An Integrated Machine Learning and Deep Learning Framework for River-Based Flood Early Warning System

The impact of global climatic conditions and unpredictable rainfall patterns has led to numerous natural disasters in Manipur, including floods and landslides. Developing a reliable flood forecasting and early warning system is crucial to prevent loss of life, property, infrastructure, and crops. This paper presents a machine learning and deep learning framework for flood prediction, comprising four modules: Data Collection, Data Manipulation, Model Implementation, and Early Warning and Flood Forecasting. By leveraging daily data from gauge stations and weather reports, the framework aims to predict river water levels accurately, particularly during the monsoon season. The objectives include developing a model that integrates K-Nearest Neighbor (KNN) with Long Short-Term Memory (LSTM) networks for predicting future water levels using multivariate time series data and providing an algorithm that offers early warnings for potential floods. The proposed framework employs a multivariate time-series approach using a hybrid KNN-LSTM method to predict river water levels. Missing data are imputed using the KNN algorithm with historical weather data, and an LSTM network predicts future water levels based on this information. This approach effectively models temporal dependencies across multiple gauge stations in the Imphal Valley, Manipur. The performance metrics for the proposed model are NSE score of 0.9393, MAPE score of 0.1076, and RMSE score of 1.1828, which are relatively better than those of the conventional approaches. This study presents a novel flood forecasting approach by integrating for imputing missing data with LSTM networks to capture temporal dependencies. The framework is applied to homogeneous regions with correlated time series, effectively modeling the complex dynamics of river water levels across multiple gauge stations.