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

MLA

Naghibi, Seyed Amir, et al. “GIS-Based Groundwater Potential Mapping Using Boosted Regression Tree, Classification and Regression Tree, and Random Forest Machine Learning Models in Iran.” Environmental Monitoring & Assessment, vol. 188, no. 1, Jan. 2016, pp. 44–70. EBSCOhost, https://doi.org/10.1007/s10661-015-5049-6.

APA

Naghibi, S. A., Pourghasemi, H. R., & Dixon, B. (2016). GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran. Environmental Monitoring & Assessment, 188(1), 44–70. https://doi.org/10.1007/s10661-015-5049-6

Chicago

Naghibi, Seyed Amir, Hamid Reza Pourghasemi, and Barnali Dixon. 2016. “GIS-Based Groundwater Potential Mapping Using Boosted Regression Tree, Classification and Regression Tree, and Random Forest Machine Learning Models in Iran.” Environmental Monitoring & Assessment 188 (1): 44–70. doi:10.1007/s10661-015-5049-6.