Using Machine Learning Models to Forecast the Conversion Coefficient between Electricity Consumption and Water Pumped for Irrigation Wells in Baicheng City, China

Abstract

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.

Methodology to Increase the Efficiency of the Mineral Water Extraction Process

“…However, this work is not limited to obtaining a mathematical apparatus. Studies [90][91][92][93] show the possibility of using neural networks for analyzing field data. This approach is the subject of further research.…”

“…For example, 1 kg of phosphorus contains phosphate fertilizers from the Syundyukov deposit [88][89][90][91]: Due to the increase in soil fertility with fertilizers, the content of the following elements increases: Mo, Zn, Cr increase to 11-12%, Pb and B increase to 7-8% and Cu, Ni, V increase to 2-4%.…”