Deep learning methods for flood mapping: a review of existing applications and future research directions

Hydrology and earth system sciences, Journal Year: 2022, Volume and Issue: 26(16), P. 4345 - 4378

Published: Aug. 25, 2022

Abstract. Deep learning techniques have been increasingly used in flood management to overcome the limitations of accurate, yet slow, numerical models and improve results traditional methods for mapping. In this paper, we review 58 recent publications outline state art field, identify knowledge gaps, propose future research directions. The focuses on type deep various mapping applications, types considered, spatial scale studied events, data model development. show that based convolutional layers are usually more as they leverage inductive biases better process characteristics flooding events. Models fully connected layers, instead, provide accurate when coupled with other statistical models. showed increased accuracy compared approaches speed methods. While there exist several applications susceptibility, inundation, hazard mapping, work is needed understand how can assist real-time warning during an emergency it be employed estimate risk. A major challenge lies developing generalize unseen case studies. Furthermore, all reviewed their outputs deterministic, limited considerations uncertainties outcomes probabilistic predictions. authors argue these identified gaps addressed by exploiting fundamental advancements or taking inspiration from developments applied areas. graph neural networks operators arbitrarily structured thus should capable generalizing across different studies could account complex interactions natural built environment. Physics-based preserve underlying physical equations resulting reliable speed-up alternatives Similarly, resorting Gaussian processes Bayesian networks.

Language: Английский

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Predicting future urban waterlogging-prone areas by coupling the maximum entropy and FLUS model

Sustainable Cities and Society, Journal Year: 2022, Volume and Issue: 80, P. 103812 - 103812

Published: March 1, 2022

Language: Английский

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A comparative assessment of flood susceptibility modelling of GIS-based TOPSIS, VIKOR, and EDAS techniques in the Sub-Himalayan foothills region of Eastern India

Environmental Science and Pollution Research, Journal Year: 2022, Volume and Issue: 30(6), P. 16036 - 16067

Published: Sept. 30, 2022

Language: Английский

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Flash-flood hazard using deep learning based on H2O R package and fuzzy-multicriteria decision-making analysis

Journal of Hydrology, Journal Year: 2022, Volume and Issue: 609, P. 127747 - 127747

Published: March 24, 2022

Language: Английский

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Evaluating the association between morphological characteristics of urban land and pluvial floods using machine learning methods

Sustainable Cities and Society, Journal Year: 2023, Volume and Issue: 99, P. 104891 - 104891

Published: Aug. 22, 2023

Language: Английский

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Threats of climate change and land use patterns enhance the susceptibility of future floods in India

Journal of Environmental Management, Journal Year: 2021, Volume and Issue: 305, P. 114317 - 114317

Published: Dec. 24, 2021

Language: Английский

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A GIS-based flood risk mapping of Assam, India, using the MCDA-AHP approach at the regional and administrative level

Geocarto International, Journal Year: 2022, Volume and Issue: 37(26), P. 11867 - 11899

Published: March 30, 2022

Floods are frequently occurring events in the Assam region due to presence of Brahmaputra River and heavy monsoon period. An efficient reliable methodology is utilized prepare a GIS-based flood risk map for region, India. At regional administrative level, hazard index (FHI), vulnerability (FVI), (FRI) developed using multi-criteria decision analysis (MCDA) – analytical hierarchy process (AHP). The selected indicators define topographical, geological, meteorological, drainage characteristics, land use cover, demographical features Assam. results show that more than 70%, 57.37%, 50% total area lie moderate very high FHI, FVI, FRI classes, respectively. proposed can be applied identify zones carry out effective management mitigation strategies vulnerable areas.

Language: Английский

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A practical approach to flood hazard, vulnerability, and risk assessing and mapping for Quang Binh province, Vietnam

Environment Development and Sustainability, Journal Year: 2022, Volume and Issue: 25(2), P. 1101 - 1130

Published: Jan. 19, 2022

Language: Английский

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Flood susceptibility mapping of Northeast coastal districts of Tamil Nadu India using Multi-source Geospatial data and Machine Learning techniques

Geocarto International, Journal Year: 2022, Volume and Issue: 37(27), P. 15252 - 15281

Published: June 30, 2022

Flooding is one of the most challenging and important natural disasters to predict, it becoming more frequent intense. The study area badly damaged by devastating flood in 2015. We assessed susceptibility northern coastal Tamil Nadu using various machine learning algorithms such as Gradient Boosting Machine (GBM), XGBoost (XGB), Rotation Forest (RTF), Support Vector (SVM), Naive Bayes (NB). Google Earth Engine (GEE) used demarcate flooded areas Sentinel-l other multi-source geospatial data generate influential factors. Recursive Feature Elimination (RFE) removes weak factors this study. resultant map classified into five classes: very low, moderate, high, high. GBM algorithm attained high classification accuracy with an under curve (AUC) value 92%. urbanized vulnerable identifying inundation useful for effective planning implementation.

Language: Английский

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A comparative assessment of multi-criteria decision-making analysis and machine learning methods for flood susceptibility mapping and socio-economic impacts on flood risk in Abela-Abaya floodplain of Ethiopia

Environmental Challenges, Journal Year: 2022, Volume and Issue: 9, P. 100629 - 100629

Published: Oct. 4, 2022

Floods have a terrible impact on people's lives and property all around the world. In this study, we evaluated modeling capabilities of two Machine Learning (ML) approaches such as Naive Bayes Tree (NBT) (NB) four Multi-Criteria Decision-Making (MCDM) analysis techniques MABAC (multi-attributive border approximation area comparison), TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), VIKOR (Vise Kriterijumska Optimizacijaik Ompromisno Resenje), SAW (Simple Additive Weighting) were applied in Abela-Abaya floodplain Ethiopia. Sixteen flood influencing factors elevation, land use/cover (LULC), soil, aspect, geomorphology, normalized difference vegetation index (NDVI), rainfall, distance from river (DR..), topographic wetness (TWI), sediment transport (STI), stream power (SPI), geology, curvature, flow accumulation (FA), slope direction (FD) used input parameters. Area Under Receiver Operating Characteristic Curve was assess validate models' predictive (AUC). The NB model performed best (AUC = 0.92), indicating that it is viable strategy determining flood-prone locations order properly plan control hazards. Face face interactive sessions conducted with 160 respondents find coordinated between issues community's apprehension danger analyze socioeconomic risk. findings revealed respondents' socio-demographic traits, experience, awareness, prevention responsibility, government confidence building cohesively related their opinion danger. estimated damage households farmland $6249 $5326 respectively 2016 flood. compilation study susceptibility mapping risk seeks enhance human perceptions minimize enhancing communication about inspiring people areas take measures mitigating damage.

Language: Английский

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