Using artificial intelligence and data fusion for environmental monitoring: A review and future perspectives

Information Fusion, Journal Year: 2022, Volume and Issue: 86-87, P. 44 - 75

Published: June 25, 2022

Language: Английский

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A Comprehensive Assessment of XGBoost Algorithm for Landslide Susceptibility Mapping in the Upper Basin of Ataturk Dam, Turkey

Applied Sciences, Journal Year: 2021, Volume and Issue: 11(11), P. 4993 - 4993

Published: May 28, 2021

The success rate in landslide susceptibility mapping efforts increased with the advancements machine learning algorithms and availability of geospatial data high spatial temporal resolutions. Existing data-driven models are not globally applicable due to variability conditioning parameters limitations up-to-date accurate data. Among numerous applications, maps essential for site selection health monitoring engineering structures, such as dams, increasing their lifetime prevent from disastrous events caused by damages. In this study, performance XGBoost algorithm was evaluated a landslide-prone area upper basin Ataturk Dam, which is prime investment located southeast Turkey. study has size 2718.7 km2 an elevation difference ca. 2000 m contains 27 lithological units. EU-DEM v1.1 Copernicus Programme used derive geomorphological features. High classification accuracy under curve value 0.96 could be obtained algorithm. According results, main factors controlling landslides lithology, altitude topographic wetness index.

Language: Английский

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Comparison of Machine Learning Algorithms for Flood Susceptibility Mapping

Remote Sensing, Journal Year: 2022, Volume and Issue: 15(1), P. 192 - 192

Published: Dec. 29, 2022

Floods are one of the most destructive natural disasters, causing financial and human losses every year. As a result, reliable Flood Susceptibility Mapping (FSM) is required for effective flood management reducing its harmful effects. In this study, new machine learning model based on Cascade Forest Model (CFM) was developed FSM. Satellite imagery, historical reports, field data were used to determine flood-inundated areas. The database included 21 flood-conditioning factors obtained from different sources. performance proposed CFM evaluated over two study areas, results compared with those other six methods, including Support Vector Machine (SVM), Decision Tree (DT), Random (RF), Deep Neural Network (DNN), Light Gradient Boosting (LightGBM), Extreme (XGBoost), Categorical (CatBoost). result showed produced highest accuracy models both Overall Accuracy (AC), Kappa Coefficient (KC), Area Under Receiver Operating Characteristic Curve (AUC) more than 95%, 0.8, 0.95, respectively. Most these recognized southwestern part Karun basin, northern northwestern regions Gorganrud basin as susceptible

Language: Английский

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Forest fire susceptibility mapping with sensitivity and uncertainty analysis using machine learning and deep learning algorithms

Advances in Space Research, Journal Year: 2023, Volume and Issue: 72(2), P. 426 - 443

Published: March 21, 2023

Language: Английский

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Machine learning models for gully erosion susceptibility assessment in the Tensift catchment, Haouz Plain, Morocco for sustainable development

Journal of African Earth Sciences, Journal Year: 2024, Volume and Issue: 213, P. 105229 - 105229

Published: March 11, 2024

Gully erosion is a widespread environmental danger, threatening global socio-economic stability and sustainable development. This study comprehensively applied seven machine learning (ML) models including SVM, KNN, RF, XGBoost, ANN, DT, LR, evaluated gully susceptibility in the Tensift catchment predict it within Haouz plain, Morocco. To ensure reliability of findings, employed robust combination inventory, sentinel images, Digital Surface Model. Eighteen predictors, encompassing topographical, geomorphological, environmental, hydrological factors, were selected after multicollinearity analyses. The revealed that approximately 28.18% at very high risk erosion. Furthermore, 15.13% 31.28% are categorized as low respectively. These findings extend to where 7.84% surface area highly risking erosion, while 18.25% 55.18% characterized areas. gauge performance ML models, an array metrics specificity, precision, sensitivity, accuracy employed. highlights XGBoost KNN most promising achieving AUC ROC values 0.96 0.93 test phase. remaining namely RF (AUC = 0.89), LR 0.80), SVM 0.81), DT 0.86), ANN 0.78), also displayed commendable performance. novelty this research its innovative approach combat through cutting edge offering practical solutions for watershed conservation, management, prevention land degradation. insights invaluable addressing challenges posed by region, beyond geographical boundaries can be used defining appropriate mitigation strategies local national scale.

