Threats of climate and land use change on future flood susceptibility

Journal of Cleaner Production, Год журнала: 2020, Номер 272, С. 122757 - 122757

Опубликована: Июль 12, 2020

Язык: Английский

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

Sensors, Год журнала: 2020, Номер 20(19), С. 5609 - 5609

Опубликована: Сен. 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.

Язык: Английский

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Evaluation of different boosting ensemble machine learning models and novel deep learning and boosting framework for head-cut gully erosion susceptibility

Journal of Environmental Management, Год журнала: 2021, Номер 284, С. 112015 - 112015

Опубликована: Янв. 27, 2021

Язык: Английский

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Decision tree based ensemble machine learning approaches for landslide susceptibility mapping

Geocarto International, Год журнала: 2021, Номер 37(16), С. 4594 - 4627

Опубликована: Март 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.

Язык: Английский

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Ensemble approach to develop landslide susceptibility map in landslide dominated Sikkim Himalayan region, India

Environmental Earth Sciences, Год журнала: 2020, Номер 79(20)

Опубликована: Окт. 1, 2020

Язык: Английский

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Torrential rainfall-induced landslide susceptibility assessment using machine learning and statistical methods of eastern Himalaya

Natural Hazards, Год журнала: 2021, Номер 107(1), С. 697 - 722

Опубликована: Фев. 8, 2021

Язык: Английский

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Prediction of highly flood prone areas by GIS based heuristic and statistical model in a monsoon dominated region of Bengal Basin

Remote Sensing Applications Society and Environment, Год журнала: 2020, Номер 19, С. 100343 - 100343

Опубликована: Июнь 18, 2020

Язык: Английский

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Novel Machine Learning Approaches for Modelling the Gully Erosion Susceptibility

Remote Sensing, Год журнала: 2020, Номер 12(17), С. 2833 - 2833

Опубликована: Сен. 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.

Язык: Английский

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Flash-flood hazard susceptibility mapping in Kangsabati River Basin, India

Geocarto International, Год журнала: 2021, Номер 37(23), С. 6713 - 6735

Опубликована: Июль 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.

Язык: Английский

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Ensemble of Machine-Learning Methods for Predicting Gully Erosion Susceptibility

Remote Sensing, Год журнала: 2020, Номер 12(22), С. 3675 - 3675

Опубликована: Ноя. 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.

Язык: Английский

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