Managing natural disasters: An analysis of technological advancements, opportunities, and challenges

Internet of Things and Cyber-Physical Systems, Journal Year: 2023, Volume and Issue: 4, P. 99 - 109

Published: Sept. 30, 2023

Natural disasters (NDs) have always been a major threat to human lives and infrastructure, causing immense damage loss. In recent years, the increasing frequency severity of natural highlighted need for more effective efficient disaster management strategies. this context, use technology has emerged as promising solution. survey paper, we explore employment technologies in order relieve impacts various disasters. We provide an overview how different such Remote Sensing, Radars Satellite Imaging, internet-of-things (IoT), Smartphones, Social Media can be utilized NDs. By utilizing these technologies, predict, respond, recover from NDs effectively, potentially saving minimizing infrastructure damage. The paper also highlights potential benefits, limitations, challenges associated with implementation purposes. While significantly improve NDM, there are that addressed, cost specialized knowledge skills. Overall, provides comprehensive managing sheds light on important role play NDM. exploring applications aims contribute development sustainable

Language: Английский

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A comparative analysis of feature selection models for spatial analysis of floods using hybrid metaheuristic and machine learning models

Environmental Science and Pollution Research, Journal Year: 2024, Volume and Issue: 31(23), P. 33495 - 33514

Published: April 29, 2024

Language: Английский

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

Environmental Science and Pollution Research, Journal Year: 2024, Volume and Issue: 31(35), P. 48497 - 48522

Published: July 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.

Language: Английский

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Optimization of SVR and CatBoost models using metaheuristic algorithms to assess landslide susceptibility

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Oct. 22, 2024

In this study, a landslide susceptibility assessment is performed by combining two machine learning regression algorithms (MLRA), such as support vector (SVR) and categorical boosting (CatBoost), with population-based optimization algorithms, grey wolf optimizer (GWO) particle swarm (PSO), to evaluate the potential of relatively new algorithm impact that can have on performance models. The Kerala state in India has been chosen test site due large number recorded incidents recent past. study started 18 predisposing factors, which were reduced 14 after multi-approach feature selection technique. Six models implemented compared using alone each them algorithms: SVR, CatBoost, SVR-PSO, CatBoost-PSO, SVR-GWO, CatBoost-GWO. resulting maps validated an independent dataset. rankings, based area under receiver operating characteristic curve (AUC) metric, are follows: CatBoost-GWO (AUC = 0.910) had highest performance, followed CatBoost-PSO 0.909), CatBoost 0.899), SVR-GWO 0.868), SVR-PSO 0.858), SVR 0.840). Other validation statistics corroborated these outcomes, Friedman Wilcoxon-signed rank tests verified statistical significance Our case showed outperformed both optimized or not; introduction significantly improves results models, GWO being slightly more effective than PSO. However, cannot drastically alter model, highlighting importance setting up rigorous model since early steps any research.

Language: Английский

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Metaheuristic-driven enhancement of categorical boosting algorithm for flood-prone areas mapping

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2025, Volume and Issue: 136, P. 104357 - 104357

Published: Jan. 14, 2025

Language: Английский

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Flash Flood Susceptibility Mapping of North-east Depression of Bangladesh using Different GIS based Bivariate Statistical Models

Watershed Ecology and the Environment, Journal Year: 2024, Volume and Issue: 6, P. 26 - 40

Published: Jan. 1, 2024

Flash flood causes severe damage to the environment and human life across world, no exception is Bangladesh. Severe flash floods affect northeastern portion of Bangladesh in early monsoon pose a serious threat every aspect socioeconomic development environmental sustainability. To manage reduce loss, map susceptible zones plays key role. Thus, aim this research flood-susceptible areas haor utilizing GIS-based bivariate statistical models. The models utilized are frequency ratio (FR), weights evidence (WoE), certainty factor (CF), Shanon's entropy (SE) information value (IV). Among 250 identified locations, 80% data was used for training purposes 20% testing purposes. Eleven selected conditioning factors include elevation, slope, aspect, curvature, TWI, TRI, SPI, distance stream, stream density, rainfall physiography. calculated assigned using ArcGIS prepare final maps. Results AUC ROC indicate WoE (success rate = 0.833 prediction =0.925) best model susceptibility mapping followed by FR 0.828 =0.928) SE 0.827 =0.923). According models, topographic (flat area) hydrologic significantly control occurrence study area. prepared maps will be helpful disaster managers master planners

Language: Английский

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Flood susceptibility mapping leveraging open-source remote-sensing data and machine learning approaches in Nam Ngum River Basin (NNRB), Lao PDR

