Modelling of piping collapses and gully headcut landforms: Evaluating topographic variables from different types of DEM DOI Creative Commons
Alireza Arabameri, Fatemeh Rezaie, Subodh Chandra Pal

et al.

Geoscience Frontiers, Journal Year: 2021, Volume and Issue: 12(6), P. 101230 - 101230

Published: May 27, 2021

The geomorphic studies are extremely dependent on the quality and spatial resolution of digital elevation model (DEM) data. unique terrain characteristics a particular landscape derived from DEM, which responsible for initiation development ephemeral gullies. As topographic features an area significantly influences erosive power water flow, it is important task extraction DEM to properly research gully erosion. Alongside, topography highly correlated with other geo-environmental factors i.e. geology, climate, soil types, vegetation density floristic composition, runoff generation, ultimately occurrences. Therefore, morphometric attributes data used in prediction erosion susceptibility (GES) mapping. In this study, remote sensing-Geographic information system (GIS) techniques coupled machine learning (ML) methods has been GES mapping parts Semnan province, Iran. Current focuses comparison predicted result by using three types Advanced Land Observation satellite (ALOS), ALOS World 3D-30 m (AW3D30) Space borne Thermal Emission Reflection Radiometer (ASTER) different resolutions. For further progress our work, here we have thirteen suitable conditioning (GECFs) based multi-collinearity analysis. ML conditional inference forests (Cforest), Cubist Elastic net chosen modelling accordingly. Variable's importance GECFs was measured through sensitivity analysis show that most factor occurrences gullies aforementioned (Cforest = 21.4, 19.65 17.08), followed lithology slope. Validation model's performed under curve (AUC) statistical indices. validation AUC shown Cforest appropriate predicting assessment DEMs (AUC value 0.994, AW3D30 0.989 ASTER 0.982) elastic cubist model. output maps will be decision-makers sustainable degraded land study area.

Language: Английский

A Review of Ensemble Learning Algorithms Used in Remote Sensing Applications DOI Creative Commons
Yuzhen Zhang, Jingjing Liu, Wenjuan Shen

et al.

Applied Sciences, Journal Year: 2022, Volume and Issue: 12(17), P. 8654 - 8654

Published: Aug. 29, 2022

Machine learning algorithms are increasingly used in various remote sensing applications due to their ability identify nonlinear correlations. Ensemble have been included many practical improve prediction accuracy. We provide an overview of three widely ensemble techniques: bagging, boosting, and stacking. first the underlying principles present analysis current literature. summarize some typical algorithms, which include predicting crop yield, estimating forest structure parameters, mapping natural hazards, spatial downscaling climate parameters land surface temperature. Finally, we suggest future directions for using applications.

Language: Английский

Citations

187
Flood Susceptibility Assessment Using Novel Ensemble of Hyperpipes and Support Vector Regression Algorithms DOI Open Access
Asish Saha, Subodh Chandra Pal, Alireza Arabameri

et al.

Water, Journal Year: 2021, Volume and Issue: 13(2), P. 241 - 241

Published: Jan. 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).

Language: Английский

Citations

141
Assessing groundwater artificial recharge suitability in the Mi River basin using GIS, RS, and FAHP: a comprehensive analysis with seasonal variations DOI Creative Commons
Qilong Song, Yuyu Liu,

Zhongjie Wang

et al.

Applied Water Science, Journal Year: 2025, Volume and Issue: 15(2)

Published: Jan. 29, 2025

Language: Английский

Citations

2
Modeling groundwater potential using novel GIS-based machine-learning ensemble techniques DOI Creative Commons
Alireza Arabameri, Subodh Chandra Pal, Fatemeh Rezaie

et al.

Journal of Hydrology Regional Studies, Journal Year: 2021, Volume and Issue: 36, P. 100848 - 100848

Published: June 26, 2021

The present study has been carried out in the Tabriz River basin (5397 km2) north-western Iran. Elevations vary from 1274 to 3678 m above sea level, and slope angles range 0 150.9 %. average annual minimum maximum temperatures are 2 °C 12 °C, respectively. rainfall ranges 243 641 mm, northern southern parts of receive highest amounts. In this study, we mapped groundwater potential (GWP) with a new hybrid model combining random subspace (RS) multilayer perception (MLP), naïve Bayes tree (NBTree), classification regression (CART) algorithms. A total 205 spring locations were collected by integrating field surveys data Iran Water Resources Management, divided into 70:30 for training validation. Fourteen conditioning factors (GWCFs) used as independent inputs. Statistics such receiver operating characteristic (ROC) five others evaluate performance models. results show that all models performed well GWP mapping (AUC > 0.8). MLP-RS achieved high validation scores = 0.935). relative importance GWCFs was revealed slope, elevation, TRI HAND most important predictors presence. This demonstrates ensemble can support sustainable management resources.

