Novel Ensemble of Multivariate Adaptive Regression Spline with Spatial Logistic Regression and Boosted Regression Tree for Gully Erosion Susceptibility DOI Creative Commons
Paramita Roy, Subodh Chandra Pal, Alireza Arabameri

et al.

Remote Sensing, Journal Year: 2020, Volume and Issue: 12(20), P. 3284 - 3284

Published: Oct. 10, 2020

The extreme form of land degradation through different forms erosion is one the major problems in sub-tropical monsoon dominated region. formation and development gullies dominant or active process this So, identification prone regions necessary for escaping type situation maintaining correspondence between spheres environment. goal study to evaluate gully susceptibility rugged topography Hinglo River Basin eastern India, which ultimately contributes sustainable management practices. Due nature data instability, weakness classifier andthe ability handle data, accuracy a single method not very high. Thus, study, novel resampling algorithm was considered increase robustness its accuracy. Gully maps have been prepared using boosted regression trees (BRT), multivariate adaptive spline (MARS) spatial logistic (SLR) with proposed techniques. re-sampling able efficiency all predicted models by improving classifier. Each variable inventory map randomly allocated 5-fold cross validation, 10-fold bootstrap optimism bootstrap, while each consisted 30% database. ensemble model tested 70% validated other K-fold validation (CV) influence random selection training Here, methods are associated higher accuracy, but SLR more optimal than any according robust nature. AUC values BRT MARS 87.40%, 90.40% 90.60%, respectively. According 107,771 km2 (27.51%) area region high susceptible erosion. This potential developmental found primarily Basin, where lateral exposure mainly observed scarce vegetation. outcome work can help policy-makers implement remedial measures minimize damage caused gully.

Language: Английский

Threats of climate and land use change on future flood susceptibility DOI
Paramita Roy, Subodh Chandra Pal, Rabin Chakrabortty

et al.

Journal of Cleaner Production, Journal Year: 2020, Volume and Issue: 272, P. 122757 - 122757

Published: July 12, 2020

Language: Английский

Citations

158
Novel Ensemble Approach of Deep Learning Neural Network (DLNN) Model and Particle Swarm Optimization (PSO) Algorithm for Prediction of Gully Erosion Susceptibility DOI Creative Commons

Shahab S. Band,

Saeid Janizadeh, Subodh Chandra Pal

et al.

Sensors, Journal Year: 2020, Volume and Issue: 20(19), P. 5609 - 5609

Published: Sept. 30, 2020

This study aims to evaluate a new approach in modeling gully erosion susceptibility (GES) based on deep learning neural network (DLNN) model and an ensemble particle swarm optimization (PSO) algorithm with DLNN (PSO-DLNN), comparing these approaches common artificial (ANN) support vector machine (SVM) models Shirahan watershed, Iran. For this purpose, 13 independent variables affecting GES the area, namely, altitude, slope, aspect, plan curvature, profile drainage density, distance from river, land use, soil, lithology, rainfall, stream power index (SPI), topographic wetness (TWI), were prepared. A total of 132 locations identified during field visits. To implement proposed model, dataset was divided into two categories training (70%) testing (30%). The results indicate that area under curve (AUC) value receiver operating characteristic (ROC) considering datasets PSO-DLNN is 0.89, which indicates superb accuracy. rest are associated optimal accuracy have similar model; AUC values ROC DLNN, SVM, ANN for 0.87, 0.85, 0.84, respectively. efficiency terms prediction increased. Therefore, it can be concluded its PSO used as novel practical method predict susceptibility, help planners managers manage reduce risk phenomenon.

Language: Английский

Citations

147
Evaluation of different boosting ensemble machine learning models and novel deep learning and boosting framework for head-cut gully erosion susceptibility DOI
Wei Chen, Xinxiang Lei, Rabin Chakrabortty

et al.

Journal of Environmental Management, Journal Year: 2021, Volume and Issue: 284, P. 112015 - 112015

Published: Jan. 27, 2021

Language: Английский

Citations

131
Decision tree based ensemble machine learning approaches for landslide susceptibility mapping DOI
Alireza Arabameri, Subodh Chandra Pal, Fatemeh Rezaie

et al.

Geocarto International, Journal Year: 2021, Volume and Issue: 37(16), P. 4594 - 4627

Published: March 5, 2021

The concept of leveraging the predictive capacity predisposing factors for landslide susceptibility (LS) modeling has been continuously improved in recent work focusing on computational and machine learning algorithms. This paper explores different approaches to LS modelling using artificial intelligence. key objective this study is estimate a map Taleghan-Alamut basin Iran Credal Decision Tree (CDT)-based (i.e. CDT-Bagging, CDT-Multiboost CDT-SubSpace) hybrid approaches, which are state-of-the-art soft computing that hardly ever utilized assessment LS. In study, we used eighteen (LPFs) considered be most important local morphological geo-environmental influencing occurrence landslides. We calculated significance each LPFs Random Forest Method. also employed Receiver Operating Characteristic curve, precision, performance, robustness measurement selection best-fitting models. results shows that, compared other models, excellent model perspective with an average area under curve (AUC) 0.993 based 4-fold cross-validation. We, therefore, consider models effective method improving spatial prediction where scarps or bodies not clearly identified during preparation inventory maps. Therefore, it will helpful preparing future maps mitigate damages.

Language: Английский

Citations

105
Ensemble approach to develop landslide susceptibility map in landslide dominated Sikkim Himalayan region, India DOI
Indrajit Chowdhuri, Subodh Chandra Pal, Alireza Arabameri

et al.

