Analysing Urban Flooding Risk with CMIP5 and CMIP6 Climate Projections

Oyelakin, Rafiu; Yang, Wenyu; Krebs, Peter

doi:10.3390/w16030474

Cited by 5 publications

(1 citation statement)

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“…Floods stand as one of the most prevalent natural disasters globally. With an escalation in both the frequency and intensity of heavy rainfall, coupled with a recurring pattern, catastrophic flood events are being witnessed across various regions worldwide, significantly impacting human productivity and livelihoods [2,3]. Generally speaking, river and coastal system flooding represents the most prevalent and devastating climate-related disaster, inflicting billions of dollars in damages annually.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Review of Methods for Hydrological Forecasting Based on Deep Learning

Zhao,

Wang,

Bai

et al. 2024

Water

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Artificial intelligence has undergone rapid development in the last thirty years and has been widely used in the fields of materials, new energy, medicine, and engineering. Similarly, a growing area of research is the use of deep learning (DL) methods in connection with hydrological time series to better comprehend and expose the changing rules in these time series. Consequently, we provide a review of the latest advancements in employing DL techniques for hydrological forecasting. First, we examine the application of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) in hydrological forecasting, along with a comparison between them. Second, a comparison is made between the basic and enhanced long short-term memory (LSTM) methods for hydrological forecasting, analyzing their improvements, prediction accuracies, and computational costs. Third, the performance of GRUs, along with other models including generative adversarial networks (GANs), residual networks (ResNets), and graph neural networks (GNNs), is estimated for hydrological forecasting. Finally, this paper discusses the benefits and challenges associated with hydrological forecasting using DL techniques, including CNN, RNN, LSTM, GAN, ResNet, and GNN models. Additionally, it outlines the key issues that need to be addressed in the future.

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Section: Introductionmentioning

confidence: 99%