Enhancing Flood Forecasting Accuracy Through Machine Learning Approaches

Abstract

Flood prediction is a critical aspect of disaster management and requires accurate forecasting techniques to mitigate the potential risks and impacts. In this study, a flood prediction model is developed and built using machine learning algorithms. The objective is to develop a robust and reliable system that can forecast the occurrence and severity of floods in a specific region. The proposed model utilizes historical data on rainfall (in millimeters) to train the machine learning algorithms, such as decision tree, random forest, K-nearest neighbors (KNN), and logistic regression algorithms to build predictive models. These algorithms are known for their capability to handle diverse data patterns and provide accurate predictions. The dataset used for training and evaluation is sourced from the region of Kerala, India, which experiences frequent flood occurrences. The data is preprocessed, including cleaning, handling missing values, and converting categorical variables, to ensure the quality and compatibility of input features. Experimental results demonstrate the effectiveness of the developed models in flood prediction. The decision tree algorithm provides interpretability and identifies significant variables influencing flood occurrence. The KNN algorithm shows promising results in capturing local patterns and neighbors’ influence. Random forest leverages ensemble learning to enhance prediction accuracy, while logistic regression estimates the probability of flood events. The proposed flood prediction models offer valuable insights for early warning systems, disaster response planning, and resource allocation. The integration of machine learning algorithms enhances the accuracy and reliability of flood prediction, facilitating proactive measures to mitigate the potential risks associated with flooding.