Efficient utilization and analysis method for sandy soil water resources in arid region based on DSS decision support system

Abstract

The invention provides an efficient utilization and analysis method for sandy soil water resources in an arid region based on a DSS decision support system. The efficient utilization and analysis method for the sandy soil water resources in the arid region based on the DSS decision support system comprises the following steps: a, collecting meteorological data, soil moisture data, vegetation coverage data and water source information of the arid region, and constructing a multi-source data fusion system; and b, combining with ground monitoring station data through a remote sensing technology. According to the efficient utilization and analysis method for the sandy soil water resources in the arid region based on the DSS decision support system, a remote sensing technology is combined with ground monitoring data, soil moisture and meteorological data are updated in real time, and the blank of data missing is filled. The data fusion method not only solves the difficulty of real-time data updating, but also accurately describes the spatial and temporal change of the soil moisture through the dynamic change model, and further improves the accuracy of decision making.

Description

Technical Field

[0001] The present invention relates to the technical field of water resource utilization in sandy soil in arid areas, and specifically to an analysis method for efficient utilization of water resources in sandy soil in arid areas based on a DSS decision support system. Background Technique

[0002] The analysis method for efficient utilization of water resources in sandy soil in arid areas combines the decision support system (DSS) with water resource management in arid areas, aiming to improve water resource utilization efficiency. The system mainly consists of four modules: a data collection module, a data processing and analysis module, a decision support module, and a visualization display module. The data collection module is responsible for collecting basic information such as soil, climate, vegetation, and water sources; the data processing and analysis module processes these data and evaluates characteristics such as water retention capacity and permeability; the decision support module generates management suggestions through optimization algorithms to help improve water resource usage efficiency; the visualization display module presents the analysis results in a graphical form for easy understanding and application by decision-makers. The principle of this system is to simulate the dynamic changes of soil moisture in arid areas through the combination of data-driven and mathematical models, and then propose reasonable water resource allocation and utilization strategies.

[0003] Although this decision support system can provide effective water resource management solutions, there are still some defects and limitations in its actual application. Data quality and timeliness are major problems for the system. Especially in arid areas, it is difficult to obtain long-term data on climate change and soil moisture, and the lack of real-time updated data may affect the accuracy of decision-making. Secondly, the models in the system are usually optimized for the conditions of specific regions, lacking broad adaptability and having weak generalization ability in different arid regions, resulting in less than ideal actual effects in some areas. Summary of the Invention

[0004] Aiming at the deficiencies of the existing technology, the present invention provides an analysis method for efficient utilization of water resources in sandy soil in arid areas based on a DSS decision support system, which solves the problems that in arid areas, it is difficult to obtain long-term data on climate change and soil moisture, and the lack of real-time updated data may affect the accuracy of decision-making; the models are usually optimized for the conditions of specific regions, lacking broad adaptability and having weak generalization ability in different arid regions.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: An analysis method for efficient utilization of water resources in sandy soil in arid areas based on a DSS decision support system, including:

[0006] a. Collect meteorological data, soil moisture data, vegetation cover data, and water source information in arid areas to construct a multi-source data fusion system;

[0007] b. By combining remote sensing technology with data from ground monitoring stations, the soil moisture and meteorological data are updated in real time. Remote sensing technology is used to monitor the spatial distribution of soil moisture, and combined with ground station data for time series updates to supplement missing values of soil moisture, and a dynamic change model of soil moisture is established. The dynamic change model of soil moisture is expressed as:

[0008] S m (t + 1) = S m (t) + ΔS m (t)

[0009] Where S m (t) is the soil moisture content at time t, and ΔS m (t) is the amount of water change in this time period;

[0010] c. Preprocess the collected multi-source data;

[0011] d. Based on machine learning algorithms, establish a water resource utilization model with strong adaptability, and adaptively optimize the model in combination with the climate and soil characteristics of different regions;

[0012] e. Use the multiple regression analysis method to predict the regional water resource demand;

[0013] f. Combine historical water resource utilization data with real-time data through data fusion and time series analysis;

[0014] g. According to the prediction results, use the optimization algorithm in the decision support system to formulate the best water resource allocation and utilization strategy;

