A Survey on Flood Predication and Classification using Machine learning

Abstract

Flood prediction and classification play a vital role in mitigating the impact of floods and ensuring the safety of communities residing in flood-prone areas. The use of machine learning techniques has gained significant attention in recent years due to their ability to effectively analyse large datasets and make accurate predictions. This survey aims to provide a comprehensive overview of the various machine learning approaches employed in flood prediction and classification. The survey begins by discussing the fundamental concepts and challenges associated with flood prediction. It highlights the importance of accurate and timely predictions in minimizing the potential damage caused by floods. The role of machine learning in addressing these challenges is then explored, emphasizing its potential to improve prediction accuracy and enhance early warning systems. Next, the survey presents a detailed analysis of the different machine learning algorithms used for flood prediction and classification. It covers a broad spectrum of techniques, including traditional algorithms such as decision trees, support vector machines, and naive Bayes, as well as more advanced methods such as artificial neural networks, random forests, and deep learning models. The strengths and limitations of each algorithm are discussed, along with their applicability to different flood prediction scenarios. Furthermore, the survey investigates the various data sources and features utilized in flood prediction models. It explores the use of remote sensing data, meteorological data, hydrological data, and social media data, among others, highlighting their role in improving prediction accuracy. The challenges associated with data collection, pre-processing, and feature selection are also addressed. The survey further examines the evaluation metrics and validation techniques used to assess the performance of flood prediction models. It discusses commonly used metrics such as accuracy, precision, recall, and F1-score, as well as cross-validation and time series analysis techniques. Lastly, the survey presents a critical analysis of the current research trends and identifies potential areas for future research in flood prediction using machine learning. It discusses emerging technologies such as Internet of Things (IoT), big data analytics, and ensemble learning, and their potential impact on improving flood prediction accuracy and efficiency.