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

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 u...

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Main Authors: Weibing Jia, Zhengying Wei, Lei Zhang
Format: Article
Language:English
Published: MDPI AG 2022-02-01
Series:Water
Subjects:
Online Access:https://www.mdpi.com/2073-4441/14/5/689
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author Weibing Jia
Zhengying Wei
Lei Zhang
author_facet Weibing Jia
Zhengying Wei
Lei Zhang
author_sort Weibing Jia
collection DOAJ
description 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.
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spelling doaj.art-3350e96c57194f1d83571310a1a854122023-11-24T00:01:37ZengMDPI AGWater2073-44412022-02-0114568910.3390/w14050689A Novel Prediction and Planning Model for the Benefit of Irrigation Water Allocation Based on Deep Learning and Uncertain ProgrammingWeibing Jia0Zhengying Wei1Lei Zhang2The State Key Laboratory for Manufacturing System Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaThe State Key Laboratory for Manufacturing System Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaThe State Key Laboratory for Manufacturing System Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaDue 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.https://www.mdpi.com/2073-4441/14/5/689irrigation water allocationfuzzy-boundary intervalshybrid modelwinter wheatsummer cornNorth China Plain
spellingShingle Weibing Jia
Zhengying Wei
Lei Zhang
A Novel Prediction and Planning Model for the Benefit of Irrigation Water Allocation Based on Deep Learning and Uncertain Programming
Water
irrigation water allocation
fuzzy-boundary intervals
hybrid model
winter wheat
summer corn
North China Plain
title A Novel Prediction and Planning Model for the Benefit of Irrigation Water Allocation Based on Deep Learning and Uncertain Programming
title_full A Novel Prediction and Planning Model for the Benefit of Irrigation Water Allocation Based on Deep Learning and Uncertain Programming
title_fullStr A Novel Prediction and Planning Model for the Benefit of Irrigation Water Allocation Based on Deep Learning and Uncertain Programming
title_full_unstemmed A Novel Prediction and Planning Model for the Benefit of Irrigation Water Allocation Based on Deep Learning and Uncertain Programming
title_short A Novel Prediction and Planning Model for the Benefit of Irrigation Water Allocation Based on Deep Learning and Uncertain Programming
title_sort novel prediction and planning model for the benefit of irrigation water allocation based on deep learning and uncertain programming
topic irrigation water allocation
fuzzy-boundary intervals
hybrid model
winter wheat
summer corn
North China Plain
url https://www.mdpi.com/2073-4441/14/5/689
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