[0015] h. Adopt visualization technology based on geographic information system to spatially display the water resource utilization plan for intuitive understanding by decision-makers;

[0016] i. According to the predicted climate change situation, dynamically adjust the water resource management strategy to ensure that the decision support system has continuous feedback and adjustment capabilities, and adjust the decision-making strategy in combination with climate prediction data to ensure that water resource management can be effectively adjusted under different climate conditions;

[0017] j. Through real-time monitoring and feedback mechanism, regularly correct the model parameters to ensure that the model adapts to different seasons and climate changes;

[0018] k. According to the characteristics and actual needs of different arid regions, automatically adjust the parameters and algorithms of the system model to enhance the regional adaptability of the system. For the soil types and hydrological conditions of different arid regions, automatically adjust the model parameters to enhance the application effect of the system in different regions;

[0019] I. Generate suggestions for efficient water resource utilization based on DSS decision support, and conduct decision-making information transmission and management through mobile devices or PCs. The system will display the final water resource allocation plan to decision-makers through mobile devices or PCs, supporting real-time update and adjustment.

[0020] Preferably, the meteorological data includes precipitation, temperature, evaporation, the soil moisture data is the soil moisture content measured by sensors, and the vegetation cover data is used to evaluate water transpiration and water utilization efficiency.

[0021] Preferably, in step c, the preprocessing uses data cleaning and interpolation algorithms to fill in missing values, ensuring the timeliness and accuracy of the data, and using interpolation methods to fill in missing data to ensure the continuity and spatial consistency of the data.

[0022] Preferably, the water resource utilization model is:

[0023] W r =f(T,P,S m ,E,ET)

[0024] Among them, W r represents water resource demand, T is temperature, P is precipitation, S m is soil moisture, E is evaporation, and ET is transpiration. The relationships between the parameters in the water resource utilization model are optimized through training data to achieve adaptive adjustment.

[0025] Preferably, the multiple regression analysis method is used to predict the water resource demand in arid areas, and a spatio-temporal model of regional water resource utilization is established. The formula of the regional water resource utilization spatio-temporal model is:

[0026]

[0027] Among them, is the predicted water resource demand, and α1, α2, α3, α4 are model parameters.

[0028] Preferably, the data fusion and time series analysis predict the future changes in water resource supply and demand through a time series model and correct them through data fusion technology. The time series model is the ARIMA model.

[0029] Preferably, the optimization algorithm optimizes the water resource allocation plan to maximize the water resource utilization efficiency. The optimization objective is to minimize water resource waste and maximize utilization rate. The model formula for minimizing water resource waste and maximizing utilization rate is:

[0030]

[0031] Among them, W r(i) is the water resource demand for the i-th region, W available is the total amount of available water resources.

[0032] Preferably, the geographic information system technology displays the spatio-temporal distribution of water resources through maps, allowing decision-makers to view the water resource usage in each region in real time and make adjustments based on the visualization results. The real-time monitoring and feedback mechanism includes real-time monitoring of soil moisture, temperature, and precipitation data, and dynamically adjusting model parameters to adapt to seasonal and climate changes.

[0033] The present invention provides an analysis method for efficient utilization of water resources in sandy soil in arid areas based on a DSS decision support system. It has the following beneficial effects:

[0034] This analysis method for efficient utilization of water resources in sandy soil in arid areas based on a DSS decision support system combines remote sensing technology with ground monitoring data to update soil moisture and meteorological data in real time, filling the gap of missing data. This data fusion method not only solves the difficulty of real-time data update, but also accurately describes the spatio-temporal changes of soil moisture through a dynamic change model, further improving the accuracy of decision-making.

