Application of Support Vector Machine, Random Forest, and Genetic Algorithm Optimized Random Forest Models in Groundwater Potential Mapping

A comparative assessment of GIS-based data mining models and a novel ensemble model in groundwater well potential mapping

(2017) Seyed Amir Naghibi et al.   JOURNAL OF HYDROLOGY

Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: A case study at Mehran Region, Iran

(2016) Omid Rahmati et al.   CATENA

Flood susceptibility assessment using GIS-based support vector machine model with different kernel types

(2015) Mahyat Shafapour Tehrany et al.   CATENA

Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree

(2015) Dieu Tien Bui et al.   Landslides

A Comparative Assessment Between Three Machine Learning Models and Their Performance Comparison by Bivariate and Multivariate Statistical Methods in Groundwater Potential Mapping

(2015) Seyed Amir Naghibi et al.   WATER RESOURCES MANAGEMENT

Genetic Optimization Using Derivatives: ThergenoudPackage forR

(2015) Walter R. Mebane et al.   Journal of Statistical Software

Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS

(2014) Mahyat Shafapour Tehrany et al.   JOURNAL OF HYDROLOGY

Groundwater qanat potential mapping using frequency ratio and Shannon’s entropy models in the Moghan watershed, Iran

(2014) Seyed Amir Naghibi et al.   Earth Science Informatics

Groundwater spring potential mapping using bivariate statistical model and GIS in the Taleghan Watershed, Iran

(2013) D. Davoodi Moghaddam et al.   Arabian Journal of Geosciences

Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression

(2013) Taskin Kavzoglu et al.   Landslides

Groundwater potential modelling using remote sensing and GIS: a case study of the Al Dhaid area, United Arab Emirates

(2013) Samy Ismail Elmahdy et al.   Geocarto International

A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS

(2012) Biswajeet Pradhan   COMPUTERS & GEOSCIENCES

Regional groundwater productivity potential mapping using a geographic information system (GIS) based artificial neural network model

(2012) Saro Lee et al.   HYDROGEOLOGY JOURNAL

Landslide Susceptibility Assessment in Vietnam Using Support Vector Machines, Decision Tree, and Naïve Bayes Models

(2012) Dieu Tien Bui et al.   MATHEMATICAL PROBLEMS IN ENGINEERING

GIS-based groundwater spring potential mapping in the Sultan Mountains (Konya, Turkey) using frequency ratio, weights of evidence and logistic regression methods and their comparison

(2011) Adnan Ozdemir   JOURNAL OF HYDROLOGY

Using a binary logistic regression method and GIS for evaluating and mapping the groundwater spring potential in the Sultan Mountains (Aksehir, Turkey)

(2011) Adnan Ozdemir   JOURNAL OF HYDROLOGY

GIS mapping of regional probabilistic groundwater potential in the area of Pohang City, Korea

(2010) Hyun-Joo Oh et al.   JOURNAL OF HYDROLOGY

Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine

(2009) Işık Yilmaz   Environmental Earth Sciences

The Application of Remote Sensing Technology to the Interpretation of Land Use for Rainfall-Induced Landslides Based on Genetic Algorithms and Artificial Neural Networks

(2009) Yie-Ruey Chen et al.   IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong, China

(2008) X. Yao et al.   GEOMORPHOLOGY