Trade-Off and Synergy Mechanism of Agricultural Water Resource Spatial Allocation in Monsoon Climate Areas Based on Machine Learning: A Case Study of Reservoir Layout Optimization in Shandong Province, China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Sources and Processing
2.2.1. Determination of Data Coordinate Points
2.2.2. Data and Processing of Factors Influencing Suitability Degree
2.2.3. Data and Processing of Influencing Factors of Demand Degree
- Source of data
Influencing Factors | Data Source |
---|---|
original regulating storage capacity | For the regulating storage capacity of the existing reservoirs adjacent to the coordinate points within the irrigation area, the geographical information data of the reservoirs comes from the team’s statistical collection, and the data on the regulating storage capacity comes from the introduction reports of the reservoirs. |
irrigation area | For the area of the irrigation district where the coordinate point is located, the data comes from the Shandong Province Irrigation District Directory. |
annual average precipitation at the coordinate point P | Data comes from the spatial distribution map of precipitation in Shandong Province. |
Potential evapotranspiration | Data sources refer to Zhao Shen’s study on the spatiotemporal variation of evapotranspiration and potential evapotranspiration in Shandong Province [16]. |
distribution coefficient α | Data comes from the proportion of agricultural water allocation to total water demand in various cities of Shandong Province. |
- 2.
- Data processing
2.2.4. Predictor Variables Data and Processing
- Data Sources
- 2.
- Data Processing
2.3. Research Methods
2.3.1. Research Process
2.3.2. GA_BP Neural Network Model
2.3.3. GA_Bi-Objective Optimization Model
2.3.4. Error Analysis
3. Results
3.1. GA_BP Analysis of Neural Network Model Results
3.1.1. Prediction Results for the Independent Variable to Be Selected
3.1.2. Correlation Analysis of Independent Variables to Be Selected
3.2. GA-Bi-Objective Optimization Model and Result Analysis
3.2.1. Construction of Bi-Objective Optimization Function
- Suitability Objective Function :
- 2.
- Demand Degree Objective Function :
3.2.2. Analysis of Bi-Objective Optimization Results
3.2.3. Testing the Effectiveness of the Dual-Objective Optimization Model
3.3. Optimization Analysis of Spatial Layout for Reservoir Site Selection
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
1 | water resources | Distance\River Level | Level 5 | Level 4 | Level 3 | Level 2 | Level 1 | ||
>2000 | 1 | 1 | 1 | 1 | 1 | ||||
1500~2000 | 1 | 2 | 3 | 4 | 5 | ||||
1000~1500 | 2 | 3 | 4 | 5 | 6 | ||||
500~1000 | 3 | 4 | 5 | 6 | 7 | ||||
0~500 | 5 | 6 | 7 | 8 | 9 | ||||
2 | Precipitation | (A1) | |||||||
(A2) | |||||||||
—Conversion fraction of precipitation; i = 1, 2, 3, …, 329 —Are the maximum and minimum values of the assignment interval for the conversion score, and the assignment interval is [1,9]; —Averages; this is the average precipitation in Shandong Province; —Standard deviation; this is the standard deviation of precipitation in Shandong Province; —The standard score of precipitation is the score obtained after data accuracy, is the maximum and minimum values of the standard score. Here are the maximum and minimum values of the standard score of precipitation in Shandong Province; —Precipitation at coordinate points. | |||||||||
3 | Temperature | The method of assigning values is the same as that for precipitation. | |||||||
4 | Evaporation | The method of assigning values is the same as that for precipitation. | |||||||
5 | Geological hazards | Seismic Intensity | Score | ||||||
Six-degree | 9 | ||||||||
Seven-degree | 7 | ||||||||
Eight-degree (0.2 g) | 5 | ||||||||
Eight-degree (0.3 g) | 3 | ||||||||
Nine-degree | 1 | ||||||||
6 | Stratigraphic lithology | Rock Type\Water-Richness | Extremely Low Water Richness | Very Low Water Richness | Moderate Water Richness | High Water Richness | Extremely High Water Richness | ||
unconsolidated porous rocks | 1.0~1.4 | 1.4~1.8 | 1.8~2.2 | 2.2~2.6 | 2.6~3.0 | ||||
Fractured and porous fine-grained rocks | 3.0~3.4 | 3.4~3.8 | 3.8~4.2 | 4.2~4.6 | 4.6~5.0 | ||||
Fractured and karstified carbonate rocks | 5.0~5.4 | 5.4~5.8 | 5.8~6.2 | 6.2~6.6 | 6.6~7.0 | ||||
Fracture-hosted igneous rocks | 7.0~7.4 | 7.4~7.8 | 7.8~8.2 | 8.2~8.6 | 8.6~9.0 | ||||
7 | Hydrogeology | Water quality\Abundance of underground water resources | V | VI | III | II | I | ||
Class I | 9 | 8 | 7 | 6 | 5 | ||||
Class II | 8 | 7 | 6 | 5 | 4 | ||||
Class III | 7 | 6 | 5 | 4 | 3 | ||||
Class IV | 6 | 5 | 4 | 3 | 2 | ||||
Class V | 5 | 4 | 3 | 2 | 1 | ||||
8 | Socio-economic | The method of assigning values is the same as that for precipitation. | |||||||
9 | Land terrain type | Land/Terrain | Mountainous | Hilly | Plain | ||||
Water areas | 9 | 7 | 5 | ||||||
