Nations Office for Disaster Risk Reduction, the economic losses caused by flood disasters worldwide reach up to tens of billions of US dollars annually, and the number of affected people is in the millions. With the acceleration of climate change and urbanization, the frequency and intensity of floods are on the rise, further exacerbating the threat to human life and property.

[0003] Traditional flood early warning systems mainly rely on a single data source, such as meteorological data (e.g., rainfall, temperature, and air pressure) or hydrological data (e.g., river flow and water level). However, a single data source has obvious limitations and is difficult to comprehensively reflect the multi-dimensional characteristics of flood risk. For example, although meteorological data can provide information on rainfall trends, it cannot accurately reflect the impact of factors such as terrain, land use, and population distribution on flood risk. Similarly, although hydrological data can reflect the hydrological conditions of rivers and lakes, it lacks consideration of meteorological conditions and geographical features, resulting in insufficient accuracy and timeliness of early warnings. Therefore, technicians in this field have provided a multi-source data fusion flood risk assessment and early warning method to solve the problems raised in the above background technique. Summary of the Invention

[0004] (I) Technical Problems to be Solved

[0005] In view of the deficiencies of the prior art, the present invention provides a multi-source data fusion flood risk assessment and early warning method, which solves the problems of single data source and insufficient accuracy and timeliness of early warnings in traditional early warning systems.

[0006] (II) Technical Solutions

[0007] To achieve the above objectives, the present invention is realized through the following technical solutions: A multi-source data fusion flood risk assessment and early warning method, including:

[0008] Data collection, collecting meteorological data, including but not limited to rainfall, temperature, air pressure, and wind speed; collecting hydrological data, including river flow, water level, and soil moisture; collecting geographical data, including terrain elevation, land use type, and vegetation cover; collecting social data, including population density, building distribution, and infrastructure conditions;

[0009] Data preprocessing: Clean the collected data to remove noise and outliers, standardize the data to ensure comparability of data from different data sources, and perform spatio-temporal registration on the data to ensure consistency in time and space;

[0010] Data fusion: Adopt a multi-source data fusion algorithm to fuse data from different data sources, build a multi-source data fusion model, and comprehensively consider the impact of each data source on flood risk;

[0011] Risk assessment: Based on the fused data, calculate the flood risk index, use machine learning algorithms to establish a flood risk assessment model, and train and validate the model to improve the accuracy of the assessment;

[0012] Warning release: According to the risk assessment results, determine the warning level, release warning information through multiple channels, monitor data changes in real time, and update warning information in a timely manner.

[0013] Preferably, the data collection includes the following steps:

[0014] S1. Meteorological data collection: Meteorological data mainly comes from the national meteorological bureau, local meteorological stations, and satellite remote sensing data. The data types include rainfall, temperature, air pressure, wind speed, and wind direction. Among them, rainfall includes real-time rainfall and historical rainfall data, and the collection frequency is once per hour or once per minute; Temperature affects evaporation and soil moisture, and is usually recorded as daily average temperature or hourly temperature; Air pressure affects the change of weather systems, and the collection frequency is once per hour; Wind speed and wind direction affect the distribution of rainfall and the flood propagation speed, and the collection frequency is once per hour; Obtain real-time and historical meteorological data through API interfaces or data download services;

[0015] S2. Hydrological data collection: Hydrological data comes from hydrological stations, river monitoring points, and groundwater monitoring networks. The data types include river flow, water level, and soil moisture. Among them, river flow monitors the flow change of the river in real time, and the collection frequency is once per hour or once every 15 minutes; Monitor the water levels of rivers, lakes, and reservoirs, and the collection frequency is once per hour; Soil moisture is obtained through soil moisture sensors, and the collection frequency is once per day; Transmit data in real time through Internet of Things devices and hydrological monitoring networks;

[0016] S3. Geographic data collection: Geographic data comes from geographic information systems, remote sensing satellites, and digital elevation models. The data types include terrain elevation, land use type, and vegetation cover. Among them, terrain elevation is used to analyze the flood inundation range, and the collection frequency is updated once a year; Land use types include cities, farmlands, and forests, which affect the flood impact area, and the collection frequency is updated once a year; Vegetation cover affects soil permeability and water flow speed, and the collection frequency is updated once a year; The collection method is obtained through satellite remote sensing images and GIS databases;

[0017] S4. Social data collection. Social data comes from the National Bureau of Statistics, local civil affairs departments, and census data. The data types include population density, building distribution, and infrastructure conditions. Among them, population density affects the number of people affected by flood disasters, and the collection frequency is updated annually; building distribution includes residential, commercial, and industrial buildings, and the collection frequency is updated annually; infrastructure conditions include roads, bridges, and dams, which affect flood evacuation and rescue, and the collection frequency is updated annually.

