Analysis and predictive model research of water demand features in southern Anhui rural areas
Accurate prediction of water demand plays a crucial role in enhancing the utilization efficiency of water resources in rural areas and optimizing the daily scheduling of rural drinking water plants.However,existing forecasting algorithms face challenges such as limited availability and poor quality of historical data in rural areas.Additionally,the computational load of some algorithms is high,making them less adaptable to the operational environment of rural drinking water facilities.In this study,daily water supply data from three different rural drinking water plants in southern Anhui Province from 2021 to 2022 were selected as the research subjects.Through feature analysis,it was observed that daily water demand in rural areas of southern Anhui exhibits significant seasonal trends and considerable short-term fluctuations.Addressing the data characteristics and computational requirements,this study selected the ARIMA forecasting model optimized using the grid search method for prediction,and conducted comparative analyses with other models such as SVR,LSTM and RF.The results indicate that the ARIMA model,when en-hanced with grid search,is better suited for the operational environment of rural drinking water fa-cilities.Leveraging its strong learning capability for periodic and seasonal data,the model accurate-ly predicts the trends and patterns in water supply at these facilities.Furthermore,it demonstrates a certain degree of universality and has a significantly lower computational burden compared to oth-er methods.Model comparison analysis demonstrates that the ARIMA model exhibits the highest prediction accuracy.The Mean Absolute Percentage Error(MAPE)for the test dataset of three ag-ricultural drinking water plants are 1.827,1.454 and 2.714,respectively.
Agricultural drinking water plantsWater quantity forecastingMachine learningARIMA model
万方数据
维普