Geomatics Natural Hazards and Risk, Journal Year: 2024, Volume and Issue: 15(1)

Published: May 28, 2024

Frequent floods caused by monsoons and rainstorms have significantly affected the resilience of human natural ecosystems in Nam Ngum River Basin, Lao PDR. A cost-efficient framework integrating advanced remote sensing machine learning techniques is proposed to address this issue enhancing flood susceptibility understanding informed decision-making. This study utilizes geo-datasets algorithms (Random Forest, Support Vector Machine, Artificial Neural Networks, Long Short-Term Memory) generate comprehensive maps. The results highlight Random Forest's superior performance, achieving highest train test Area Under Curve Receiver Operating Characteristic (AUROC) (1.00 0.993), accuracy (0.957), F1-score (0.962), kappa value (0.914), with lowest mean squared error (0.207) Root Mean Squared Error (0.043). Vulnerability particularly pronounced low-elevation low-slope southern downstream areas (Central part PDR). reveal that 36%–53% basin's total area highly susceptible flooding, emphasizing dire need for coordinated floodplain management strategies. research uses freely accessible data, addresses data scarcity studies, provides valuable insights disaster risk sustainable planning

Language: Английский

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Enhancing the Performance of Machine Learning and Deep Learning-Based Flood Susceptibility Models by Integrating Grey Wolf Optimizer (GWO) Algorithm

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(14), P. 2595 - 2595

Published: July 16, 2024

Flooding is a recurrent hazard occurring worldwide, resulting in severe losses. The preparation of flood susceptibility map non-structural approach to management before its occurrence. With recent advances artificial intelligence, achieving high-accuracy model for mapping (FSM) challenging. Therefore, this study, various intelligence approaches have been utilized achieve optimal accuracy modeling address challenge. By incorporating the grey wolf optimizer (GWO) metaheuristic algorithm into models—including neural networks (RNNs), support vector regression (SVR), and extreme gradient boosting (XGBoost)—the objective generate maps evaluate variation performance. tropical Manimala River Basin India, severely battered by flooding past, has selected as test site. This 15 conditioning factors such aspect, enhanced built-up bareness index (EBBI), slope, elevation, geomorphology, normalized difference water (NDWI), plan curvature, profile soil adjusted vegetation (SAVI), stream density, texture, power (SPI), terrain ruggedness (TRI), land use/land cover (LULC) topographic wetness (TWI). Thus, six are produced applying RNN, SVR, XGBoost, RNN-GWO, SVR-GWO, XGBoost-GWO models. All models exhibited outstanding (AUC above 0.90) performance, performance ranks following order: RNN-GWO (AUC: 0.968) > 0.961) SVR-GWO 0.960) RNN 0.956) XGBoost 0.953) SVR 0.948). It was discovered that hybrid GWO optimization improved three RNN-GWO-based shows 8.05% MRB very susceptible floods. found SPI, LULC, TWI top five influential factors.

Language: Английский

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Extreme flash flood susceptibility mapping using a novel PCA-based model stacking approach

Advances in Space Research, Journal Year: 2024, Volume and Issue: 74(11), P. 5371 - 5382

Published: Aug. 6, 2024

Language: Английский

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Deep Learning Methods of Satellite Image Processing for Monitoring of Flood Dynamics in the Ganges Delta, Bangladesh

Water, Journal Year: 2024, Volume and Issue: 16(8), P. 1141 - 1141

Published: April 17, 2024

Mapping spatial data is essential for the monitoring of flooded areas, prognosis hazards and prevention flood risks. The Ganges River Delta, Bangladesh, world’s largest river delta prone to floods that impact social–natural systems through losses lives damage infrastructure landscapes. Millions people living in this region are vulnerable repetitive due exposure, high susceptibility low resilience. Cumulative effects monsoon climate, rainfall, tropical cyclones hydrogeologic setting Delta increase probability floods. While engineering methods mitigation include practical solutions (technical construction dams, bridges hydraulic drains), regulation traffic land planning support systems, geoinformation rely on modelling remote sensing (RS) evaluate dynamics hazards. Geoinformation indispensable mapping catchments areas visualization affected regions real-time monitoring, addition implementing developing emergency plans vulnerability assessment warning supported by RS data. In regard, study used monitor southern segment Delta. Multispectral Landsat 8-9 OLI/TIRS satellite images were evaluated (March) post-flood (November) periods analysis extent landscape changes. Deep Learning (DL) algorithms GRASS GIS modules qualitative quantitative as advanced image processing. results constitute a series maps based classified

Language: Английский

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