| Cite this article: | 苏楠,章少辉,白美健,等.融合随机森林和SHAP方法的灌区用水调度经验分析[J].灌溉排水学报,0,():-. |
| sunan,zhangshaohui,baimeijian,et al.融合随机森林和SHAP方法的灌区用水调度经验分析[J].灌溉排水学报,0,():-. |
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| Analysis of water regulation experience by combining random forest and SHAP method in irrigation district |
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sunan, zhangshaohui, baimeijian, zhangbaozhong
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China Institute of Water Resources and Hydropower Research
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| Abstract: |
| 【Objective】The rich experience of water scheduling accumulated in irrigated areas is extremely important. Whereas, the experience cannot be quantitatively characterized so far, making it difficult to be replicated and applied by other managers. The difficulty of solving this problem is that the characteristic variables which affect the target flow rate of water dispatching constitute a complex nonlinear decision network, and simultaneously there is redundant correlation among the characteristic variables. In order to quantitatively characterize the irrigation area to accumulate rich experience in water management, so that it can be copied and applied by other managers.【Method】To solve this problem, based on the measured data samples of three typical years in the irrigation area of Waxi trunk Canal in Pishihang Irrigation Area, this paper fully considered the spatial variation of characteristic variables such as temperature, rainfall and soil moisture, then deeply integrated the interpretation method of random forest model and SHAP model. Furthermore, the nonlinear quantitative characterization between the target flow of water dispatching and each feature variable was constructed under a limited data sample. 【Result】In other words, firstly the random forest model nonlinear regression algorithm is used to construct the nonlinear mapping between water dispatching target flow and each characteristic variable. Secondly, using SHAP to optimize the representation of the nonlinear mapping, which aims to eliminate the redundant correlation between different class feature variables and class feature variables considering spatial variation. In addition, the importance and ranking of the response of water dispatching target flow to each characteristic variable can be obtained, and the combination of characteristic variables suitable for actual water use scheduling is proposed, at the same time, the positive and negative directions of the response of target flow to each characteristic variable are given. By using this method, the importance scores and changes of each characteristic variable in long time series and different typical year scenarios can be obtained. In addition to finding the combination of characteristic variables suitable for actual water dispatching, the indexes of characteristic variables that dispatchers mainly refer to in different dispatching scenarios can be analyzed and obtained. Combined with the analysis of SHAP value, the positive and negative directions of the response of water dispatching target flow to each characteristic variable can be obtained.【Conclusion】On the whole, the quantitative knowledge representation of historical experience of water dispatching in irrigated areas is realized, which provides scientific basis for rational prediction of different water dispatching flows in the future. In this process, the method used in this paper has realized the quantitative knowledge representation of the historical experience of water dispatching in irrigated areas and provided scientific basis for rational prediction of different water dispatching flows in the future.
Key words: Pishihang Irrigation District; Water dispatching; Random forest model; SHAP method; Characteristic variable; Nonlinear characterization |
| Key words: Pishihang Irrigation District; Water dispatching; Random forest model; SHAP method; Characteristic variable; Nonlinear characterization |
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