Flood Detection with SAR: A Review of Techniques and Datasets

Remote Sensing, Год журнала: 2024, Номер 16(4), С. 656 - 656

Опубликована: Фев. 10, 2024

Floods are among the most severe and impacting natural disasters. Their occurrence rate intensity have been significantly increasing worldwide in last years due to climate change urbanization, bringing unprecedented effects on human lives activities. Hence, providing a prompt response flooding events is of crucial relevance for humanitarian, social economic reasons. Satellite remote sensing using synthetic aperture radar (SAR) offers great deal support facing flood mitigating their global scale. As opposed multi-spectral sensors, SAR important advantages, as it enables Earth’s surface imaging regardless weather sunlight illumination conditions. In decade, availability data, even at no cost, thanks efforts international national space agencies, has deeply stimulating research activities every Earth observation field, including mapping monitoring, where advanced processing paradigms, e.g., fuzzy logic, machine learning, data fusion, applied, demonstrating superiority with respect traditional classification strategies. However, fair assessment performance reliability techniques key importance an efficient disasters and, hence, should be addressed carefully quantitative basis trough quality metrics high-quality reference data. To this end, recent development open datasets specifically covering related ground-truth can thorough objective validation well reproducibility results. Notwithstanding, SAR-based monitoring still suffers from limitations, especially vegetated urban areas, complex scattering mechanisms impair accurate extraction water regions. All such aspects, methodologies, datasets, strategies, challenges future perspectives described discussed.

Язык: Английский

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Investigating the Role of the Key Conditioning Factors in Flood Susceptibility Mapping Through Machine Learning Approaches

Earth Systems and Environment, Год журнала: 2024, Номер 8(1), С. 63 - 81

Опубликована: Янв. 1, 2024

Abstract This study harnessed the formidable predictive capabilities of three state-of-the-art machine learning models—extreme gradient boosting (XGB), random forest (RF), and CatBoost (CB)—applying them to meticulously curated datasets topographical, geological, environmental parameters; goal was investigate intricacies flood susceptibility within arid riverbeds Wilayat As-Suwayq, which is situated in Sultanate Oman. The results underscored exceptional discrimination prowess XGB CB, boasting impressive area under curve (AUC) scores 0.98 0.91, respectively, during testing phase. RF, a stalwart contender, performed commendably with an AUC 0.90. Notably, investigation revealed that certain key variables, including curvature, elevation, slope, stream power index (SPI), topographic wetness (TWI), roughness (TRI), normalised difference vegetation (NDVI), were critical achieving accurate delineation flood-prone locales. In contrast, ancillary factors, such as annual precipitation, drainage density, proximity transportation networks, soil composition, geological attributes, though non-negligible, exerted relatively lesser influence on susceptibility. empirical validation further corroborated by robust consensus XGB, RF CB models. By amalgamating advanced deep techniques precision geographical information systems (GIS) rich troves remote-sensing data, can be seen pioneering endeavour realm analysis cartographic representation semiarid fluvial landscapes. findings advance our comprehension vulnerability dynamics provide indispensable insights for development proactive mitigation strategies regions are susceptible hydrological perils.

Язык: Английский

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Flood susceptibility mapping using AutoML and a deep learning framework with evolutionary algorithms for hyperparameter optimization

Applied Soft Computing, Год журнала: 2023, Номер 148, С. 110846 - 110846

Опубликована: Сен. 13, 2023

Язык: Английский

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Study on groundwater pollution and its human impact analysis using geospatial techniques in semi-urban of south India

Environmental Research, Год журнала: 2023, Номер 240, С. 117532 - 117532

Опубликована: Окт. 29, 2023

Язык: Английский

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Modelling on assessment of flood risk susceptibility at the Jia Bharali River basin in Eastern Himalayas by integrating multicollinearity tests and geospatial techniques

Modeling Earth Systems and Environment, Год журнала: 2023, Номер 10(2), С. 2393 - 2419

Опубликована: Дек. 16, 2023

Abstract Climate change and anthropogenic factors have exacerbated flood risks in many regions across the globe, including Himalayan foothill region India. The Jia Bharali River basin, situated this vulnerable area, frequently experiences high-magnitude floods, causing significant damage to environment local communities. Developing accurate reliable susceptibility models is crucial for effective prevention, management, adaptation strategies. In study, we aimed generate a comprehensive zone model catchment by integrating statistical methods with expert knowledge-based mathematical models. We applied four distinct models, Frequency Ratio model, Fuzzy Logic (FL) Multi-criteria Decision Making based Analytical Hierarchy Process evaluate of basin. results revealed that approximately one-third basin area fell within moderate very high flood-prone zones. contrast, over 50% was classified as low demonstrated strong performance, ROC-AUC scores exceeding 70% MAE, MSE, RMSE below 30%. FL AHP were recommended application among areas similar physiographic characteristics due their exceptional performance training datasets. This study offers insights policymakers, regional administrative authorities, environmentalists, engineers working region. By providing robust research enhances prevention efforts thereby serving vital climate strategy regions. findings also implications disaster risk reduction sustainable development areas, contributing global towards achieving United Nations' Sustainable Development Goals.

