Advancing water demand management: predictive analytics using convolutional neural networks and developed maritime search and rescue algorithm based on the shared socioeconomic pathways

Abstract Efficient management of water resources is crucial based on the idea of developing socioeconomic conditions. To achieve this, it is essential to forecast water demand accurately. This investigation introduces a predictive framework that utilizes a convolutional neural network-based Xception model, which has been optimized through the developed maritime search and rescue algorithm to increase accuracy in forecasting future water demand trends under shared socioeconomic pathway scenarios. The enhanced Xception model uses the shared socioeconomic pathways to evaluate the potential effects of socioeconomic growth on domestic and industry demand. Policymakers and managers of water resources can benefit from the findings of this investigation, as it provides insights into the future trends of water needs. This information can help in making informed decisions and planning for sustainable water resource management, even in the presence of uncertainty and variability. The study’s results can enable a better understanding of future water demand patterns.