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| Issue Date | Title | Author(s) |
| 2020-07 | A novel ensemble computational intelligence approach for the spatial prediction of land subsidence susceptibility. | Arabameri, A; Saha, S; Roy, J; Tiefenbacher, JP; Cerda, A; Biggs, T; Pradhan, B; Thi Ngo, PT; Collins, AL |
| 2022-05-05 | Application of the novel state-of-the-art soft computing techniques for groundwater potential assessment | Arabameri, A; Santosh, M; Moayedi, H; Tiefenbacher, JP; Pal, SC; Nalivan, OA; Costache, R; Ahmed, N; Hoque, MA-A; Chakrabortty, R; Cerda, A |
| 2019-05-01 | Assessment of landslide susceptibility using statistical- and artificial intelligence-based FR-RF integrated model and multiresolution DEMs | Arabameri, A; Pradhan, B; Rezaei, K; Lee, CW |
| 2019-12-01 | Comparative assessment using boosted regression trees, binary logistic regression, frequency ratio and numerical risk factor for gully erosion susceptibility modelling | Arabameri, A; Pradhan, B; Lombardo, L |
| 2019-01-01 | Comparison of machine learning models for gully erosion susceptibility mapping | Arabameri, A; Chen, W; Loche, M; Zhao, X; Li, Y; Lombardo, L; Cerda, A; Pradhan, B; Bui, DT |
| 2019-01-01 | An ensemble model for landslide susceptibility mapping in a forested area | Arabameri, A; Pradhan, B; Rezaei, K; Lee, S; Sohrabi, M |
| 2020-01-02 | Evaluation of recent advanced soft computing techniques for gully erosion susceptibility mapping: A comparative study | Arabameri, A; Blaschke, T; Pradhan, B; Pourghasemi, HR; Tiefenbacher, JP; Bui, DT |
| 2020-08-01 | Flash flood susceptibility modelling using functional tree and hybrid ensemble techniques | Arabameri, A; Saha, S; Chen, W; Roy, J; Pradhan, B; Bui, DT |
| 2022-01-01 | Flood susceptibility mapping in Brahmaputra floodplain of Bangladesh using deep boost, deep learning neural network, and artificial neural network | Ahmed, N; Hoque, MAA; Arabameri, A; Pal, SC; Chakrabortty, R; Jui, J |
| 2019-03-01 | GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression tree algorithms | Arabameri, A; Pradhan, B; Rezaei, K; Sohrabi, M; Kalantari, Z |
| 2019-09-01 | Gully erosion susceptibility mapping using GIS-based multi-criteria decision analysis techniques | Arabameri, A; Pradhan, B; Rezaei, K; Conoscenti, C |
| 2019-02-15 | Gully erosion zonation mapping using integrated geographically weighted regression with certainty factor and random forest models in GIS | Arabameri, A; Pradhan, B; Rezaei, K |
| 2019-12-19 | Gully Head-Cut Distribution Modeling Using Machine Learning Methods—A Case Study of N.W. Iran | Arabameri, A; Chen, W; Blaschke, T; Tiefenbacher, JP; Pradhan, B; Tien Bui, D |
| 2018-10-30 | Identification of erosion-prone areas using different multi-criteria decision-making techniques and gis | Arabameri, A; Pradhan, B; Pourghasemi, HR; Rezaei, K |
| 2020-11-01 | Implementation of artificial intelligence based ensemble models for gully erosion susceptibility assessment | Chowdhuri, I; Pal, SC; Arabameri, A; Saha, A; Chakrabortty, R; Blaschke, T; Pradhan, B; Band, SS |
| 2020-03 | A methodological comparison of head-cut based gully erosion susceptibility models: Combined use of statistical and artificial intelligence | Arabameri, A; Cerda, A; Pradhan, B; Tiefenbacher, JP; Lombardo, L; Bui, DT |
| 2020-03-01 | Morphometric analysis for soil erosion susceptibility mapping using novel gis-based ensemble model | Arabameri, A; Tiefenbacher, JP; Blaschke, T; Pradhan, B; Bui, DT |
| 2020-06-01 | Novel ensemble approaches of machine learning techniques in modeling the gully erosion susceptibility | Arabameri, A; Nalivan, OA; Saha, S; Roy, J; Pradhan, B; Tiefenbacher, JP; Ngo, PTT |
| 2020-10-02 | Novel ensemble of multivariate adaptive regression spline with spatial logistic regression and boosted regression tree for gully erosion susceptibility | Roy, P; Pal, SC; Arabameri, A; Chakrabortty, R; Pradhan, B; Chowdhuri, I; Lee, S; Bui, DT |
| 2019-10-20 | Novel ensembles of COPRAS multi-criteria decision-making with logistic regression, boosted regression tree, and random forest for spatial prediction of gully erosion susceptibility | Arabameri, A; Yamani, M; Pradhan, B; Melesse, A; Shirani, K; Tien Bui, D |
