Prediction of water consumption and affecting factor analysis using Inception-V4 network and enhanced single candidate optimization: a case study

Predicting water usage is a crucial aspect of organization and management of water resources. Developing a highly precise model for forecasting water consumption is significant in enhancing regional water resource management and supporting sustainable growth in the socioeconomic sector. This study investigates the primary elements that influence water consumption. This study examines the water consumption prediction model to enhance the efficiency of selecting its parameters and offers insights for analyzing regional water usage as well as for planning and managing water resources. Utilizing data on water consumption and its influencing factors from 2003 to 2023, it aims to forecast future water usage. This research employs the developed Inception-V4 (IV4) network by Enhanced Single Candidate Optimization (ESCO), for analyzing regional water consumption. The extracted effective factors have been utilized as input samples for the developed model to forecast water consumption over the next 15 years. The findings indicate that the RMSE and the MAE for modeling that have been made using the developed IV4 are 0.0519 and 0.0407 respectively. This result shows superior performance compared to other models in terms of prediction error and trend accuracy.