[0018] Preferably, the data preprocessing includes the following steps:

[0019] S1. Data cleaning. Remove noise in sensor data by using a filtering algorithm, and detect and process outliers using statistical methods or machine learning algorithms;

[0020] S2. Data standardization. Convert data with different dimensions into a unified scale to ensure the comparability of data from different data sources, which is convenient for subsequent data fusion and risk assessment;

[0021] S3. Spatiotemporal registration. Unify the data collected at different times to a unified time reference, and unify the data with different spatial resolutions to the same spatial reference.

[0022] Preferably, the data fusion uses the weighted average method to perform weighted averaging on the data from different data sources. The weights are determined according to the importance of each data source, and a multi-source data fusion model is constructed. Considering the impact of each data source on flood risk, historical data is used for training, and the model parameters are adjusted to improve the fusion accuracy.

[0023] Preferably, the risk assessment includes the following steps:

[0024] S1 Flood risk index calculation. Comprehensively consider relevant factors such as rainfall, river flow, water level, soil moisture, terrain elevation, and population density, and calculate the flood risk index using the weighted comprehensive evaluation method;

[0025] S2. Application of machine learning algorithms. Identify the flood risk level by using support vector machines for detection, classification, and regression analysis;

[0026] S3. Model training and validation. Use historical flood event data as the training set, adopt the cross-validation method to evaluate the model performance, and adjust the model parameters and optimize the model structure according to the validation results to improve the evaluation accuracy.

[0027] Preferably, the warning release includes the following steps:

[0028] S1. Early warning level determination: According to the flood risk index, the early warning levels are divided into low risk, medium risk, and high risk. Combining historical data and expert experience, the criteria for early warning levels are formulated.

[0029] S2. Early warning information release: Through text messages, emails, social media, and broadcasts, early warning information is released through multiple channels. According to the early warning level and risk changes, the release frequency is determined.

[0030] S3. Real-time monitoring and update: A real-time monitoring system is established to obtain real-time data changes. According to the monitoring results, the early warning information is updated in a timely manner to ensure the timeliness and accuracy of the early warning.

[0031] (III) Beneficial effects

[0032] The present invention provides a multi-source data fusion flood risk assessment and early warning method, which has the following beneficial effects:

[0033] 1. In the present invention, by integrating multi-source data such as meteorology, hydrology, geography, and society, the multi-dimensional characteristics of flood risk can be comprehensively reflected. By integrating different types of data, richer information support is provided, making the risk assessment more comprehensive and accurate.

[0034] 2. In the present invention, through Internet of Things devices and real-time data interfaces, meteorological, hydrological, and other data can be obtained in real time. It has the real-time nature of high-concurrency processing capabilities, can quickly process and analyze data, and update early warning information in real time.

[0035] 3. In the present invention, early warning information is released through multiple channels such as text messages, emails, social media, and broadcasts to ensure that the information can be quickly transmitted to relevant personnel and the public. According to the risk assessment results, different early warning levels are divided, and corresponding early warning criteria are formulated to ensure the accuracy and effectiveness of the early warning information. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a schematic structural diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0037] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0038] Embodiment 1:

[0039] As Figure 1 shown, the embodiment of the present invention provides a multi-source data fusion flood risk assessment and early warning method, including:

[0040] Data collection, collecting meteorological data, including but not limited to rainfall, temperature, air pressure, and wind speed; collecting hydrological data, including river flow, water level, and soil moisture; collecting geographical data, including terrain elevation, land use type, and vegetation cover; collecting social data, including population density, building distribution, and infrastructure status;

[0041] Data preprocessing, cleaning the collected data to remove noise and outliers, standardizing the data to ensure comparability of data from different data sources, and performing spatio-temporal registration on the data to ensure consistency in time and space;

[0042] Data fusion, using multi-source data fusion algorithms to fuse data from different data sources, constructing a multi-source data fusion model, and comprehensively considering the impact of each data source on flood risk;

[0043] Risk assessment, calculating the flood risk index based on the fused data, using machine learning algorithms to establish a flood risk assessment model, and training and validating the model to improve the accuracy of the assessment;

[0044] Early warning issuance, determining the warning level according to the risk assessment results, issuing warning information through multiple channels, and real-time monitoring of data changes to update the warning information in a timely manner.

