Flood susceptible prediction through the use of geospatial variables and machine learning methods

Journal of Hydrology, Год журнала: 2023, Номер 617, С. 129121 - 129121

Опубликована: Янв. 13, 2023

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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A hybrid of ensemble machine learning models with RFE and Boruta wrapper-based algorithms for flash flood susceptibility assessment

International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2023, Номер 122, С. 103401 - 103401

Опубликована: Июль 14, 2023

Flash floods are among the world most destructive natural disasters, and developing optimum hybrid Machine Learning (ML) models for flash flood susceptibility (FFS) modeling remains a challenge. This study proposed novel intelligence algorithms based on of several ensemble ML (i.e., Bagged Flexible Discriminant Analysis (BAFDA), Extreme Gradient Boosting (XBG), Rotation Forest (ROF) Boosted Generalized Additive Model (BGAM)) wrapper-based factor optimization Recursive Feature Elimination (RFE) Boruta) to improve accuracy FFS mapping at Neka-Haraz watershed in Iran. In addition, Random Search (RS) method is meta-optimization developed hyper-parameters. considers 20 conditioning factors (CgFs) 380 non-flood locations create geospatial database. The performance each model was evaluated by area under receiver operating characteristic (ROC) curve (AUC) validation methods, such as efficiency. demonstrated good performance, with BGAM-Boruta achieving highest (AUC = 0.953, Efficiency 0.910), followed ROF-Boruta 0.952), ROF-RFE 0.951), BAFDA-Boruta 0.950), BGAM-RFE ROF 0.949), BGAM 0.948), BAFDA-RFE 0.943), XGB-Boruta BAFDA 0.939), XGB-RFE 0.938) XGB 0.911). model, regional coverage about 46% high very areas. Moreover, revealed that distance river, slope, rainfall, altitude, road CgFs significant this region.

Язык: Английский

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A novel machine learning tool for current and future flood susceptibility mapping by integrating remote sensing and geographic information systems

Journal of Hydrology, Год журнала: 2024, Номер 632, С. 130936 - 130936

Опубликована: Фев. 27, 2024

Язык: Английский

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Flood Susceptibility Mapping Using SAR Data and Machine Learning Algorithms in a Small Watershed in Northwestern Morocco

Remote Sensing, Год журнала: 2024, Номер 16(5), С. 858 - 858

Опубликована: Фев. 29, 2024

Flood susceptibility mapping plays a crucial role in flood risk assessment and management. Accurate identification of areas prone to flooding is essential for implementing effective mitigation measures informing decision-making processes. In this regard, the present study used high-resolution remote sensing products, i.e., synthetic aperture radar (SAR) images inventory preparation integrated four machine learning models (Random Forest: RF, Classification Regression Trees: CART, Support Vector Machine: SVM, Extreme Gradient Boosting: XGBoost) predict Metlili watershed, Morocco. Initially, 12 independent variables (elevation, slope angle, aspect, plan curvature, topographic wetness index, stream power distance from streams, roads, lithology, rainfall, land use/land cover, normalized vegetation index) were as conditioning factors. The dataset was divided into 70% 30% training validation purposes using popular library, scikit-learn (i.e., train_test_split) Python programming language. Additionally, area under curve (AUC) evaluate performance models. accuracy results showed that XGBoost predicted with AUC values 0.807, 0.780, 0.756, 0.727, respectively. However, RF model performed better at prediction compared other applied. As per model, 22.49%, 16.02%, 12.67%, 18.10%, 31.70% watershed are estimated being very low, moderate, high, highly susceptible flooding, Therefore, integration data could have promising predicting similar environments.

Язык: Английский

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Flood risk evaluation of the coastal city by the EWM-TOPSIS and machine learning hybrid method

International Journal of Disaster Risk Reduction, Год журнала: 2024, Номер 106, С. 104435 - 104435

Опубликована: Март 28, 2024

Язык: Английский

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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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Impacts of straw return methods on crop yield, soil organic matter, and salinity in saline-alkali land in North China

Field Crops Research, Год журнала: 2025, Номер 322, С. 109752 - 109752

Опубликована: Янв. 16, 2025

Язык: Английский

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Integrating machine learning regression and classification for enhanced interpretability in optimizing the Fenton process for real wastewater treatment conditions

Separation and Purification Technology, Год журнала: 2025, Номер unknown, С. 132182 - 132182

Опубликована: Фев. 1, 2025

Язык: Английский

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Forest Fire Susceptibility Prediction Based on Machine Learning Models with Resampling Algorithms on Remote Sensing Data

Remote Sensing, Год журнала: 2020, Номер 12(22), С. 3682 - 3682

Опубликована: Ноя. 10, 2020

This study predicts forest fire susceptibility in Chaloos Rood watershed Iran using three machine learning (ML) models—multivariate adaptive regression splines (MARS), support vector (SVM), and boosted tree (BRT). The utilizes 14 set of predictors derived from vegetation indices, climatic variables, environmental factors, topographical features. To assess the suitability models estimating variance bias estimation, training dataset obtained Natural Resources Directorate Mazandaran province was subjected to resampling cross validation (CV), bootstrap, optimism bootstrap techniques. Using inflation factor (VIF), weight indicating strength spatial relationship occurrence assigned each contributing variable. Subsequently, were trained validated receiver operating characteristics (ROC) area under curve (AUC) curve. Results model based on techniques (non, 5- 10-fold CV, bootstrap) produced AUC values 0.78, 0.88, 0.90, 0.86 0.83 for MARS model; 0.82, 0.89, 0.87, 0.84 SVM 0.91 BRT model. Across individual model, CV performed best with 0.90 0.89. Overall, outperformed other all ramification highest value algorithm. Generally, process enhanced prediction performance models.

Язык: Английский

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