Language: Английский

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Machine learning algorithm-based risk assessment of riparian wetlands in Padma River Basin of Northwest Bangladesh

Environmental Science and Pollution Research, Journal Year: 2021, Volume and Issue: 28(26), P. 34450 - 34471

Published: March 2, 2021

Language: Английский

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Landslide susceptibility mapping using artificial neural network tuned by metaheuristic algorithms

Environmental Earth Sciences, Journal Year: 2021, Volume and Issue: 80(24)

Published: Nov. 27, 2021

Language: Английский

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Assessment Analysis of Flood Susceptibility in Tropical Desert Area: A Case Study of Yemen

Remote Sensing, Journal Year: 2022, Volume and Issue: 14(16), P. 4050 - 4050

Published: Aug. 19, 2022

Flooding is one of the catastrophic natural hazards worldwide that can easily cause devastating effects on human life and property. Remote sensing devices are becoming increasingly important in monitoring assessing disaster susceptibility hazards. The proposed research work pursues an assessment analysis flood a tropical desert environment: case study Yemen. base data for this were collected organized from meteorological, satellite images, remote data, essential geographic various sources used as input into four machine learning (ML) algorithms. In study, RS (Sentinel-1 images) to detect flooded areas area. We also Sentinel application platform (SNAP 7.0) Sentinel-1 image detecting zones locations. Flood spots discovered verified using Google Earth Landsat press create inventory map Four ML algorithms flash (FFS) Tarim city (Yemen): K-nearest neighbor (KNN), Naïve Bayes (NB), random forests (RF), eXtreme gradient boosting (XGBoost). Twelve conditioning factors prepared, assessed multicollinearity, with inventories parameters run each model. A total 600 non-flood points chosen, where 75% 25% training validation datasets. confusion matrix area under receiver operating characteristic curve (AUROC) validate maps. results obtained reveal all models had high capacity predict floods (AUC > 0.90). Further, terms performance, tree-based ensemble (RF, XGBoost) outperform other algorithms, RF algorithm provides robust performance = 0.982) flood-prone only few adjustments required prior value lies fact being tested first time Yemen assess susceptibility, which be assess, example, earthquakes, landslides, disasters. Furthermore, makes significant contributions effort reduce risk disasters, particularly This will, therefore, help enhance environmental sustainability.

Language: Английский

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SBAS-InSAR based validated landslide susceptibility mapping along the Karakoram Highway: a case study of Gilgit-Baltistan, Pakistan

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: Feb. 27, 2023

Geological settings of the Karakoram Highway (KKH) increase risk natural disasters, threatening its regular operations. Predicting landslides along KKH is challenging due to limitations in techniques, a environment, and data availability issues. This study uses machine learning (ML) models landslide inventory evaluate relationship between events their causative factors. For this, Extreme Gradient Boosting (XGBoost), Random Forest (RF), Artificial Neural Network (ANN), Naive Bayes (NB), K Nearest Neighbor (KNN) were used. A total 303 points used create an inventory, with 70% for training 30% testing. Susceptibility mapping Fourteen The area under curve (AUC) receiver operating characteristic (ROC) employed compare accuracy models. deformation generated susceptible regions was evaluated using SBAS-InSAR (Small-Baseline subset-Interferometric Synthetic Aperture Radar) technique. sensitive showed elevated line-of-sight (LOS) velocity. XGBoost technique produces superior Landslide map (LSM) region integration findings. improved LSM offers predictive modeling disaster mitigation gives theoretical direction management KKH.

Language: Английский

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Snow avalanche susceptibility mapping using novel tree-based machine learning algorithms (XGBoost, NGBoost, and LightGBM) with eXplainable Artificial Intelligence (XAI) approach

Stochastic Environmental Research and Risk Assessment, Journal Year: 2023, Volume and Issue: 37(6), P. 2243 - 2270

Published: March 13, 2023

Language: Английский

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