[0035] The present invention combines machine learning algorithms with multiple regression analysis to establish a water resource utilization model with strong adaptability, which can be adaptively optimized according to the climate and soil characteristics of different regions, thereby enhancing the regional adaptability of the system. This adaptive adjustment mechanism greatly improves the generalization ability of the model in different arid regions, is applicable to different soil types and climate conditions, and avoids the limitations of existing methods that rely too much on specific regions. In addition, the optimization algorithm ensures the efficient management of resources by minimizing waste and maximizing the water resource utilization efficiency during the water resource allocation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 is a schematic flow diagram of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[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. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0038] As Figure 1As shown in the figure, the embodiment of the present invention provides an analysis method for efficient utilization of water resources in sandy soil in arid areas based on a DSS decision support system, including: a. Collect meteorological data, soil moisture data, vegetation cover data, and water source information in arid areas to construct a multi-source data fusion system. The meteorological data includes precipitation, temperature, evaporation, the soil moisture data is the soil moisture content measured by sensors, and the vegetation cover data is used to evaluate water transpiration and water use efficiency;

[0039] b. Combine remote sensing technology with data from ground monitoring stations to update soil moisture and meteorological data in real time. Use remote sensing technology to monitor the spatial distribution of soil moisture, and combine ground station data for time series update to supplement missing values of soil moisture, and establish a dynamic change model of soil moisture. The dynamic change model of soil moisture is expressed as:

[0040] S m (t + 1) = S m (t) + ΔS m (t)

[0041] where S m (t) is the soil moisture content at time t, and ΔS m (t) is the water change amount in this time period;

[0042] c. Preprocess the collected multi-source data. The preprocessing uses data cleaning and interpolation algorithms to fill in missing values to ensure the timeliness and accuracy of the data, and uses interpolation methods to fill in missing data to ensure the continuity and spatial consistency of the data;

[0043] d. Establish a water resource utilization model with strong adaptability based on machine learning algorithms, and adaptively optimize the model in combination with the climate and soil characteristics of different regions. The water resource utilization model is:

[0044] W r = f(T, P, S m , E, ET)

[0045] where W r represents water resource demand, T is temperature, P is precipitation, S m is soil moisture, E is evaporation, and ET is transpiration. The relationship between the parameters in the water resource utilization model is optimized through training data to achieve adaptive adjustment;

[0046] e. Use the multiple regression analysis method to predict the regional water resource demand. The multiple regression analysis method predicts the water resource demand in arid areas and establishes a spatio-temporal model of regional water resource utilization. The formula of the spatio-temporal model of regional water resource utilization is:

[0047]

[0048] Among them, is the predicted water resource demand, and α1, α2, α3, α4 are model parameters;

[0049] f. Combine historical water resource utilization data with real-time data. Through data fusion and time series analysis, the data fusion and time series analysis predict the future changes in water resource supply and demand through a time series model and correct them through data fusion technology. The time series model is the ARIMA model;

[0050] g. According to the prediction results, use the optimization algorithm in the decision support system to formulate the best water resource allocation and use strategy. The optimization algorithm optimizes the water resource allocation plan to achieve the maximum water resource use efficiency. The optimization objectives are to minimize water resource waste and maximize utilization rate. The model formula for minimizing water resource waste and maximizing utilization rate is:

[0051]

[0052] Among them, W r (i) is the water resource demand of the i-th region, and W available is the total available water resource;

[0053] h. Adopt visualization technology based on geographic information system to spatially display the water resource utilization plan for intuitive understanding by decision-makers. The geographic information system technology displays the spatio-temporal distribution of water resources through maps. Decision-makers can view the water resource use situation in each region in real time and make adjustments based on the visualization results. The real-time monitoring and feedback mechanism includes real-time monitoring of soil humidity, temperature, and precipitation data, dynamically adjusting model parameters, and achieving adaptation to seasonal and climate changes;

[0054] i. Dynamically adjust the water resource management strategy according to the predicted climate change situation to ensure that the decision support system has continuous response and adjustment capabilities. Combine climate prediction data to adjust decision-making strategies to ensure that water resource management can be effectively adjusted under different climate conditions;

[0055] j. Regularly correct model parameters through the real-time monitoring and feedback mechanism to ensure that the model adapts to different seasons and climate changes;

[0056] k. Automatically adjust the parameters and algorithms of the system model according to the characteristics and actual needs of different arid regions to enhance the regional adaptability of the system. Automatically adjust model parameters according to the soil types and hydrological conditions of different arid regions to enhance the application effect of the system in different regions;

[0057] I. Generate suggestions for efficient utilization of water resources based on DSS decision support, and conduct decision-making information transmission and management through mobile devices or PC terminals. The system will display the final water resource allocation plan to decision-makers through mobile terminals or PC terminals, supporting real-time update and adjustment.

