Investigating the Hydrometeorological Precursors of Floods in the Plains of India

Floods rank among the most devastating natural calamities in the Indian plains, where monsoon regimes and large river systems result in recurrent flooding. Knowing the hydrometeorological precursors of floods is paramount to enhance predictive accuracy and decrease risks. This study investigates the most significant hydrometeorological parameters influencing flood events in the Indian plains and develops predictive models using Machine Learning (MI) and Deep Learning techniques. This research uses a 60-year historical rainfall record of five cities, Patna, Kanpur, Prayagraj, Haridwar, and Varanasi, collected from the India Meteorological Department (IMD). Robust statistical modelling and feature selection techniques determine the most significant flood predictors. The research adopts some of the machine and deep learning techniques such as Random Forest, Support Vector Machines (SVM), K-Nearest Neighbour (KNN), Long Short-Term Memory (LSTM) networks, Fully Connected Networks (FCN), Deep FCN, Convolutional Neural Networks (CNNs) to evaluate their performance when making flood forecasts. The results show that the intensity of rainfall plays a vital role in determining floods. LSTM networks handle the time sequential data and generate the future rainfall data, providing an FCN-trained model for better prediction accuracy. The proposed Deep Learning - based models demonstrate the effectiveness of early flood warning systems that allow authorities to initiate preventive measures promptly. The results also demonstrate the significance of region-specific flood prevention measures in response to climate variability and land- use changes in the Indian plains. By improving the accuracy of flood forecasts, the study is helpful to disaster management agencies, policymakers, and researchers. The present research will inspire the integration of onboard data sources from other locations and the model's generalizability to other flood-risk areas. Roll-out of Machine Learning and Deep Learning - driven approaches in flood forecasting will significantly minimize the socio-economic impact of floods, leading to enhanced preparedness and resilience for high-risk communities.