Flood hazard mapping in western Iran: assessment of deep learning vis-à-vis machine learning models

Satarzadeh, Eslam; Sarraf, Amirpouya; Hajikandi, Hooman; Sadeghian, Mohammad Sadegh

doi:10.1007/s11069-021-05098-6

Cited by 20 publications

(5 citation statements)

References 48 publications

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“…In hydrological modeling applications, the LSTM network performs better than alternative models, illustrating its reliability and efficiency. Other applications of DL in floods include flood forecasting [45,46], flood monitoring [47,48], and flood hazard mapping [49].…”

Section: Literature Reviewmentioning

confidence: 99%

The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management

Kumar,

Azamathulla,

Sharma

et al. 2023

Sustainability

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Floods are a devastating natural calamity that may seriously harm both infrastructure and people. Accurate flood forecasts and control are essential to lessen these effects and safeguard populations. By utilizing its capacity to handle massive amounts of data and provide accurate forecasts, deep learning has emerged as a potent tool for improving flood prediction and control. The current state of deep learning applications in flood forecasting and management is thoroughly reviewed in this work. The review discusses a variety of subjects, such as the data sources utilized, the deep learning models used, and the assessment measures adopted to judge their efficacy. It assesses current approaches critically and points out their advantages and disadvantages. The article also examines challenges with data accessibility, the interpretability of deep learning models, and ethical considerations in flood prediction. The report also describes potential directions for deep-learning research to enhance flood predictions and control. Incorporating uncertainty estimates into forecasts, integrating many data sources, developing hybrid models that mix deep learning with other methodologies, and enhancing the interpretability of deep learning models are a few of these. These research goals can help deep learning models become more precise and effective, which will result in better flood control plans and forecasts. Overall, this review is a useful resource for academics and professionals working on the topic of flood forecasting and management. By reviewing the current state of the art, emphasizing difficulties, and outlining potential areas for future study, it lays a solid basis. Communities may better prepare for and lessen the destructive effects of floods by implementing cutting-edge deep learning algorithms, thereby protecting people and infrastructure.

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Section: Literature Reviewmentioning

confidence: 99%

The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management

Kumar,

Azamathulla,

Sharma

et al. 2023

Sustainability

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“…The capability of different DNN methods for predicting the flash flood in Iran has been evaluated (Satarzadeh et al 2022;Costache et al 2019;Chen et al 2022). These methods showed good performances (over 85%).…”

Section: Flood Susceptibility Mapsmentioning

confidence: 99%

A hybrid deep neural network with statistical learning algorithms for flood susceptibility modeling

Ghobadi

Ahmadipari

2023

Preprint

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Flood, with its environmental impact, is a naturally destructive process usually causes severe damage. Therefore, the determination of the areas susceptible to flood by the latest tools, which can render precise estimations, is essential to mitigate this damage. In this study, it was attempted to evaluate flood susceptibility in Lorestan, Iran using a novel hybrid approach including Deep Neural Network (DNN), Frequency Ratio (FR), and Stepwise Weight Assessment Ratio Analysis (SWARA). For this purpose, a geospatial database of floods, including 142 flood locations and 10 flood influencing variables, was used to predict the flood susceptibility areas. FR and SWARA were applied to weigh and score the flood influencing variables, while DNN, which is an excellent tool of machine learning and artificial intelligence, was used to prepare the inference flood pattern. The performance of the models was checked by the area under the curve (AUC), receiver operating characteristic (ROC) curve, and various statistical tests. The outputs indicated that both of the proposed algorithms, DNN-FR and DNN-SWARA, were able to estimate the future flood zones with a precision of over 90%. The outputs also confirmed that although the two algorithms had a high goodness-of-fit and prediction accuracy, the DNN-FR (AUC = 0.953) outperformed the DNN-SWARA (AUC = 0.941). Thus, the DNN-FR algorithm was proposed to be applied as a more reliable and accurate tool for spatial estimation of flood zones.

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“…Inclusion of relevant geo‐environmental‐related factors is a fundamental step in spatial flood modeling. The selection of factors in this study was based on a literature review and data availability (Janizadeh et al., 2021; Satarzadeh et al., 2021; Tien Bui et al., 2019). Slope, elevation, aspect, plan curvature, length of slope (LS), and topographic wetness index (TWI) were obtained directly from a digital elevation model of Sweden (with 50 m spatial resolution, downloaded from the Swedish Agricultural University: https://zeus.slu.se/get/?drop=get), using the tools available in the ArcGIS 10.8.2 software (http://www.esri.com).…”

Section: Case Study: Swedenmentioning

confidence: 99%

A Country Wide Evaluation of Sweden's Spatial Flood Modeling With Optimized Convolutional Neural Network Algorithms

Panahi,

Khosravi,

Rezaie

et al. 2023

Earth's Future

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Flooding is one of the most serious and frequent natural hazards affecting human life, property, and the environment. This study develops and tests a deep learning approach for large‐scale spatial flood modeling, using Convolutional Neural Network (CNN) and optimized versions combined with the Gray Wolf Optimizer (GWO) or the Imperialist Competitive Algorithm (ICA). With Sweden as an application case for nation‐wide flood susceptibility mapping, this modeling approach considers ten geo‐environmental input factors (slope, elevation, aspect, plan curvature, length of slope, topographic wetness index, distance from river, distance from wetland, rainfall, and land use). The GWO and ICA optimization improves model prediction by 12% and 8%, respectively, compared with the standalone CNN model performance. The results show 40% of the land area, 45% of the railroad, and 43% of the road network of Sweden to have high or very high flood susceptibility. They also show the aspect to have the highest input factor impact on flood susceptibility prediction while, for example, rainfall ranks only seven of the total 10 considered geo‐environmental input factors. In general, accurate nation‐wide flood susceptibility prediction is essential for guiding flood management and mitigation efforts. This study's approach to such prediction has emerged as well‐performing and cost‐effective for the case of Sweden, calling for further application and testing in other world regions.

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Flood hazard mapping in western Iran: assessment of deep learning vis-à-vis machine learning models

Cited by 20 publications

References 48 publications

The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management

The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management

A hybrid deep neural network with statistical learning algorithms for flood susceptibility modeling

A Country Wide Evaluation of Sweden's Spatial Flood Modeling With Optimized Convolutional Neural Network Algorithms

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