[0058] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand 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. An analysis method for efficient utilization of water resources in sandy soil in arid areas based on a DSS decision support system, characterized in that, Including: a. Collect meteorological data, soil moisture data, vegetation cover data and water source information in arid areas to construct a multi-source data fusion system; b. Combine remote sensing technology with data from ground monitoring stations to update soil moisture and meteorological data in real time. Use remote sensing technology to monitor the spatial distribution of soil moisture, and combine ground station data for time series update to supplement missing values of soil moisture, and establish a dynamic change model of soil moisture, which is expressed as: S m S(t + 1) = m S(t)+ΔS m S(t) Among them, S m (t) is the soil moisture content at time t, and ΔS m (t) is the moisture change amount during this time period; c. Preprocess the collected multi-source data; d. Establish a water resource utilization model with strong adaptability based on machine learning algorithms, and perform adaptive optimization of the model in combination with the climate and soil characteristics of different regions; e. Use the multiple regression analysis method to predict the regional water resource demand; f. Combine historical water resource utilization data with real-time data through data fusion and time series analysis; g. According to the prediction results, use the optimization algorithm in the decision support system to formulate the best water resource allocation and utilization strategy; h. Adopt visualization technology based on geographic information system to spatially display the water resource utilization plan for easy intuitive understanding by decision-makers; i. Dynamically adjust the water resource management strategy according to the predicted climate change; j. Regularly correct the model parameters through real-time monitoring and feedback mechanism; k. Automatically adjust the parameters and algorithms of the system model according to the characteristics and actual needs of different arid areas; I. Generate suggestions for efficient water resource utilization based on DSS decision support, and conduct decision-making information transmission and management through mobile devices or PC terminals.

2. The method for analyzing the efficient utilization of water resources in sandy soil in arid areas based on the DSS decision support system according to claim 1, wherein: The meteorological data includes precipitation, temperature, evaporation, the soil moisture data is the soil moisture content measured by sensors, and the vegetation cover data is used to evaluate water transpiration and water use efficiency.

3. The method for analyzing the efficient utilization of water resources in sandy soil in arid areas based on the DSS decision support system according to claim 1, wherein: The preprocessing in step c uses data cleaning and interpolation algorithms, and the interpolation method is used to fill in the missing data.

4. The method for analyzing the efficient utilization of water resources in sandy soil in arid areas based on the DSS decision support system according to claim 1, characterized in that: The water resource utilization model is: W r = f(T, P, S m , E, ET) Among them, W r represents water resource demand, T is air temperature, P is precipitation, S m is soil moisture, E is evaporation, ET is transpiration, and the relationships between the parameters in the water resource utilization model are optimized through training data to achieve adaptive adjustment.

5. The method for analyzing the efficient utilization of water resources in sandy soil in arid areas based on the DSS decision support system according to claim 1, wherein: The multiple regression analysis method predicts the water resource demand in arid areas and establishes a spatio-temporal model of regional water resource utilization. The formula of the spatio-temporal model of regional water resource utilization is: wherein, is the predicted water resource demand, and α1, α2, α3, and α4 are model parameters.

6. The method for analyzing the efficient utilization of water resources in sandy soil in arid areas based on the DSS decision support system according to claim 1, wherein: The data fusion and time series analysis predicts the future changes in water resource supply and demand through a time series model and corrects it through data fusion technology. The time series model is the ARIMA model.

7. The method for analyzing the efficient utilization of water resources in sandy soil in arid areas based on the DSS decision support system according to claim 1, characterized in that: The optimization algorithm optimizes the water resource allocation plan to maximize the water resource use efficiency. The optimization goal is to minimize water resource waste and maximize utilization rate. The model formula for minimizing water resource waste and maximizing utilization rate is: Among them, W r (i) is the water resource demand of the i-th region, and W available is the total available water resources.

8. The method for analyzing the efficient utilization of water resources in sandy soil in arid areas based on the DSS decision support system according to claim 1, characterized in that: The geographic information system technology displays the spatio-temporal distribution of water resources through maps, and decision-makers can view the water resource use situation in each region in real time and make adjustments based on the visualization results. The real-time monitoring and feedback mechanism includes real-time monitoring of soil humidity, temperature, and precipitation data.

Images