A Novel Prediction and Planning Model for the Benefit of Irrigation Water Allocation Based on Deep Learning and Uncertain Programming

Weibing Jia,Zhengying Wei,Lei Zhang

doi:10.3390/w14050689

Abstract

Due to population growth and human activities, water shortages have become an increasingly serious concern in the North China Plain, which has become the world’s largest underground water funnel. Because the yield per unit area, planting area of crops, and effective precipitation in the region are uncertain, it is not easy to plan the amount of irrigation water for crops. In order to improve the applicability of the uncertainty programming model, a hybrid LSTM-CPP-FPP-IPP model (long short-term memory, chance-constrained programming, fuzzy possibility programming, interval parameter programming) was developed to plan the irrigation water allocation of irrigation system under uncertainty. The LSTM (long short-term memory) model was used to predict crop yield per unit area, and CPP-FPP-IPP programming (chance-constrained programming, fuzzy possibility programming, interval parameter programming) was used to plan the crop area and the effective precipitation under uncertainty. The hybrid model was used for the crop production profit of winter wheat and summer corn in five cities in the North China Plain. The average absolute error between the model prediction value and the actual value of the yield per unit area of winter wheat and summer maize in four cities in 2020 was controlled within the range of 14.02 to 696.66 kg/hectare. It shows that the model can more accurately predict the yield per unit area of crops. The planning model for the benefit of irrigation water allocation generated three scenarios of rainfall level and four planting intentions, and compared the planned scenarios with the actual production benefits of the two crops in 2020. In a dry year, the possibility of planting areas for winter wheat and summer corn is optimized. Compared with the traditional deterministic planning method, the model takes into account the uncertain parameters, which helps decision makers seek better solutions under uncertain conditions.

Highlights

The North China Plain is the main grain producing area in China
The aim of this study is to develop a prediction and planning model of the benefits of irrigation water allocation based on machine learning and uncertain programming; the key point of the study is the relationship between irrigation water consumption and production benefits under uncertain conditions
The irrigation water allocation benefits planning model based on hybrid uncertain programming was developed, and the production profits of winter wheat and summer corn in different scenarios were planned

Summary

Introduction

The contradiction between the demand for irrigation water and water supply in the area is most prominent. Because the continuing development of irrigated agriculture has caused the continued overexploitation of groundwater, the North China Plain has become the world’s largest underground water funnel, which has led to land subsidence, saltwater intrusion, the shrinkage of rivers and wetlands, land degradation, and a series of ecological and environmental problems [1]. For regional agricultural irrigation water distribution, the main challenges in water resources allocation are the uncertainty of water supply caused by climate change, the lack of consideration about the dynamic allocation of water resources, and the lack of equitable water allocation, which may lead to intensified conflicts among the different sectors of water users [2]. Fu et al [3]

Methods

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Discussion

Conclusion