"International Journal of Geography, Geology and Environment"

Author(s): Alagesan Arumugam, Jesupriya Poornakala Selvaraj and Thukkaiyannan Palaniappan

Abstract: The utilization of neural networks and machine learning techniques has revolutionized evaporation predictions from historical weather data. Researchers employ deep learning methodologies to create models that not only capture complex meteorological patterns but also enhance forecasting accuracy. Innovations like the Meteorological Information Service Decision Support System utilize predictive algorithms to optimize energy management and operational decisions in wind power forecasting, showing promising outcomes. Advanced surrogate models, including convolutional encoder-decoder networks and spatial-temporal graph neural networks, accurately simulate intricate hydrological processes to improve groundwater level predictions under data constraints. These advancements underscore the transformative impact of artificial intelligence in environmental modeling and resource management, opening avenues for practical applications. The integration of artificial neural networks and machine learning in evaporation forecasting signifies significant progress with implications for future research and practical implementation. Models such as the Meteorological Information Service Decision Support System enhance energy management by predicting factors affecting evaporation rates, aiding decision-making processes. Machine learning's effectiveness in various fields, including drug supply chain management, sets the stage for interdisciplinary applications using similar predictive parameters. Future research should concentrate on refining models for enhanced accuracy and real-time data integration to advance water resource management and climate response strategies crucial for addressing global environmental changes.

DOI: 10.22271/27067483.2025.v7.i1a.326

Pages: 29-33 | Views: 120 | Downloads: 55

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