Browsing byAuthorNaghibi, SA

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Showing results 1 to 13 of 13
Issue DateTitleAuthor(s)
2021-05-01APG: A novel Python-based ArcGIS toolbox to generate absence-datasets for geospatial studiesNaghibi, SA; Hashemi, H; Pradhan, B
2019-04-01Application of rotation forest with decision trees as base classifier and a novel ensemble model in spatial modeling of groundwater potentialNaghibi, SA; Dolatkordestani, M; Rezaei, A; Amouzegari, P; Heravi, MT; Kalantar, B; Pradhan, B
2016Assessment and comparison of combined bivariate and AHP models with logistic regression for landslide susceptibility mapping in the Chaharmahal-e-Bakhtiari Province, IranSangchini, EK; Emami, SN; Tahmasebipour, N; Pourghasemi, HR; Naghibi, SA; Arami, SA; Pradhan, B
2017-05-01A comparative assessment of GIS-based data mining models and a novel ensemble model in groundwater well potential mappingNaghibi, SA; Moghaddam, DD; Kalantar, B; Pradhan, B; Kisi, O
2017GIS-based Groundwater Spring Potential Mapping Using Data Mining Boosted Regression Tree and Probabilistic Frequency Ratio Models in IranMousavi, SM; Golkarian, A; Naghibi, SA; Kalantar, B; Pradhan, B
2016GIS-based landslide spatial modeling in Ganzhou City, ChinaHong, H; Naghibi, SA; Pourghasemi, HR; Pradhan, B
2018-10-10Groundwater augmentation through the site selection of floodwater spreading using a data mining approach (case study: Mashhad Plain, Iran)Naghibi, SA; Vafakhah, M; Hashemi, H; Pradhan, B; Alavi, SJ
2019-02-13Groundwater potential mapping using a novel data-mining ensemble modelKordestani, MD; Naghibi, SA; Hashemi, H; Ahmadi, K; Kalantar, B; Pradhan, B
2018-03-01Groundwater potential mapping using C5.0, random forest, and multivariate adaptive regression spline models in GISGolkarian, A; Naghibi, SA; Kalantar, B; Pradhan, B
2018-10-01Groundwater spring potential modelling: Comprising the capability and robustness of three different modeling approachesRahmati, O; Naghibi, SA; Shahabi, H; Bui, DT; Pradhan, B; Azareh, A; Rafiei-Sardooi, E; Samani, AN; Melesse, AM
2019-08-10Inverse method using boosted regression tree and k-nearest neighbor to quantify effects of point and non-point source nitrate pollution in groundwaterMotevalli, A; Naghibi, SA; Hashemi, H; Berndtsson, R; Pradhan, B; Gholami, V
2019-01-01Optimized conditioning factors using machine learning techniques for groundwater potential mappingKalantar, B; Al-Najjar, HAH; Pradhan, B; Saeidi, V; Halin, AA; Ueda, N; Naghibi, SA
2020-06-01Water Resources Management Through Flood Spreading Project Suitability Mapping Using Frequency Ratio, k-nearest Neighbours, and Random Forest AlgorithmsNaghibi, SA; Vafakhah, M; Hashemi, H; Pradhan, B; Alavi, SJ