Язык: Английский

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Integrating machine learning and geospatial data analysis for comprehensive flood hazard assessment

Environmental Science and Pollution Research, Год журнала: 2024, Номер 31(35), С. 48497 - 48522

Опубликована: Июль 20, 2024

Flooding is a major natural hazard worldwide, causing catastrophic damage to communities and infrastructure. Due climate change exacerbating extreme weather events robust flood modeling crucial support disaster resilience adaptation. This study uses multi-sourced geospatial datasets develop an advanced machine learning framework for assessment in the Arambag region of West Bengal, India. The inventory was constructed through Sentinel-1 SAR analysis global databases. Fifteen conditioning factors related topography, land cover, soil, rainfall, proximity, demographics were incorporated. Rigorous training testing diverse models, including RF, AdaBoost, rFerns, XGB, DeepBoost, GBM, SDA, BAM, monmlp, MARS algorithms, undertaken categorical mapping. Model optimization achieved statistical feature selection techniques. Accuracy metrics model interpretability methods like SHAP Boruta implemented evaluate predictive performance. According area under receiver operating characteristic curve (AUC), prediction accuracy models performed around > 80%. RF achieves AUC 0.847 at resampling factor 5, indicating strong discriminative AdaBoost also consistently exhibits good ability, with values 0.839 10. indicated precipitation elevation as most significantly contributing area. Most pointed out southern portions highly susceptible areas. On average, from 17.2 18.6% hazards. In analysis, various nature-inspired algorithms identified selected input parameters assessment, i.e., elevation, precipitation, distance rivers, TWI, geomorphology, lithology, TRI, slope, soil type, curvature, NDVI, roads, gMIS. As per analyses, it found that rivers play roles decision-making process assessment. results majority building footprints (15.27%) are high very risk, followed by those low risk (43.80%), (24.30%), moderate (16.63%). Similarly, cropland affected flooding this categorized into five classes: (16.85%), (17.28%), (16.07%), (16.51%), (33.29%). However, interdisciplinary contributes towards hydraulic hydrological management.

Язык: Английский

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Improving flood-prone areas mapping using geospatial artificial intelligence (GeoAI): A non-parametric algorithm enhanced by math-based metaheuristic algorithms

Journal of Environmental Management, Год журнала: 2025, Номер 375, С. 124238 - 124238

Опубликована: Янв. 29, 2025

Язык: Английский

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Optimizing the sustainable performance of public buildings: A hybrid machine learning algorithm

Energy, Год журнала: 2025, Номер unknown, С. 135283 - 135283

Опубликована: Фев. 1, 2025

Язык: Английский

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A critical review of flood risk assessment in Kerala Post-2018: Methodological approaches, gaps, and future directions

Journal of Hydrology Regional Studies, Год журнала: 2025, Номер 58, С. 102262 - 102262

Опубликована: Март 5, 2025

Язык: Английский

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Leveraging machine learning algorithms for improved disaster preparedness and response through accurate weather pattern and natural disaster prediction

Frontiers in Environmental Science, Год журнала: 2023, Номер 11

Опубликована: Ноя. 2, 2023

Globally, communities and governments face growing challenges from an increase in natural disasters worsening weather extremes. Precision disaster preparation is crucial responding to these issues. The revolutionary influence that machine learning algorithms have strengthening catastrophe response systems thoroughly explored this paper. Beyond a basic summary, the findings of our study are striking demonstrate sophisticated powers forecasting variety patterns anticipating range catastrophes, including heat waves, droughts, floods, hurricanes, more. We get practical insights into complexities applications, which support enhanced effectiveness predictive models preparedness. paper not only explains theoretical foundations but also presents proof significant benefits provide. As result, results open door for governments, businesses, people make wise decisions. These accurate predictions catastrophes emerging may be used implement pre-emptive actions, eventually saving lives reducing severity damage.

Язык: Английский

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