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Using Machine Learning Models to Forecast the Conversion Coefficient between Electricity Consumption and Water Pumped for Irrigation Wells in Baicheng City, China
Forecasting the electricity-to-water conversion coefficient (EWCC) can help manage and plan irrigation water in arid and semiarid areas. However, the EWCC is influenced by several factors, making it difficult to develop an analytical model for validation or prediction. Therefore, this study selected 206 typical irrigation wells in Baicheng City to conduct EWCC tests in a field investigation to gather information regarding the results and related influencing factors. Subsequently, machine learning models (multiple linear regression model, support vector model, and backpropagation neural network) were trained, validated, and tested, and their precisions were evaluated and compared. The backpropagation neural network model was the most accurate, followed by the support vector and multiple linear regression models. The backpropagation neural network model results were consistent with those of the field survey, and this model was thus used to forecast the EWCC for all the townships in Baicheng City. The forecasting models revealed that most towns had an EWCC from 3 to 7 m3/kW·h, with an EWCC greater than 7 observed in the Tao’er River Fan and Yueliangpao District. The BP models developed in this study proved to be dependable and applicable for forecasting the EWCC in this area.
Using Machine Learning Models to Forecast the Conversion Coefficient between Electricity Consumption and Water Pumped for Irrigation Wells in Baicheng City, China
Forecasting the electricity-to-water conversion coefficient (EWCC) can help manage and plan irrigation water in arid and semiarid areas. However, the EWCC is influenced by several factors, making it difficult to develop an analytical model for validation or prediction. Therefore, this study selected 206 typical irrigation wells in Baicheng City to conduct EWCC tests in a field investigation to gather information regarding the results and related influencing factors. Subsequently, machine learning models (multiple linear regression model, support vector model, and backpropagation neural network) were trained, validated, and tested, and their precisions were evaluated and compared. The backpropagation neural network model was the most accurate, followed by the support vector and multiple linear regression models. The backpropagation neural network model results were consistent with those of the field survey, and this model was thus used to forecast the EWCC for all the townships in Baicheng City. The forecasting models revealed that most towns had an EWCC from 3 to 7 m3/kW·h, with an EWCC greater than 7 observed in the Tao’er River Fan and Yueliangpao District. The BP models developed in this study proved to be dependable and applicable for forecasting the EWCC in this area.
Using Machine Learning Models to Forecast the Conversion Coefficient between Electricity Consumption and Water Pumped for Irrigation Wells in Baicheng City, China
Hao Ke (author) / Fang Zhang (author) / Yang Sikai (author) / Ma Zhe (author) / Xu Bin (author)
2024
Article (Journal)
Electronic Resource
Unknown
electricity-to-water conversion coefficient (EWCC) , agriculture irrigation wells , machine learning method , multiple linear regression model (MLR) , support vector model (SVM) , backpropagation neural network (BP) , Hydraulic engineering , TC1-978 , Water supply for domestic and industrial purposes , TD201-500
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