ABSTRACT
Sustainability in water resource management is critical, given the necessity to monitor and predict key indicators such as SDG 6.4.2: Water Stress. This indicator, which measures the ratio of water resources abstracted relative to their availability, is vital for assessing the pressure on water resources and ensuring their long-term sustainability. The objective of this study is to compare various neural network architectures utilizing different optimisers to predict water stress. The analysis was based on a dataset sourced from AQUASTAT, encompassing data from 28 European countries. Several neural network architectures, with configurations ranging from two to four layers, were implemented, and evaluated using optimisers including SGD, Adam, RMSprop, and Adagrad. The findings revealed that a three-layer architecture combined with the Adam optimiser delivered the best performance, achieving an MSE of 0.02187 and a R2 of 0.9745, indicating high predictive accuracy. Nevertheless, a two-layer architecture with the SGD optimiser also exhibited strong performance, highlighting its simplicity and effectiveness. These results underscore the importance of meticulous selection of both architecture and optimiser when predicting critical indicators such as water stress. This study not only enhances the accuracy of water-related risk predictions 362but also supports informed decision-making for sustainable water resource management in Europe.
