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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Developing a machine learning-based predictive model for cesium sorption distribution coefficient on crushed granite

Journal of Environmental Radioactivity, Journal Year: 2025, Volume and Issue: 283, P. 107628 - 107628

Published: Feb. 4, 2025

Language: Английский

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Spatial Analysis of Flood Hazard Zoning Map Using Novel Hybrid Machine Learning Technique in Assam, India

Remote Sensing, Journal Year: 2022, Volume and Issue: 14(24), P. 6229 - 6229

Published: Dec. 8, 2022

Twenty-two flood-causative factors were nominated based on morphometric, hydrological, soil permeability, terrain distribution, and anthropogenic inferences further analyzed through the novel hybrid machine learning approach of random forest, support vector machine, gradient boosting, naïve Bayes, decision tree (ML) models. A total 400 flood nonflood locations acted as target variables hazard zoning map. All operative in this study tested using variance inflation factor (VIF) values (<5.0) Boruta feature ranking (<10 ranks) for FHZ maps. The model along with RF GBM had sound maps area. area under receiver operating characteristics (AUROC) curve statistical matrices such accuracy, precision, recall, F1 score, gain lift applied to assess performance. 70%:30% sample ratio training validation standalone models concerning AUROC value showed results all ML models, (97%), SVM (91%), NB (96%), DT (88%), (97%). also suitability RF, GBM, developing

Language: Английский

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Spatial modeling of flood hazard using machine learning and GIS in Ha Tinh province, Vietnam

Journal of Water and Climate Change, Journal Year: 2022, Volume and Issue: 14(1), P. 200 - 222

Published: Dec. 19, 2022

Abstract The objective of this study was the development an approach based on machine learning and GIS, namely Adaptive Neuro-Fuzzy Inference System (ANFIS), Gradient-Based Optimizer (GBO), Chaos Game Optimization (CGO), Sine Cosine Algorithm (SCA), Grey Wolf (GWO), Differential Evolution (DE) to construct flood susceptibility maps in Ha Tinh province Vietnam. database includes 13 conditioning factors 1,843 locations, which were split by a ratio 70/30 between those used build validate model, respectively. Various statistical indices, root mean square error (RMSE), area under curve (AUC), absolute (MAE), accuracy, R1 score, applied models. results show that all proposed models performed well, with AUC value more than 0.95. Of models, ANFIS-GBO most accurate, 0.96. Analysis shows approximately 32–38% is located high very zone. successful performance over large-scale can help local authorities decision-makers develop policies strategies reduce threats related flooding future.

Language: Английский

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Local-scale flash flood susceptibility assessment in northeastern Bangladesh using machine learning algorithms

Environmental Challenges, Journal Year: 2023, Volume and Issue: 14, P. 100833 - 100833

Published: Dec. 30, 2023

The recurring flash floods in northeastern Bangladesh have been causing significant damage to the lives and livelihoods of local people every year, underscoring necessity effective flood management mitigation efforts. Identifying hazard areas is very first step achieving such ends. While machine learning algorithms widely used susceptibility studies, there a dearth research addressing their application for local-scale assessment Bangladesh. Besides, most only statistical metrics are evaluate prediction capability algorithms, overlooking ability account spatial consistency. Therefore, this study attempts performance Random Forest (RF) Support Vector Machine (SVM) at scale using metrics. RF model performed better than SVM with area under curve (0.964), accuracy (92.1%) kappa (0.894). Five zones were identified natural breaks method: high, moderate, low low. overall agreement between maps was 73.3%. For susceptible classes, highest RF-SVM (97.15%) SVM-RF (81.54%). findings can be useful more accurate similar settings. Moreover, prepared map helpful taking sustainable measures mitigate devastating effects

Language: Английский

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A Systematic Literature Review on Classification Machine Learning for Urban Flood Hazard Mapping

Water Resources Management, Journal Year: 2024, Volume and Issue: 38(15), P. 5823 - 5864

Published: Aug. 3, 2024

Language: Английский

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Assessment of data intelligence algorithms in modeling daily reference evapotranspiration under input data limitation scenarios in semi-arid climatic condition

Water Science & Technology, Journal Year: 2023, Volume and Issue: 87(10), P. 2504 - 2528

Published: May 4, 2023

Abstract Crop evapotranspiration is essential for planning and designing an efficient irrigation system. The present investigation assessed the capability of four machine learning algorithms, namely, XGBoost linear regression (XGBoost Linear), Ensemble Tree, Polynomial Regression (Polynomial Regr), Isotonic (Isotonic Regr) in modeling daily reference (ETo) at IARI, New Delhi. models were developed considering full limited dataset scenarios. efficacy constructed was against Penman–Monteith (PM56) model estimated ETo. Results revealed under conditions, Tree gave best results ETo during training period, while testing period scenarios S1(Tmax) S2 (Tmax, Tmin), Regr yielded superior over other models. In addition, outperformed others rest input data algorithms reported values correlation coefficient (r), mean absolute error (MAE), square (MSE), root (RMSE), percentage (MAPE). Thus, we recommend applying algorithm precisely semi-arid climatic conditions.

Language: Английский

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Flood risk decomposed: optimized machine learning hazard mapping and multi-criteria vulnerability analysis in the city of Zaio, Morocco.

Journal of African Earth Sciences, Journal Year: 2024, Volume and Issue: unknown, P. 105431 - 105431

Published: Sept. 1, 2024

Language: Английский

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A flash flood susceptibility prediction and partitioning method based on GeoDetector and random forest

Stochastic Environmental Research and Risk Assessment, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 8, 2025

Language: Английский

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Predicting flood risks using advanced machine learning algorithms with a focus on Bangladesh: influencing factors, gaps and future challenges

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(3)

Published: Feb. 27, 2025

Language: Английский

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