Evaporation Prediction Using Artificial Neural Networks and Machine Learning Based on Historical Weather Data

Abstract

The application of artificial neural networks and machine learning techniques has significantly improved the prediction of evaporation using historical weather data. These advanced computational approaches enable the modeling of complex meteorological relationships, thereby enhancing the accuracy of forecasting systems. Deep learning methods are increasingly being used to develop robust predictive models capable of capturing nonlinear patterns in environmental data. Recent innovations, such as decision support systems based on meteorological information, have demonstrated the effectiveness of predictive algorithms in optimizing resource management and supporting operational decision-making, particularly in areas such as wind energy forecasting. In addition, advanced surrogate modeling techniques, including convolutional encoder-decoder networks and spatial-temporal graph neural networks, have been successfully applied to simulate complex hydrological processes and improve the estimation of groundwater and related environmental variables under limited data conditions. These developments highlight the growing impact of artificial intelligence in environmental modeling and natural resource management. The integration of machine learning approaches into evaporation forecasting represents a major advancement with wide-ranging practical applications. Such models not only improve prediction accuracy but also support better planning and decision-making in water resource systems. Future research should focus on enhancing model precision, improving real-time data integration, and expanding the adaptability of these systems to changing climatic conditions. These improvements are essential for strengthening water resource management and addressing the challenges posed by global environmental change.