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

Sustainability, Journal Year: 2023, Volume and Issue: 15(13), P. 10543 - 10543

Published: July 4, 2023

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

Language: Английский

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Explainable artificial intelligence in disaster risk management: Achievements and prospective futures

International Journal of Disaster Risk Reduction, Journal Year: 2023, Volume and Issue: 98, P. 104123 - 104123

Published: Nov. 1, 2023

Disasters can have devastating impacts on communities and economies, underscoring the urgent need for effective strategic disaster risk management (DRM). Although Artificial Intelligence (AI) holds potential to enhance DRM through improved decision-making processes, its inherent complexity "black box" nature led a growing demand Explainable AI (XAI) techniques. These techniques facilitate interpretation understanding of decisions made by models, promoting transparency trust. However, current state XAI applications in DRM, their achievements, challenges they face remain underexplored. In this systematic literature review, we delve into burgeoning domain XAI-DRM, extracting 195 publications from Scopus ISI Web Knowledge databases, selecting 68 detailed analysis based predefined exclusion criteria. Our study addresses pertinent research questions, identifies various hazard types, components, methods, uncovers limitations these approaches, provides synthesized insights explainability effectiveness decision-making. Notably, observed significant increase use 2022 2023, emphasizing interpretability. Through rigorous methodology, offer key directions that serve as guide future studies. recommendations highlight importance multi-hazard analysis, integration early warning systems digital twins, incorporation causal inference methods strategy planning effectiveness. This serves beacon researchers practitioners alike, illuminating intricate interplay between revealing profound solutions revolutionizing management.

Language: Английский

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Leveraging machine learning and open-source spatial datasets to enhance flood susceptibility mapping in transboundary river basin

International Journal of Digital Earth, Journal Year: 2024, Volume and Issue: 17(1)

Published: Feb. 9, 2024

Floods pose devastating effects on the resiliency of human and natural systems. flood risk management challenges are typically complicated in transboundary river basin due to conflicting objectives between multiple countries, lack systematic approaches data monitoring sharing, limited collaboration developing a unified system for hazard prediction communication. An open-source, low-cost modeling framework that integrates open-source models can help improve our understanding susceptibility inform design equitable strategies. This study datasets machine -learning techniques quantify across data-scare basin. The analysis focuses Gandak River Basin, spanning China, Nepal, India, where damaging recurring floods serious concern. is assessed using four widely used learning techniques: Long-Short-Term-Memory, Random Forest, Artificial Neural Network, Support Vector Machine. Our results exhibit improved performance Network Machine predicting maps, revealing higher vulnerability southern plains. demonstrates remote sensing prediction, mapping, environment.

Language: Английский

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Explainable machine learning for the prediction and assessment of complex drought impacts

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 898, P. 165509 - 165509

Published: July 17, 2023

Language: Английский

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Uncertainty Reduction in Flood Susceptibility Mapping Using Random Forest and eXtreme Gradient Boosting Algorithms in Two Tropical Desert Cities, Shibam and Marib, Yemen

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(2), P. 336 - 336

Published: Jan. 15, 2024

Flooding is a natural disaster that coexists with human beings and causes severe loss of life property worldwide. Although numerous studies for flood susceptibility modelling have been introduced, notable gap has the overlooked or reduced consideration uncertainty in accuracy produced maps. Challenges such as limited data, due to confidence bounds, overfitting problem are critical areas improving accurate models. We focus on mapping, mainly when there significant variation predictive relevance predictor factors. It also noted receiver operating characteristic (ROC) curve may not accurately depict sensitivity resulting map overfitting. Therefore, reducing was targeted increase improve processing time prediction. This study created spatial repository test models, containing data from historical flooding twelve topographic geo-environmental conditioning variables. Then, we applied random forest (RF) extreme gradient boosting (XGB) algorithms susceptibility, incorporating variable drop-off empirical loop function. The results showed function crucial method resolve model associated factors methods. approximately 8.42% 9.89% Marib City 9.93% 15.69% Shibam were highly vulnerable floods. Furthermore, this significantly contributes worldwide endeavors focused hazards linked disasters. approaches used can offer valuable insights strategies risks, particularly Yemen.

Language: Английский

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Efficiency evaluation of low impact development practices on urban flood risk

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 356, P. 120467 - 120467

Published: March 13, 2024

Language: Английский

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Utilizing Remote Sensing and GIS Techniques for Flood Hazard Mapping and Risk Assessment

Civil Engineering Journal, Journal Year: 2024, Volume and Issue: 10(5), P. 1423 - 1436

Published: May 1, 2024

In this paper, a comprehensive flood hazard map for the vicinity of King Talal Dam in Jordan, utilizing advanced remote sensing (RS) and GIS methodologies, is developed. Key geographical environmental factors, encompassing terrain slope, elevation, aspect, proximity to water streams, drainage density, land use/land cover, are integrated highlight areas with increased risk. This study, by employing novel theoretical approach, harnesses synergistic capabilities RS collect analyze geospatial data. The Analytic Hierarchy Process (AHP) applied assign weights various flood-conditioning quantifying their relative importance risk assessment. Through weighted sum overlay technique, aforementioned factors categorize levels from very low high. study successfully maps hazards, identifying near main channels, ravines, lower-elevation prone flooding. research provides robust framework assessment, contributing valuable knowledge fields management disaster mitigation. It underscores continuous monitoring updating accommodate changing use, climate, hydrological conditions. innovative application offers crucial insights urban planners policymakers, emphasizing need proactive strategies flood-prone serving as model similar regions. Doi: 10.28991/CEJ-2024-010-05-05 Full Text: PDF

Language: Английский

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Multi-Criteria Assessment of Flood Risk on Railroads Using a Machine Learning Approach: A Case Study of Railroads in Minas Gerais

Infrastructures, Journal Year: 2025, Volume and Issue: 10(1), P. 12 - 12

Published: Jan. 8, 2025

In a climate change scenario where extreme precipitation events occur more frequently and intensely, risk assessment plays critical role in ensuring the safety operational efficiency of facilities. This case study uses combination multi-criteria analysis approach hydrological studies that use machine learning algorithms to simulate new rainfall order estimate flooding on railroads. Risk variables, including terrain, drainage capability, accumulated flow, land cover, will be weighed using multicriteria approach. A methodical evaluation most vulnerable locations railroad network possible thanks these parameters based geographic information system (GIS) meantime, historical precipitation, balance data used calibrate validate models. The database required for model can created with data. research regions are situated densely rail-networked state Minas Gerais. geographical climatic diversity Gerais makes it perfect place test suggested approaches. models evaluated included linear regression, random forest, decision tree, support vector machines. Among models, Linear Regression emerged as best-performing an R2 value 0.999998, mean squared error (MSE) 0.018672, low tendency overfitting (0.000011).

Language: Английский

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Multi-hazards (landslides, floods, and gully erosion) modeling and mapping using machine learning algorithms

Journal of African Earth Sciences, Journal Year: 2022, Volume and Issue: 197, P. 104788 - 104788

Published: Nov. 9, 2022

Language: Английский

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Satellite image classification using deep learning approach

Earth Science Informatics, Journal Year: 2024, Volume and Issue: 17(3), P. 2495 - 2508

Published: April 3, 2024

Language: Английский

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