A comparison of machine learning models for the mapping of groundwater spring potential

Application of learning vector quantization and different machine learning techniques to assessing forest fire influence factors and spatial modelling

(2020) Hamid Reza Pourghasemi et al.   ENVIRONMENTAL RESEARCH

Assessing and mapping multi-hazard risk susceptibility using a machine learning technique

(2020) Hamid Reza Pourghasemi et al.   Scientific Reports

Spatial mapping of groundwater springs potentiality using grid search-based and genetic algorithm-based support vector regression

(2020) A'kif Al-Fugara et al.   Geocarto International

Gully erosion spatial modelling: Role of machine learning algorithms in selection of the best controlling factors and modelling process

(2020) Hamid Reza Pourghasemi et al.   Geoscience Frontiers

Is multi-hazard mapping effective in assessing natural hazards and integrated watershed management?

(2019) Hamid Reza Pourghasemi et al.   Geoscience Frontiers

A comparison between ten advanced and soft computing models for groundwater qanat potential assessment in Iran using R and GIS

(2017) Seyed Amir Naghibi et al.   THEORETICAL AND APPLIED CLIMATOLOGY

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

(2017) Seyed Amir Naghibi et al.   WATER RESOURCES MANAGEMENT

Interpretation of dam deformation and leakage with boosted regression trees

(2016) Fernando Salazar et al.   ENGINEERING STRUCTURES

GIS-based multivariate adaptive regression spline and random forest models for groundwater potential mapping in Iran

(2016) Mohsen Zabihi et al.   Environmental Earth Sciences

Evaluation of groundwater vulnerability in El-Bahariya Oasis, Western Desert, Egypt, using modelling and GIS techniques: A case study

(2016) M H Masoud et al.   Journal of Earth System Science

Spatial analysis of groundwater potential using weights-of-evidence and evidential belief function models and remote sensing

(2015) Naser Tahmassebipoor et al.   Arabian Journal of Geosciences

Quantifying temporal changes in gully erosion areas with object oriented analysis

(2015) Rajesh B.V. Shruthi et al.   CATENA

GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran

(2015) Seyed Amir Naghibi et al.   ENVIRONMENTAL MONITORING AND ASSESSMENT

Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines

(2015) V. Rodriguez-Galiano et al.   ORE GEOLOGY REVIEWS

GIS-based groundwater spring potential assessment and mapping in the Birjand Township, southern Khorasan Province, Iran

(2014) Zohre Sadat Pourtaghi et al.   HYDROGEOLOGY JOURNAL

Application of GIS based data driven evidential belief function model to predict groundwater potential zonation

(2014) Haleh Nampak et al.   JOURNAL OF HYDROLOGY

Groundwater recharge zones mapping using GIS: a case study in Southern part of Jordan Valley, Jordan

(2013) Nezar Hammouri et al.   Arabian Journal of Geosciences

Gully erosion susceptibility assessment by means of GIS-based logistic regression: A case of Sicily (Italy)

(2013) Christian Conoscenti et al.   GEOMORPHOLOGY

Delineation of groundwater potential zone: An AHP/ANP approach

(2013) ETISHREE AGARWAL et al.   Journal of Earth System Science

Machine Learning Feature Selection Methods for Landslide Susceptibility Mapping

(2013) Natan Micheletti et al.   Mathematical Geosciences

Multivariate adaptive regression spline (MARS) and least squares support vector machine (LSSVM) for OCR prediction

(2012) Pijush Samui et al.   SOFT COMPUTING

Evaluation of modelling techniques for forest site productivity prediction in contrasting ecoregions using stochastic multicriteria acceptability analysis (SMAA)

(2011) Wim Aertsen et al.   ENVIRONMENTAL MODELLING & SOFTWARE

Uncertainty in the spatial prediction of soil texture

(2011) Mareike Ließ et al.   GEODERMA

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

Object-oriented mapping of landslides using Random Forests

(2011) André Stumpf et al.   REMOTE SENSING OF ENVIRONMENT

Groundwater recharge study in arid region: An approach using GIS techniques and numerical modeling

(2010) Ismail Chenini et al.   COMPUTERS & GEOSCIENCES

Groundwater assessment in Salboni Block, West Bengal (India) using remote sensing, geographical information system and multi-criteria decision analysis techniques

(2010) Madan K. Jha et al.   HYDROGEOLOGY JOURNAL

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

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

Assessment of Groundwater Potential in a Semi-Arid Region of India Using Remote Sensing, GIS and MCDM Techniques

(2010) Deepesh Machiwal et al.   WATER RESOURCES MANAGEMENT

Groundwater vulnerability to selenium in semi-arid environments: Amman Zarqa Basin, Jordan

(2009) Mustafa Al Kuisi et al.   ENVIRONMENTAL GEOCHEMISTRY AND HEALTH

Weight of evidence and artificial neural networks for potential groundwater spring mapping: an application to the Mt. Modino area (Northern Apennines, Italy)

(2009) Alessandro Corsini et al.   GEOMORPHOLOGY

A case study using support vector machines, neural networks and logistic regression in a GIS to identify wells contaminated with nitrate-N

(2009) Barnali Dixon   HYDROGEOLOGY JOURNAL

Integrated remote sensing and GIS‐based approach for assessing groundwater potential in West Medinipur district, West Bengal, India

(2008) A. Chowdhury et al.   INTERNATIONAL JOURNAL OF REMOTE SENSING

Wetland vegetation distribution modelling for the identification of constraining environmental variables

(2008) J. Peters et al.   LANDSCAPE ECOLOGY

GIS for the assessment of the groundwater recharge potential zone

(2008) Hsin-Fu Yeh et al.   Environmental geology.