Journal Article

Predicting Nitrate Concentration and Its Spatial Distribution in Groundwater Resources Using Support Vector Machines (SVMs) Model

Sartaj, Majid, Arabgol, Raheleh, Asghari, Keyvan

2016

Overview

In this paper, a support vector machine (SVM) model was developed to predict nitrate concentration in groundwater of Arak plain, Iran. The model provided a tool for prediction of nitrate concentration using a set of easily measurable groundwater quality variables including water temperature, electrical conductivity, groundwater depth, total dissolved solids, dissolved oxygen, pH, land use, and season of the year as input variables. The data set comprised of 160 water samples representing 40 different wells monitored for 1 year. The associated parameters for the optimum SVM model were obtained using a combination of 4-fold cross-validation and grid search technique. The optimum model was used to predict nitrate concentration in Arak plain aquifer. The SVM model predicted nitrate concentration in training and test stage data sets with reasonably high correlation (0.92 and 0.87, respectively) with the measured values and low root mean squared errors of 0.086 and 0.111, respectively. Finally, the map of nitrate concentration in groundwater was prepared for all four seasons using the trained SVM model and a geographic information system (GIS) interpolation scheme and compared with the results with a physics-based (flow and contaminant) model. Overall, the results showed that SVM model could be used as a fast, reliable, and cost-effective method for assessment and predicting groundwater quality.

• Publisher: Springer International Publishing,Springer,Springer Nature B.V
• Subject: Analysis / Applications of Mathematics / Aquifers / Assessments / Contaminants / Contamination / cost effectiveness / data collection / Datasets / Dissolution / Dissolved oxygen / Drinking water / Earth and Environmental Science / Electrical conductivity / Environment / Geographic information systems / Ground-water quality / Groundwater / Groundwater pollution / Iran / Land use / Math. Appl. in Environmental Science / Mathematical Modeling and Industrial Mathematics / Mathematical models / Nitrate content / Nitrates / Operations Research/Decision Theory / Partial differential equations / Physical properties / prediction / Regression analysis / Remote sensing / Spatial distribution / Support vector machines / Total dissolved solids / Variables / Water analysis / Water depth / Water quality / Water resources / Water sampling / water table / Water temperature / Water, Underground / wells