[0045] Data collection includes the following steps:

[0046] S1. Meteorological data collection, meteorological data mainly comes from the national meteorological bureau, local weather stations, and satellite remote sensing data. The data types include rainfall, temperature, air pressure, wind speed, and wind direction. Among them, rainfall includes real-time rainfall and historical rainfall data, and the collection frequency is once per hour or per minute; temperature affects evaporation and soil moisture, and is usually recorded as the daily average temperature or hourly temperature; air pressure affects the change of weather systems, and the collection frequency is once per hour; wind speed and wind direction affect the distribution of rainfall and the flood propagation speed, and the collection frequency is once per hour; real-time and historical meteorological data are obtained through API interfaces or data download services;

[0047] S2. Hydrological data collection, hydrological data comes from hydrological stations, river monitoring points, and groundwater monitoring networks. The data types include river flow, water level, and soil moisture. Among them, river flow monitors the flow changes of rivers in real-time, and the collection frequency is once per hour or every 15 minutes; the water levels of rivers, lakes, and reservoirs are monitored, and the collection frequency is once per hour; soil moisture is obtained through soil moisture sensors, and the collection frequency is once per day; data is transmitted in real-time through Internet of Things devices and hydrological monitoring networks;

[0048] S3. Geographic data collection. The geographic data comes from geographic information systems, remote sensing satellites, and digital elevation models. The data types include terrain elevation, land use type, and vegetation cover. Among them, terrain elevation is used to analyze the flood inundation area, and the collection frequency is updated annually; the land use type includes cities, farmlands, and forests, which affect the flood-affected area, and the collection frequency is updated annually; vegetation cover affects the soil permeability and water flow velocity, and the collection frequency is updated annually; the collection method is obtained through satellite remote sensing images and GIS databases;

[0049] S4. Social data collection. The social data comes from the national bureau of statistics, local civil affairs departments, and census data. The data types include population density, building distribution, and infrastructure conditions. Among them, population density affects the number of people affected by flood disasters, and the collection frequency is updated annually; the building distribution includes residential, commercial, and industrial buildings, and the collection frequency is updated annually; the infrastructure conditions include roads, bridges, and dams, which affect flood evacuation and rescue, and the collection frequency is updated annually.

[0050] Data preprocessing includes the following steps:

[0051] S1. Data cleaning. Remove the noise in the sensor data by using filtering algorithms, and detect and process outliers by using statistical methods or machine learning algorithms;

[0052] S2. Data standardization. Convert data with different dimensions into a unified scale to ensure the comparability of data from different data sources, which is convenient for subsequent data fusion and risk assessment;

[0053] S3. Temporal and spatial registration. Unify the data collected at different times to a unified time reference, and unify the data with different spatial resolutions to the same spatial reference.

[0054] Data fusion uses the weighted average method to perform weighted averaging on the data from different data sources. The weights are determined according to the importance of each data source. Build a multi-source data fusion model, consider the impact of each data source on flood risk, use historical data for training, and adjust the model parameters to improve the fusion accuracy.

[0055] Risk assessment includes the following steps:

[0056] S1 Flood risk index calculation. Comprehensively consider relevant factors such as rainfall, river flow, water level, soil moisture, terrain elevation, and population density, and calculate the flood risk index by using the weighted comprehensive evaluation method;

[0057] S2. Application of machine learning algorithms. Identify the flood risk level by using support vector machines for detection, classification, and regression analysis;

[0058] S3. Model training and validation: Use historical flood event data as the training set, adopt the cross-validation method to evaluate the model performance. According to the validation results, adjust the model parameters, optimize the model structure, and improve the evaluation accuracy.

[0059] Early warning release includes the following steps:

[0060] S1. Determine the early warning level: According to the flood risk index, divide the early warning level into low risk, medium risk, and high risk. Combine historical data and expert experience to formulate the criteria for the early warning level.

[0061] S2. Release early warning information: Through text messages, emails, social media, and broadcasts, release early warning information through multiple channels. Determine the release frequency according to the early warning level and risk changes.

[0062] S3. Real-time monitoring and update: Establish a real-time monitoring system to obtain the data change situation in real time. According to the monitoring results, update the early warning information in a timely manner to ensure the timeliness and accuracy of the early warning.

[0063] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A multi-source data fusion flood risk assessment and early warning method, characterized in that: Including: Data collection, collecting meteorological data, including but not limited to rainfall, temperature, air pressure and wind speed; Collecting hydrological data, including river flow, water level and soil moisture; Collecting geographical data, including terrain elevation, land use type and vegetation cover; collecting social data, including population density, building distribution and infrastructure status; Data preprocessing, cleaning the collected data to remove noise and outliers, standardizing the data to ensure the comparability of data from different data sources, and performing spatio-temporal registration on the data to ensure the consistency of the data in time and space; Data fusion, using multi-source data fusion algorithms to fuse data from different data sources, constructing a multi-source data fusion model, and comprehensively considering the impact of each data source on flood risk; Risk assessment, calculating the flood risk index based on the fused data, using machine learning algorithms to establish a flood risk assessment model, training and validating the model to improve the accuracy of the assessment; Early warning release, determining the early warning level according to the risk assessment results, releasing early warning information through various channels, and monitoring data changes in real time to update the early warning information in a timely manner.