Language: Английский

Citations

97
Threats of climate change and land use patterns enhance the susceptibility of future floods in India DOI
Subodh Chandra Pal, Indrajit Chowdhuri, Biswajit Das

et al.

Journal of Environmental Management, Journal Year: 2021, Volume and Issue: 305, P. 114317 - 114317

Published: Dec. 24, 2021

Language: Английский

Citations

78
Prediction of gully erosion susceptibility mapping using novel ensemble machine learning algorithms DOI Creative Commons
Alireza Arabameri, Subodh Chandra Pal, Romulus Costache

et al.

Geomatics Natural Hazards and Risk, Journal Year: 2021, Volume and Issue: 12(1), P. 469 - 498

Published: Jan. 1, 2021

Spatial modelling of gully erosion at regional level is very relevant for local authorities to establish successful counter-measures and change land-use planning. This work exploring researching the potential a genetic algorithm-extreme gradient boosting (GE-XGBoost) hybrid computer education solution spatial mapping susceptibility erosion. The new machine learning approach combine extreme (XGBoost) algorithm (GA). GA metaheuristic being used improve efficiency XGBoost classification approach. A GIS database has been developed that contains recorded instances incidents 18 conditioning variables. These parameters are as predictive variables assess condition non-erosion or in given region within Kohpayeh-Sagzi River Watershed research area Iran. Exploratory results indicate proposed GE-XGBoost model superior other benchmark with desired precision (89.56%). Therefore, newly built may be promising method large-scale susceptibility.

Language: Английский

Citations

76
Evaluation of different DEMs for gully erosion susceptibility mapping using in-situ field measurement and validation DOI
Indrajit Chowdhuri, Subodh Chandra Pal, Asish Saha

et al.

Ecological Informatics, Journal Year: 2021, Volume and Issue: 65, P. 101425 - 101425

Published: Sept. 11, 2021

Language: Английский

Citations

65
Optimization modelling to establish false measures implemented with ex-situ plant species to control gully erosion in a monsoon-dominated region with novel in-situ measurements DOI
Asish Saha, Subodh Chandra Pal, Alireza Arabameri

et al.

Journal of Environmental Management, Journal Year: 2021, Volume and Issue: 287, P. 112284 - 112284

Published: March 9, 2021

Language: Английский

Citations

60
Flood susceptibility mapping using meta-heuristic algorithms DOI Creative Commons
Alireza Arabameri, Amir Seyed Danesh,

M. Santosh

et al.

Geomatics Natural Hazards and Risk, Journal Year: 2022, Volume and Issue: 13(1), P. 949 - 974

Published: April 11, 2022

Flood is a common global natural hazard, and detailed flood susceptibility maps for specific watersheds are important management measures. We compute the map Kaiser watershed in Iran using machine learning models such as support vector (SVM), Particle swarm optimization (PSO), genetic algorithm (GA) along with ensembles (PSO-GA SVM-GA). The application of assessment mapping analyzed, future research suggestions presented. model was constructed based on fifteen causatives: slope, slope aspect, elevation, plan curvature, land use, cover, normalize differences vegetation index (NDVI), convergence (CI), topographical wetness (TWI), topographic positioning Index (TPI), drainage density (DD), distance to stream, terrain ruggedness (TRI), surface texture (TST), geology stream power (SPI) inventory data which later divided by 70% training 30% validated model. output evaluated through sensitivity, specificity, accuracy, precision, Cohen Kappa, F-score, receiver operating curve (ROC). evaluation method shows robust results from (0.839), particle (0.851), (0.874), SVM-GA (0.886), PSO-GA (0.902). Compared have done some methods commonly used this assessment. A high-quality, informative database essential classification types that very helpful improve performances. performance ensemble better than model, yielding high degree accuracy (AUC-0.902%). Our approach, therefore, provides novel studies other watersheds.

Language: Английский

Citations

57
Vulnerability assessment of drought in India: Insights from meteorological, hydrological, agricultural and socio-economic perspectives DOI
Asish Saha, Subodh Chandra Pal, Indrajit Chowdhuri

et al.

Gondwana Research, Journal Year: 2022, Volume and Issue: 123, P. 68 - 88

Published: Nov. 14, 2022

Language: Английский

Citations

54