Comprehensive Overview of Flood Modeling Approaches: A Review of Recent Advances

Abstract

As one of nature’s most destructive calamities, floods cause fatalities, property destruction, and infrastructure damage, affecting millions of people worldwide. Due to its ability to accurately anticipate and successfully mitigate the effects of floods, flood modeling is an important approach in flood control. This study provides a thorough summary of flood modeling’s current condition, problems, and probable future directions. The study of flood modeling includes models based on hydrologic, hydraulic, numerical, rainfall–runoff, remote sensing and GIS, artificial intelligence and machine learning, and multiple-criteria decision analysis. Additionally, it covers the heuristic and metaheuristic techniques employed in flood control. The evaluation examines the advantages and disadvantages of various models, and evaluates how well they are able to predict the course and impacts of floods. The constraints of the data, the unpredictable nature of the model, and the complexity of the model are some of the difficulties that flood modeling must overcome. In the study’s conclusion, prospects for development and advancement in the field of flood modeling are discussed, including the use of advanced technologies and integrated models. To improve flood risk management and lessen the effects of floods on society, the report emphasizes the necessity for ongoing research in flood modeling.

A Transfer Learning Approach Based on Radar Rainfall for River Water-Level Prediction

“…The convolution layer applies a convolution operation to create feature maps smaller than the original input image while retaining the feature information to cope with prediction tasks. The convolution operation is a summation-product operation defined by an input image and a filter, as shown in the following Equation (1).…”

“…River water-level forecasting is important in predicting floods and mitigating disaster losses. Numerous studies have been conducted to improve forecasting accuracy [1]. This issue has become even more important in recent years, as climate change has caused more frequent torrential rains and increased flooding worldwide, especially in Asia [2,3].…”

“…Traditionally, river water-level prediction has been conducted based on physical models. Many contributions have been made in the literature to improve prediction accuracy [1]. Since the popularization of classical lump models such as the tank model [8] and the storage function (SF) model [9,10], several distributed models have been proposed.…”

Advanced Machine Learning Techniques to Improve Hydrological Prediction: A Comparative Analysis of Streamflow Prediction Models

“…In recent years, there has been a lot of interest in the ability of ML algorithms to manage enormous datasets and capture intricate relationships in hydrological systems. These methods provide a data-driven approach to hydrological modeling, allowing for the creation of prediction models that are more precise [43,44]. Different ML techniques, including ANN, SVM, and decision trees, have been used in the context of river flow prediction to improve forecasting abilities [45,46].…”

Hydro-Meteorological Disaster Incidents and Associated Weather Systems in Sri Lanka

“…The use of deep learning (Deep Learning and Machine learning are two subfields within the artificial intelligence) in flood forecasting and management has the potential to revolutionize the field by increasing the accuracy and timeliness of flood predictions (Kumar et al, 2023b). To increase the precision and effectiveness of flood models, emerging technologies such as remote sensing, cloud computing, and artificial intelligence are essential (Kumar et al, 2023c). The present study analyses the extreme events and climate systems associated with them.…”