Application of Artificial Neural Networks for Rainfall Estimation: Case Study in the Pastaza River Basin, Ecuador
Application of Artificial Neural Networks for Rainfall Estimation: Case Study in the Pastaza River Basin, Ecuador
DOI:
https://doi.org/10.56124/finibus.v7i14.013Keywords:
artificial neural networks, rainfall estimation, water management, pastaza river basinAbstract
The Pastaza River basin in Ecuador, crucial for its biodiversity and water management, faces significant challenges due to climate change. This study presents the application of artificial neural networks (ANN) to address deficiencies in pluviometric data for this basin. By implementing an optimised model with 5000 iterations, a 95% reliability in precipitation data estimation was achieved. Data from multiple meteorological stations were analysed, adjusting the model based on the distances between stations, demonstrating improved accuracy and coherence compared to traditional methods. The results highlight the ANN's capability to adapt to significant data variations, enhancing water management planning and mitigating the effects of extreme weather events through better precipitation prediction. The ability of ANN to process large volumes of data with complex interactions is particularly relevant in the hydrometeorological field, where spatial and temporal data variability is substantial. This advancement demonstrates the applicability of ANN in hydrology and climatology, contributing to the understanding of regional climate variability. The integration of advanced artificial intelligence techniques in the estimation and homogenisation of hydrological data provides a solid foundation for developing more effective adaptation and mitigation strategies in response to climate change. As technology evolves, new perspectives emerge for applying similar techniques in other river basins in the region, improving the management of water resources in Ecuador.
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