A novel ensemble computational intelligence approach for the spatial prediction of land subsidence susceptibility

Arabameri, Alireza; Saha, Sunil; Tiefenbacher, John P.; Cerdà, Artemi; Biggs, Trent W.; Pradhan, Biswajeet; Ngo, Phuong Thao Thi; Collins, Adrian L.

doi:10.1016/j.scitotenv.2020.138595

Cited by 81 publications

(50 citation statements)

References 101 publications

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“…Another study of LS in the Kashmar region, Iran based on MaxEnt models gives the result of ROC-AUC is 88.9% (Rahmati et al, 2019a,b) which is significantly higher than the present study. Similarly, LS susceptibility studies in the Semnan province of Iran using ANN models gives the result of ROC-AUC is 0.919 (Arabameri et al, 2020d) which is less than the our study result (AUC = 0.924). Therefore, studies indicate that the same ML models give different result in accuracy assessment from region to region, depending upon the local geo-environmental factors.…”

Section: Discussioncontrasting

confidence: 98%

“…A present time, ML algorithms and their ensemble methods have been applied in various fields for the susceptibility mapping and it has been shown to be effective in terms of predictive performance (Nguyen et al, 2019;Arabameri et al, 2020c;Feng et al, 2020;Liu et al, 2020;Zhang et al, 2020a;Saha et al, 2021b). Particularly, ensemble models always enhanced the output result by integrated the several ML algorithms (Mojaddadi et al, 2017;Arabameri et al, 2020d;Saha et al, 2021a).…”

Section: Discussionmentioning

confidence: 99%

“…Thus, based on the literature survey and local geoenvironmental conditions, different LSCFs have primarily selected to perform the LSSM in this present study area. After that multi-collinearity assessment studies was carried out and based on the result of multi-collinearity, a total of 12 geo-environmental variables were selected for the LS susceptibility modeling (Chen et al, 2019b;Arabameri et al, 2020d). Thereafter, ML algorithms of GLM, MaxEnt, SVM, and ANN were used and 11 possible ensemble models were developed to mapping LS susceptibility analysis.…”

Section: Discussionmentioning

confidence: 99%

“…The accuracy of the models in success and prediction rate has been analyzed through the ROC-AUC curve. The ROC curve is a quantitative model evaluator successfully used for model performance evaluation in most of the studies (Su et al, 2017;Arabameri et al, 2020d). The graphical presentation of the ROC-AUC curve created its most suitable model evaluator.…”

Section: Discussionmentioning

confidence: 99%

“…A body of literature survey (Arabameri et al, 2020d(Arabameri et al, , 2021Babaee et al, 2020;Rezaei et al, 2020) and based on the local topographical, hydrological, climatological, geological and environmental condition, here we have selected twelve appropriate LS conditioning factors. Therefore, we used twelve geo-environmental conditioning factors namely elevation, aspect, distance to road (DtR), groundwater drawdown, distance to fault (DtF), topographic wetness index (TWI), distance from stream (DtS), normalized differentiate vegetation index (NDVI), curvature, slope, land use and lithology to meet our objective.…”

Section: Introductionmentioning

confidence: 99%

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Performance Evaluation of GIS-Based Novel Ensemble Approaches for Land Subsidence Susceptibility Mapping

et al. 2021

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The optimal prediction of land subsidence (LS) is very much difficult because of limitations in proper monitoring techniques, field-base surveys and knowledge related to functioning and behavior of LS. Thus, due to the lack of LS susceptibility maps it is almost impossible to identify LS prone areas and as a result it influences severe economic and human losses. Hence, preparation of LS susceptibility mapping (LSSM) can help to prevent natural and human catastrophes and reduce the economic damages significantly. Machine learning (ML) techniques are becoming increasingly proficient in modeling purpose of such kinds of occurrences and they are increasing used for LSSM. This study compares the performances of single and hybrid ML models to preparation of LSSM for future prediction of performance analysis. In this study, the spatial prediction of LS was assessed using four ML models of maximum entropy (MaxEnt), general linear model (GLM), artificial neural network (ANN) and support vector machine (SVM). Alongside, the possible numbers of novel ensemble models were integrated through the aforementioned four ML models for optimal analysis of LSSM. An inventory LS map was prepared based on the previous occurrences of LS points and the dataset were divvied into 70:30 ratios for training and validating of the modeling process. To identify the robust and best LSSMs, receiver operating characteristic-area under curve (ROC-AUC) curve was employed. The ROC-AUC result indicated that ANN model gives the highest ROC-AUC (0.924) in training accuracy. The highest AUC (0.823) of the LSSMs was determined based on validation datasets identified by SVM followed by ANN-SVM (0.812).

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Section: Discussioncontrasting

confidence: 98%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Performance Evaluation of GIS-Based Novel Ensemble Approaches for Land Subsidence Susceptibility Mapping

et al. 2021

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Environmental Aspects of Biomass Utilization in Supercapacitors

Akter

Bari

Chowdhury

et al. 2023

Biomass‐Based Supercapacitors

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An analytical method to estimate groundwater depletion of an aquitard due to variable drawdowns in adjacent aquifers

Xiao

et al. 2022

Preprint

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Computing aquitard depletion, which is often overlooked, is of great significance for the assessment of groundwater resources and land subsidence. The issue is viewed as troublesome because of the additional computational burden, the poorly known hydrogeological parameters of the aquitard, and the lack of drawdown history in pumped aquifers. In this study, an analytical solution is derived to describe the drawdown variation in a nonlinear-consolidated aquitard under the condition of variable drawdowns in adjacent aquifers. Based on the analytical solution, we study the characteristics of groundwater dynamics and water balance under the conditions of linearly increasing drawdown of aquifers in adjacent aquifers. In addition, we put forward a method to calculate the depletion and hydrogeological parameters of an aquitard corresponding to variable drawdowns in adjacent aquifers, applicable even when historical drawdown data are lacking. The accuracy of the method is generally very good, but results improve when the drawdown history of pumped aquifers is divided into more periods for estimation. Under the condition of linear drawdown in adjacent aquifers, groundwater depletion and maximum water release rate of the aquitard increases with increasing compression index, coefficient of consolidation, aquitard thickness, rate of drawdown change in the adjacent aquifer, while decreasing with initial void ratio, and initial effective stress. The proposed approach is demonstrated at a field site in Shanghai City of China, and it would help for the effective management of groundwater resources and estimation of the global transfer from groundwater to surface water.

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A novel ensemble computational intelligence approach for the spatial prediction of land subsidence susceptibility

Cited by 81 publications

References 101 publications

Performance Evaluation of GIS-Based Novel Ensemble Approaches for Land Subsidence Susceptibility Mapping

Performance Evaluation of GIS-Based Novel Ensemble Approaches for Land Subsidence Susceptibility Mapping

Environmental Aspects of Biomass Utilization in Supercapacitors

An analytical method to estimate groundwater depletion of an aquitard due to variable drawdowns in adjacent aquifers

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