2. The multi-source data fusion flood risk assessment and early warning method according to claim 1, wherein: The data collection includes the following steps: S1. Meteorological data collection, meteorological data mainly comes from the national meteorological bureau, local meteorological stations and satellite remote sensing data. The data types include rainfall, temperature, air pressure, wind speed and wind direction. Among them, rainfall includes real-time rainfall and historical rainfall data, and the collection frequency is once per hour or once per minute; temperature affects evaporation and soil moisture, and is usually recorded as the daily average temperature or hourly temperature; air pressure affects the change of weather systems, and the collection frequency is once per hour; wind speed and wind direction affect the distribution of rainfall and the flood propagation speed, and the collection frequency is once per hour; real-time and historical meteorological data are obtained through API interfaces or data download services; S2. Hydrological data collection, hydrological data comes from hydrological stations, river monitoring points and groundwater monitoring networks. The data types include river flow, water level and soil moisture. Among them, river flow monitors the flow change of the river in real time, and the collection frequency is once per hour or once every 15 minutes; monitors the water levels of rivers, lakes and reservoirs, and the collection frequency is once per hour; soil moisture is obtained through soil moisture sensors, and the collection frequency is once per day; data is transmitted in real time through Internet of Things devices and hydrological monitoring networks; S3. Geographical data collection, geographical data comes from geographical information systems, remote sensing satellites and digital elevation models. The data types include terrain elevation, land use type and vegetation cover. Among them, terrain elevation is used to analyze the flood inundation range, and the collection frequency is updated once a year; land use types include cities, farmlands and forests, which affect the flood impact area, and the collection frequency is updated once a year; vegetation cover affects soil permeability and water flow velocity, and the collection frequency is updated once a year; the collection method is obtained through satellite remote sensing images and GIS databases; S4. Social data collection. The social data comes from the National Bureau of Statistics, local civil affairs departments, and census data. The data types include population density, building distribution, and infrastructure conditions. Among them, the population density affects the number of people affected by flood disasters, and the collection frequency is updated annually; the building distribution includes residential, commercial, and industrial buildings, and the collection frequency is updated annually; the infrastructure conditions include roads, bridges, and dams, which affect flood evacuation and rescue, and the collection frequency is updated annually.

3. The multi-source data fusion flood risk assessment and early warning method according to claim 1, characterized in that: The data preprocessing includes the following steps: S1. Data cleaning. Remove the noise in the sensor data by using a filtering algorithm, and detect and process outliers by using statistical methods or machine learning algorithms. S2. Data standardization. Convert data with different dimensions into a unified scale to ensure the comparability of data from different data sources, which is convenient for subsequent data fusion and risk assessment. S3. Spatiotemporal registration. Unify the data collected at different times to a unified time reference, and unify the data with different spatial resolutions to the same spatial reference.

4. The multi-source data fusion flood risk assessment and early warning method according to claim 1, wherein: The data fusion uses the weighted average method to perform weighted averaging on the data from different data sources. The weights are determined according to the importance of each data source. A multi-source data fusion model is constructed, considering the impact of each data source on the flood risk. Use historical data for training, adjust the model parameters, and improve the fusion accuracy.

5. A multi-source data fusion flood risk assessment and early warning method according to claim 1, characterized in that: The risk assessment includes the following steps: S1. Flood risk index calculation. Comprehensively consider relevant factors such as rainfall, river flow, water level, soil moisture, terrain elevation, and population density, and calculate the flood risk index by using the weighted comprehensive evaluation method. S2. Application of machine learning algorithms. Identify the flood risk level by using support vector machines for detection, classification, and regression analysis. S3. Model training and validation. Use historical flood event data as the training set, and adopt the cross-validation method to evaluate the model performance. According to the validation results, adjust the model parameters, optimize the model structure, and improve the evaluation accuracy.

6. The multi-source data fusion flood risk assessment and early warning method according to claim 1, characterized in that: The warning release includes the following steps: S1. Warning level determination. According to the flood risk index, divide the warning level into low risk, medium risk, and high risk, and formulate the standards for the warning level in combination with historical data and expert experience. S2. Warning information release. Release warning information through multiple channels such as text messages, emails, social media, and radio, and determine the release frequency according to the warning level and risk changes. S3. Real-time monitoring and update. Establish a real-time monitoring system to obtain the data change situation in real time. According to the monitoring results, update the warning information in a timely manner to ensure the timeliness and accuracy of the warning.

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