Predictive Analysis Using a Novel Deep Learning Algorithm “Geonet” for Flood Likelihood Monitoring
Keywords:
Machine learning, Deep learning, Geological AI, Flood prediction, LSTMAbstract
In alignment with the United Nations’ Sustainable Development Goals, India is committing itself to a comprehensive framework of 17 goals and 179 targeted objectives that address social, economic, and environmental challenges, aiming to achieve these by 2030. Against the backdrop of recurring flood events in Kolhapur, Maharashtra, our research takes on a critical role: predicting the likelihood of flooding by analyzing key weather parameters. The proposed innovative approach involves meticulous data collection, thorough pre-processing, and the application of the groundbreaking “GeoNet” deep learning algorithm. This state-of-the-art algorithm is meticulously designed to classify and predict flood-prone conditions by analyzing vital parameters such as pressure, maximum temperature, actual temperature, minimum temperature, dew point, humidity, cloud cover, sunshine, wind direction, and wind speed. By processing daily data over an entire year, the proposed study creates a robust model that offers actionable insights. Furthermore, the representation of weather variables through the proposed model is informative and visually compelling. As demonstrated by the proposed comprehensive analysis of the confusion matrix, the results reveal that the GeoNet algorithm significantly outperforms existing machine learning classifiers, achieving an impressive accuracy rate of 85%. This advancement strengthens our understanding of flood dynamics and enhances our ability to implement timely and effective mitigation strategies.
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