Grassland and woodland | 7 | 5 | 3 | ||||||
Agricultural land | 5 | 3 | 1 | ||||||
Tideland, Gobi, etc. | 3 | 1 | 1 | ||||||
Urban land | 1 | 1 | 1 | ||||||
10 | Ecologically sensitive areas | Distance from ecologically sensitive areas | Score | ||||||
≥2000 | 9 | ||||||||
1501~2000 | 7 | ||||||||
1001~1500 | 5 | ||||||||
501~1000 | 3 | ||||||||
0~500 | 1 |
Appendix B
- (1)
- GA_BP algorithm main function: (xunhuan_GA_BP_other1)https://kdocs.cn/l/cftWYHbiB8Uw (accessed on 22 August 2024)
- (2)
- Fitness function: (fitness1)https://kdocs.cn/l/ceoeK7BVtS9V (accessed on 22 August 2024)
- (3)
- The select function (select)https://kdocs.cn/l/chEyjic8T8Sa (accessed on 22 August 2024)
- (4)
- The crossover function: (Cross)https://kdocs.cn/l/cbpOA5vZUPAO (accessed on 22 August 2024)
- (5)
- The variational function: (Mutation)https://kdocs.cn/l/cfBjNq2Q9jL1 (accessed on 22 August 2024)
Appendix C
- (1)
- GA_Bi-objective optimization function: (GA_Bi_objective)https://kdocs.cn/l/ccquscpr04GC (accessed on 22 August 2024)
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Influencing Factors | Data Source | Data Processing |
---|---|---|
water resource | Data comes from the vector map of the river basin water system data across China. | Water resources are an important factor influencing the site selection of reservoirs, and the factors of water sources include the basin area upstream of the dam site, the average flow over time, and the peak flow [33,34]. Research has found that the main factor affecting the storage capacity of large and medium-sized reservoirs is the flow of the river flowing into the reservoir. In addition, to facilitate the statistical management of rivers, China has established a set of river grading standards. The hydrological information such as the size of the river flow can be reflected through the river grade; the distance from the river can be used to judge the cost and necessity of building a reservoir. Therefore, this paper uses the river grade and the distance from the river to reflect the richness of the water source. |
precipitation | Data comes from the annual precipitation distribution map of Shandong Province. | The values are assigned based on the standardized and transformed rainfall amounts of the coordinate points obtained from the annual precipitation distribution map of Shandong Province, with the assignment scores ranging from 1 to 9. |
temperature | Data comes from the annual average temperature distribution map of Shandong Province. | Based on the annual average temperature distribution map of Shandong Province, the temperature values obtained for the coordinate points are standardized and transformed to obtain transformation scores, with the scoring range being [1,9]. The scoring method is the same as that for precipitation. |
evaporation | Data comes from the evapotranspiration distribution map of Shandong Province. | Based on the evapotranspiration distribution map of Shandong Province, the values of evaporation obtained for the coordinate points are standardized and converted to obtain transformation scores, with the scoring range being [1,9]. The scoring method is the same as that used for precipitation. |
geological hazards | Data comes from the seismic intensity distribution map of Shandong Province. | Based on the research in engineering hydrogeology by Wang Xue et al. [34,35,36,37,38], using the grading standards of seismic intensity [35,36], the intensity of geological disasters is analyzed to assess the impact of geological disasters on the suitability of reservoir construction. The coordinate points within Shandong Province are assigned values, where 0.2 g and 0.3 g represent the seismic acceleration, and g represents the acceleration due to gravity. |
stratigraphic lithology | Data comes from the stratigraphic lithology distribution map of Shandong Province. | Based on the types of rock layers and the intensity of water richness displayed in the spatial distribution map of water-rich rock types in Shandong Province, the valuation of rock lithology is evenly divided. |
hydrogeology | Data comes from the distribution map of groundwater resources in Shandong Province and the “Surface Water Quality Standards” [37]. | Based on the distribution map of groundwater resources in Shandong Province, the richness of groundwater sources is divided into five grades according to the classification standards in the map, with reference to the grading standards of groundwater water richness and the depth of groundwater burial for valuation. According to the degree of pollution of groundwater sources in Shandong Province, the water quality is divided into five categories following the water environmental quality grades defined in the “Surface Water Quality Standards” [37]. |