Environmental Earth Sciences, Journal Year: 2020, Volume and Issue: 79(20)

Published: Oct. 1, 2020

Language: Английский

Citations

86
Torrential rainfall-induced landslide susceptibility assessment using machine learning and statistical methods of eastern Himalaya DOI
Indrajit Chowdhuri, Subodh Chandra Pal, Rabin Chakrabortty

et al.

Natural Hazards, Journal Year: 2021, Volume and Issue: 107(1), P. 697 - 722

Published: Feb. 8, 2021

Language: Английский

Citations

80
Prediction of highly flood prone areas by GIS based heuristic and statistical model in a monsoon dominated region of Bengal Basin DOI
Sadhan Malik, Subodh Chandra Pal, Indrajit Chowdhuri

et al.

Remote Sensing Applications Society and Environment, Journal Year: 2020, Volume and Issue: 19, P. 100343 - 100343

Published: June 18, 2020

Language: Английский

Citations

79
Novel Machine Learning Approaches for Modelling the Gully Erosion Susceptibility DOI Creative Commons
Alireza Arabameri, Omid Asadi Nalivan, Subodh Chandra Pal

et al.

Remote Sensing, Journal Year: 2020, Volume and Issue: 12(17), P. 2833 - 2833

Published: Sept. 1, 2020

The extreme form of land degradation caused by the formation gullies is a major challenge for sustainability resources. This problem more vulnerable in arid and semi-arid environment associated damage to agriculture allied economic activities. Appropriate modeling such erosion therefore needed with optimum accuracy estimating regions taking appropriate initiatives. Golestan Dam has faced an acute gully over last decade adversely affected society. Here, artificial neural network (ANN), general linear model (GLM), maximum entropy (MaxEnt), support vector machine (SVM) learning algorithm 90/10, 80/20, 70/30, 60/40, 50/50 random partitioning training validation samples was selected purposively susceptibility. main objective this work predict susceptible zone possible accuracy. For purpose, approaches were implemented. 20 conditioning factors considered predicting areas considering multi-collinearity test. variance inflation factor (VIF) tolerance (TOL) limit assessment reducing error models increase efficiency outcome. ANN sample most optimal analysis. area under curve (AUC) values receiver operating characteristics (ROC) (50/50) data are 0.918 0.868, respectively. importance causative estimated help Jackknife test, which reveals that important topography position index (TPI). Apart from this, prioritization all predicted into account set, should future researchers select perspective. type outcome planners local stakeholders implement water conservation measures.

Language: Английский

Citations

78
Flash-flood hazard susceptibility mapping in Kangsabati River Basin, India DOI
Rabin Chakrabortty, Subodh Chandra Pal, Fatemeh Rezaie

et al.

Geocarto International, Journal Year: 2021, Volume and Issue: 37(23), P. 6713 - 6735

Published: July 12, 2021

Flood-susceptibility mapping is an important component of flood risk management to control the effects natural hazards and prevention injury. We used a remote-sensing geographic information system (GIS) platform machine-learning model develop susceptibility map Kangsabati River Basin, India where flash common due monsoon precipitation with short duration high intensity. And in this subtropical region, climate change's impact helps influence distribution rainfall temperature variation. tested three models-particle swarm optimization (PSO), artificial neural network (ANN), deep-leaning (DLNN)-and prepared final classify flood-prone regions study area. Environmental, topographical, hydrological, geological conditions were included models, was selected based on relations between potentiality causative factors multi-collinearity analysis. The results validated evaluated using area under receiver operating characteristic (ROC) curve (AUC), which indicator current state environment value >0.95 implies greater floods. AUC values for ANN, DLNN, PSO training datasets 0.914, 0.920, 0.942, respectively. Among these showed best performance 0.942. approach applicable eastern part India, allow mitigation help improve region.

Language: Английский

Citations

78
Ensemble of Machine-Learning Methods for Predicting Gully Erosion Susceptibility DOI Creative Commons
Subodh Chandra Pal, Alireza Arabameri, Thomas Blaschke

et al.

Remote Sensing, Journal Year: 2020, Volume and Issue: 12(22), P. 3675 - 3675

Published: Nov. 10, 2020

Gully formation through water-induced soil erosion and related to devastating land degradation is often a quasi-normal threat human life, as it responsible for huge loss of surface soil. Therefore, gully susceptibility (GES) mapping necessary in order reduce the adverse effect diminishes this type harmful consequences. The principle goal present research study develop GES maps Garhbeta I Community Development (C.D.) Block; West Bengal, India, by using machine learning algorithm (MLA) boosted regression tree (BRT), bagging ensemble BRT-bagging with K-fold cross validation (CV) resampling techniques. combination aforementioned MLAs approaches state-of-the-art soft computing, not used evaluation. In further progress our work, here we total 20 conditioning factors (GECFs) 199 head cut points modelling GES. variables’ importance, which erosion, was determined based on random forest (RF) among several GECFs study. output result model’s performance validated receiver operating characteristics-area under curve (ROC-AUC), sensitivity, specificity, positive predictive value (PPV) negative (NPV) statistical analysis. predicted shows that most well fitted where AUC K-3 fold 0.972, whereas PPV NPV 0.94, 0.93, 0.96 respectively, training dataset, followed BRT model. Thus, from concluded BRT-Bagging can be applied new approach studies spatial prediction outcome work helpful policy makers implementing remedial measures minimize damages caused erosion.

Language: Английский

Citations

76