Statistical Learning Methods for Classification and Prediction of Groundwater Quality Using a Small Data Record

Abstract

The objective of this study was to consider the efficiency of support vector machine (SVM) and artificial neural network (ANN) for the classification and prediction of groundwater quality using a small data record in Malayer, Iran. For this purpose, 14 groundwater quality variables that had been collected from 27 groundwater sampling wells were used. Cluster analysis discriminated the total sampling stations into two groups. The classification was implemented by SVM using polynomial and RBF kernel methods. The respective sensitivity and specificity of this model were 0.89 and 0.80 while that of positive predictive value and negative predictive value were 0.89 and 0.86, respectively. The prediction of water quality index (WQI) was implemented using ANN. Despite the high correlation coefficient between the predicted and observed values of WQI(r = 0.90), the generalization ability of this model was low(r = 0.60) indicating the over-fitting of the model to the training data set.

Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios

“…The study also showed that the SVM model is a fast, reliable, and cost-effective AI technique. The feasibility of AI techniques in groundwater quality simulation has been evaluated by numerous scholars and such studies have produced efficient performances [32][33][34][35][36][37][38][39][40].…”

Applying Fuzzy Inference and Machine Learning Methods for Prediction with a Small Dataset: A Case Study for Predicting the Consequences of Oil Spills on a Ground Environment

“…The authors of [10] showed that an appropriate choice of regression algorithm and input parameters allows us to build accurate surrogates for simulations in additive manufacturing. The authors of [11] used a cluster analysis approach to generate surrogate data in the medical domain. In [12], the authors applied a deep neural network (DNN) to integrate similarity features and perform learning to predict software defects.…”

“…As a result, different approaches to predicting, detecting, and assessing oil spills have been proposed to reduce their consequences [28][29][30]. Depending on the types of prediction, detection, and assessment problems being solved and the answers required, particular classification [11] or regression [31] algorithms are used. Both classes of algorithms (classification and regression) are used to predict outcomes in Supervised ML (SML), and both use labelled datasets to train algorithms [32].…”

“…In classification, the algorithm is trained using the training dataset (input (x)) to categorise data into different discrete classes (output (y)), such as dog or cat, yes or no, healthy or unhealthy, spam or not spam, etc. The most popular classification algorithms are as follows: Linear classifiers [34], Support Vector Machines (SVM) [35], the k-Nearest Neighbours algorithm [36], Decision Tree learning [37], and Neural Networks [11,12,20].…”

“…In regression, the algorithm is trained using the training dataset (input (x)) for the prediction of continuous variables (output (y)) which fall in the range of some allowable intervals, such as predicting market trends, prices, weather forecasting, etc. The most popular regression algorithms are as follows: Gaussian Process Regression (GPR) [39], Support Vector Regression (SVR) [40], Linear and Logistical Regressions [27,30,41], and Neural Networks [11,12,20].…”