socio-economic | Data comes from the GDP data of various counties in Shandong Province that were publicly disclosed in the 2021 Shandong Provincial Government Report. | Based on the GDP data of various counties in Shandong Province from the Shandong Provincial Government Report, standardized valuation is conducted for 329 points, with a valuation range of [1,9]. The valuation method is the same as that for precipitation. |
Land terrain type | Data comes from the land use map and topographic map of Shandong Province. | To reduce the potential flood risks of reservoir construction on land use, relocation, farmland, and urban and rural settlements, minimize the destruction to the natural environment and ecology, ensure agricultural irrigation, and reduce the impact on economic development, this study assigns values to the types of land and terrain based on the land use map and topographic map of Shandong Province. |
ecologically sensitive area | Data comes from the “Shandong Province Ecological Protection Red Line Planning” [38]. | In accordance with the “Shandong Province Ecological Protection Red Line Planning” [38], the spatial distribution locations of ecological protection areas are determined, and the distance between the planned reservoir sites and ecologically sensitive areas is planned to reduce the impact of reservoir construction on ecologically sensitive areas. This study assigns values to the research coordinate points based on their distance from ecologically sensitive areas. |
Independent Variable to be Selected | Interpretation | Source of Data |
---|---|---|
Adjustment of storage capacity | Adjustment of storage capacity (also known as regulating storage capacity) is the volume of the reservoir between the normal storage level and the dead level. Used to regulate runoff and provide water supply to reservoirs. Natural runoff is redistributed for use by regulating the capacity of the regulating reservoir in accordance with water demand requirements. | Reservoir regulation capacity data for the study coordinate points were obtained from local reservoir reports. |
Total capacity | Total reservoir capacity is the volume of the reservoir below the calibrated flood level. It is the sum of dead storage capacity, regulating storage capacity and flood control storage capacity, and is called total storage capacity. It is the total size of the reservoir construction. | Total reservoir capacity data for the study coordinate points were obtained from local reservoir reports. |
Reservoir capacity factor (β) | Reservoir capacity factor (also known as regulating storage coefficient) is a measure of the ability of an artificially constructed water storage project (reservoir) to regulate the amount of water coming from the rain catchment area above the dam site, which is the ratio of the regulating capacity to the average amount of water coming from the reservoir over a number of years. | Reservoir capacity coefficient data for the study coordinate points were obtained from local reservoir reports. |
Parameter | Value |
---|---|
Number of input neurons | 10 |
Number of neurons in hidden layer | 8 |
Number of output neurons | 1 |
Total number of evolutionary iterations | 1000 |
Population size | 100 |
Crossover probability | 0.5 |
Variation probability | 0.1 |
Train set | 80% |
Test set | 20% |
Parameters | Value |
---|---|
Number of independent variables | 329 |
Constraints on independent variables | |
Population size | 100 |
Maximum number of iterations | 200 |
Pareto set proportion | 0.45 |
Crossover ratio | 0.75 |
Crossover function | crossoverheuristic |
Variation function | mutationadaptfeasible |
Category\Standard | RMSE | R |
---|---|---|
Adjustment of storage capacity | 0.10~0.14 | 0.65~0.94 |
Total capacity | 0.19~0.25 | 0.23~0.61 |
Reservoir capacity factor | 0.58~0.65 | 0.18~0.51 |
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Zou, M.; Xie, D.; Xu, L.; Dai, K.; Liang, S.; Guo, M.; Qin, X.; Zhao, W. Trade-Off and Synergy Mechanism of Agricultural Water Resource Spatial Allocation in Monsoon Climate Areas Based on Machine Learning: A Case Study of Reservoir Layout Optimization in Shandong Province, China. Agronomy 2024, 14, 1902. https://doi.org/10.3390/agronomy14091902
Zou M, Xie D, Xu L, Dai K, Liang S, Guo M, Qin X, Zhao W. Trade-Off and Synergy Mechanism of Agricultural Water Resource Spatial Allocation in Monsoon Climate Areas Based on Machine Learning: A Case Study of Reservoir Layout Optimization in Shandong Province, China. Agronomy. 2024; 14(9):1902. https://doi.org/10.3390/agronomy14091902
Chicago/Turabian StyleZou, Mingyu, Danfeng Xie, Liwen Xu, Ke Dai, Shuang Liang, Mengdie Guo, Xushuai Qin, and Weijun Zhao. 2024. "Trade-Off and Synergy Mechanism of Agricultural Water Resource Spatial Allocation in Monsoon Climate Areas Based on Machine Learning: A Case Study of Reservoir Layout Optimization in Shandong Province, China" Agronomy 14, no. 9: 1902. https://doi.org/10.3390/agronomy14091902