Flood susceptibility mapping through geoinformatics and ensemble learning methods, with an emphasis on the AdaBoost-Decision Tree algorithm, in Mazandaran, Iran

Earth Science Informatics, Год журнала: 2024, Номер 17(2), С. 1433 - 1457

Опубликована: Янв. 15, 2024

Язык: Английский

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A 3D virtual geographic environment for flood representation towards risk communication

International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2024, Номер 128, С. 103757 - 103757

Опубликована: Март 21, 2024

Risk communication seeks to develop a shared understanding of disaster among stakeholders, thereby amplifying public awareness and empowering them respond more effectively emergencies. However, existing studies have overemphasized specialized numerical modelling, making the professional output challenging understand use by non-research stakeholders. In this context, article proposes 3D virtual geographic environment for flood representation towards risk communication, which integrates parallel computation, in pipeline. Finally, section Rhine River Bonn, Germany, is selected experiment analysis. The experimental results show that proposed approach capable modelling within few hours, speedup ratio reached 6.45. intuitive scene with city models beneficial promoting particularly helpful participants without direct experience floods its spatiotemporal process. It also can be embedded Geospatial Infrastructure Management Ecosystem (GeoIME) cloud application intelligent systems.

Язык: Английский

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Improving the model robustness of flood hazard mapping based on hyperparameter optimization of random forest

Expert Systems with Applications, Год журнала: 2023, Номер 241, С. 122682 - 122682

Опубликована: Ноя. 24, 2023

Язык: Английский

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A novel approach for flood hazard assessment using hybridized ensemble models and feature selection algorithms

International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2023, Номер 122, С. 103443 - 103443

Опубликована: Авг. 1, 2023

Identifying flood-prone regions is critical for effective management of flood hazards as floods are among the most devastating natural disasters globally. However, accurate modeling and prediction challenging due to their complexity. The current research has proposed a novel approach Flood Hazard (FH) using hybrid Machine Learning (ML) models that integrate ensemble ML with several Feature Selection (FS) algorithms. An optimum set Influential Factors (FIFs) was determined Simulated Annealing (SA) Information Gain (IG) FS employed include AdaboostM1 (ABM), Boosted Generalized Linear Model (BGLM), Stochastic Gradient Boosting (SGB) In addition, hyper-parameters were optimized random search (RS) method repeated cross-validation technique. trained inventory map FIFs obtained from spatial database. results verified SA IG algorithms detect 9 13 factors in FH assessment, respectively. Moreover, rainfall, distance river, altitude, lithology have greater impact than other Sardabroud watershed, Mazandaran Province, Iran. Several robust indicators, such area under curve (AUC) relative operating characteristic (ROC) curves statistical measurements, assess robustness models. SA-ABM model had highest AUC value (0.983), while IG-ABM, SA-BGLM, SA-SGB, IG-BGLM, IG-SGB lower values (0.952 0.973). Finally, classified 27% study having high hazard floods.

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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Enhanced machine learning models development for flash flood mapping using geospatial data

Euro-Mediterranean Journal for Environmental Integration, Год журнала: 2024, Номер 9(3), С. 1087 - 1107

Опубликована: Май 31, 2024

Язык: Английский

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Leveraging GIS-based AHP, remote sensing, and machine learning for susceptibility assessment of different flood types in peshawar, Pakistan

Journal of Environmental Management, Год журнала: 2024, Номер 371, С. 123094 - 123094

Опубликована: Ноя. 2, 2024

Язык: Английский

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Interpretable flash flood susceptibility mapping in Yarlung Tsangpo River Basin using H2O Auto-ML

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Янв. 11, 2025

Flash flood susceptibility mapping is essential for identifying areas prone to flooding events and aiding decision-makers in formulating effective prevention measures. This study aims evaluate the flash Yarlung Tsangpo River Basin (YTRB) using multiple machine learning (ML) models facilitated by H2O automated ML platform. The best-performing model was used generate a map, its interpretability analyzed Shapley Additive Explanations (SHAP) tree interpretation method. results revealed that top four models, including both single ensemble demonstrated high accuracy tests. map generated eXtreme Randomized Trees (XRT) showed 8.92%, 12.95%, 15.42%, 31.34%, 31.37% of area exhibited very high, moderate, low, low susceptibility, respectively, with approximately 74.9% historical floods occurring classified as moderate susceptibility. SHAP plot identified topographic factors primary drivers floods, importance analysis ranking most influential such descending order DEM, wetness index, position normalized difference vegetation average multi-year precipitation. demonstrates benefits interpretable learning, which can provide guidance mitigation.

Язык: Английский

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Flood susceptibility mapping in the Yom River Basin, Thailand: stacking ensemble learning using multi-year flood inventory data

Geocarto International, Год журнала: 2025, Номер 40(1)

Опубликована: Фев. 10, 2025

Язык: Английский

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Refined Landslide Susceptibility Mapping by Integrating the SHAP-CatBoost Model and InSAR Observations: A Case Study of Lishui, Southern China

Applied Sciences, Год журнала: 2023, Номер 13(23), С. 12817 - 12817

Опубликована: Ноя. 29, 2023

Landslide susceptibility mapping based on static influence factors often exhibits issues of low accuracy and classification errors. To enhance the mapping, this study proposes a refined approach that integrates categorical boosting (CatBoost) with small baseline subset interferometric synthetic-aperture radar (SBAS-InSAR) results, achieving more precise detailed mapping. We utilized optical remote sensing images, information value (IV) model, fourteen influencing (elevation, slope, aspect, roughness, profile curvature, plane lithology, distance to faults, land use type, normalized difference vegetation index (NDVI), topographic wetness (TWI), rivers, roads, annual precipitation) establish IV-CatBoost landslide method. Subsequently, Sentinel-1A ascending data from January 2021 March 2023 were derive deformation rates within city Lishui in southern region China. Based outcomes derived SBAS-InSAR, discernment matrix was formulated rectify inaccuracies partitioned regions, leading creation CatBoost integration (IVCI) model. In end, we interpretations alongside surface deformations obtained SBAS-InSAR cross-verify excellence IVCI. Research findings indicate distinct enhancement levels across 165,784 grids (149.20 km2) following correction. The enhanced classes spectral characteristics images closely correspond trends cumulative deformation, reflecting high level consistency field-based conditions. These improved classifications effectively refinement proposed paper enhances prediction accuracy, providing valuable technical reference for hazard prevention control region.

Язык: Английский

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