Flooding and its relationship with land cover change, population growth, and road density

Geoscience Frontiers, Год журнала: 2021, Номер 12(6), С. 101224 - 101224

Опубликована: Май 5, 2021

Bangladesh experiences frequent hydro-climatic disasters such as flooding. These are believed to be associated with land use changes and climate variability. However, identifying the factors that lead flooding is challenging. This study mapped flood susceptibility in northeast region of using Bayesian regularization back propagation (BRBP) neural network, classification regression trees (CART), a statistical model (STM) evidence belief function (EBF), their ensemble models (EMs) for three time periods (2000, 2014, 2017). The accuracy machine learning algorithms (MLAs), STM, EMs were assessed by considering area under curve—receiver operating characteristic (AUC-ROC). Evaluation levels aforementioned revealed EM4 (BRBP-CART-EBF) outperformed (AUC > 90%) standalone other analyzed. Furthermore, this investigated relationships among cover change (LCC), population growth (PG), road density (RD), relative (RCF) areas period between 2000 2017. results showed very high increased 19.72% 2017, while PG rate 51.68% over same period. Pearson correlation coefficient RCF RD was calculated 0.496. findings highlight significant association floods causative factors. could valuable policymakers resource managers they can improvements management reduction damage risks.

Язык: Английский

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Flood Susceptibility Assessment Using Novel Ensemble of Hyperpipes and Support Vector Regression Algorithms

Water, Год журнала: 2021, Номер 13(2), С. 241 - 241

Опубликована: Янв. 19, 2021

Recurrent floods are one of the major global threats among people, particularly in developing countries like India, as this nation has a tropical monsoon type climate. Therefore, flood susceptibility (FS) mapping is indeed necessary to overcome natural hazard phenomena. With mind, we evaluated prediction performance FS Koiya River basin, Eastern India. The present research work was done through preparation sophisticated inventory map; eight conditioning variables were selected based on topography and hydro-climatological condition, by applying novel ensemble approach hyperpipes (HP) support vector regression (SVR) machine learning (ML) algorithms. HP-SVR also compared with stand-alone ML algorithms HP SVR. In relative importance variables, distance river most dominant factor for occurrences followed rainfall, land use cover (LULC), normalized difference vegetation index (NDVI). validation accuracy assessment maps five popular statistical methods. result evaluation showed that optimal model (AUC = 0.915, sensitivity 0.932, specificity 0.902, 0.928 Kappa 0.835) assessment, 0.885) SVR 0.871).

Язык: Английский

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Novel ensemble machine learning models in flood susceptibility mapping

Geocarto International, Год журнала: 2021, Номер 37(16), С. 4571 - 4593

Опубликована: Фев. 19, 2021

The research aims to propose the new ensemble models by combining machine learning techniques, such as rotation forest (RF), nearest shrunken centroids (NSC), k-nearest neighbour (KNN), boosted regression tree (BRT), and logitboost (LB) with base classifier adabag (AB) for flood susceptibility mapping (FSM). proposed were implemented in central west coast of India, which is vulnerable events. For inventory mapping, a total 210 localities identified. Twelve effective factors selected using boruta algorithm FSM. area under receiver operating characteristics (AUROC) curve other statistical measures (sensitivity, specificity, accuracy, kappa, root mean square error (RMSE), absolute (MAE)) employed estimate compare success rate approaches. validation results individual terms AUC value AB (92.74%) >RF (91.50%) >BRT (90.75%) >LB (89.07%) >NSC (88.97%) >KNN (83.88%), whereas showed that AB-RF (94%) was highest prediction efficiency followed by, AB-KNN (93.33%), AB-NSC (93.02%), AB-LB (92.83%), AB-BRT (92.64%). outcomes established more appropriate increase accuracy different single models. Therefore, this study can be useful proper planning management hazard alike geographic environment.

Язык: Английский

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A novel hybrid of meta-optimization approach for flash flood-susceptibility assessment in a monsoon-dominated watershed, Eastern India

Environmental Earth Sciences, Год журнала: 2022, Номер 81(5)

Опубликована: Фев. 21, 2022

Язык: 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, Год журнала: 2022, Номер 609, С. 127747 - 127747

Опубликована: Март 24, 2022

Язык: Английский

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Enhancing flood risk assessment through integration of ensemble learning approaches and physical-based hydrological modeling

Geomatics Natural Hazards and Risk, Год журнала: 2023, Номер 14(1)

Опубликована: Май 4, 2023

This study aims to examine three machine learning (ML) techniques, namely random forest (RF), LightGBM, and CatBoost for flooding susceptibility maps (FSMs) in the Vietnamese Vu Gia-Thu Bon (VGTB). The results of ML are compared with those rainfall-runoff model, different training dataset sizes utilized performance assessment. Ten independent factors assessed. An inventory map approximately 850 sites is based on several post-flood surveys. randomly split between (70%) testing (30%). AUC-ROC 97.9%, 99.5%, 99.5% CatBoost, RF, respectively. FSMs developed by methods show good agreement terms an extension flood inundation using model. models' showed 10–13% total area be highly susceptible flooding, consistent RRI's map. that downstream areas (both urbanized agricultural) under high very levels susceptibility. Additionally, input datasets tested determine least number data points having acceptable reliability. demonstrate can realistically predict FSMs, regardless samples.

Язык: Английский

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Enhancing flood susceptibility modeling using multi-temporal SAR images, CHIRPS data, and hybrid machine learning algorithms

The Science of The Total Environment, Год журнала: 2023, Номер 871, С. 162066 - 162066

Опубликована: Фев. 10, 2023

Язык: 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, Год журнала: 2024, Номер 17(1)

Опубликована: Фев. 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.

Язык: 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, Год журнала: 2025, Номер 136, С. 104357 - 104357

Опубликована: Янв. 14, 2025

Язык: Английский

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Flood hazards susceptibility mapping using statistical, fuzzy logic, and MCDM methods

Soft Computing, Год журнала: 2021, Номер 25(14), С. 9325 - 9346

Опубликована: Май 26, 2021

Язык: Английский

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