Performance analysis of physically-based (HEC-RAS, CADDIES) and AI-based (LSTM) flood models for two case studies
Copyright policy )Performance analysis of physically-based (HEC-RAS, CADDIES) and AI-based (LSTM) flood models for two case studies
Abstract. Megacities in developing countries are commonly affected by flooding events. The use of flood models can contribute to an evidence-based decision-making process. For a good representation, these models require physical data for catchment parameterization, and observed data for calibration and validation, which is often scarce. In this study, we analysed the performance results of physically-based (HEC-RAS, CADDIES) and AI-based (LSTM) flood models for two case studies: the Narmada basin in India and the Aricanduva catchment in Brazil. The models were evaluated for accuracy, interpretability, running time, and complexity.
Artificial intelligence, Environmental Engineering, Rainfall-Runoff Modeling, Hydrological Modeling, Urban Flooding, Anomaly Detection in High-Dimensional Data, Global Flood Risk Assessment and Management, Artificial Intelligence, Hydrological Modeling using Machine Learning Methods, GE1-350, QE1-996.5, Global and Planetary Change, Geography, FOS: Environmental engineering, Groundwater Level Forecasting, Geology, Flood myth, Computer science, Environmental sciences, Archaeology, Computer Science, Physical Sciences, Environmental Science, Flood Inundation Modeling
Artificial intelligence, Environmental Engineering, Rainfall-Runoff Modeling, Hydrological Modeling, Urban Flooding, Anomaly Detection in High-Dimensional Data, Global Flood Risk Assessment and Management, Artificial Intelligence, Hydrological Modeling using Machine Learning Methods, GE1-350, QE1-996.5, Global and Planetary Change, Geography, FOS: Environmental engineering, Groundwater Level Forecasting, Geology, Flood myth, Computer science, Environmental sciences, Archaeology, Computer Science, Physical Sciences, Environmental Science, Flood Inundation Modeling
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).1 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Citations provided by BIP!Popularity provided by BIP!