Self-organizing map clustering technique for ANN-based spatiotemporal modeling of groundwater quality parameters

Abstract

The present study integrates co-kriging as spatial estimator and self-organizing map (SOM) as clustering technique to identify spatially homogeneous clusters of groundwater quality data and to choose the most effective input data for feed-forward neural network (FFNN) model to simulate electrical conductivity (EC) and total dissolved solids (TDS) of groundwater. The methodology is presented in three stages. In the first stage, a geostatistics approach of co-kriging is used to estimate groundwater quality parameters at locations where the groundwater levels are measured. In stage two, a SOM clustering technique is used to identify spatially homogeneous clusters of groundwater quality data. The dominant input data, selected by spatial clustering and mutual information are then imposed into the FFNN model for one-step-ahead predictions of groundwater quality parameters at stage three. The performance of the newly proposed model is compared to a conventional linear forecasting method of multiple linear regression (MLR). The results suggest that the proposed model decreases dimensionality of the input layer and consequently the complexity of the FFNN model with acceptable efficiency in spatiotemporal simulation of groundwater quality parameters. The application of FFNN for modeling EC and TDS parameters increases the accuracy of predictions respectively up to 84.5% and 17% on average with regard to the MLR model.

A Review of the Artificial Neural Network Models for Water Quality Prediction

“…The term 'feed-forward' means that a neuron connection only exists from a neuron in the input layer to other neurons in the hidden layer or from a neuron in the hidden layer to neurons in the output layer. However, the neurons within a layer are not interconnected [9]. MLPs with only three layers are the most widely used architectures [59] in many types of feedforward ANNs (see Figure 2), followed by BPNNs [37] which use the back-propagation algorithms to train networks.…”

“…At present, there are many traditional water quality prediction methods, such as multiple linear regression (MLR) [7], auto-regressive integrated moving average (ARIMA) [8], etc. MLR is not able to detect a nonlinear relationship between water quality parameters because of its linear inherence [9]. output strategies.…”

A New Scheme to Improve the Performance of Artificial Intelligence Techniques for Estimating Total Organic Carbon from Well Logs

“…The root-mean-square error (RMSE) was used to weigh the deviation between the model prediction value and the actual value. The more favourable the model prediction effect was, the smaller the root-mean-square error (RMSE) was, and its calculation Equation was Equation (23). The VAF was usually used to evaluate the accuracy of models by comparing the model prediction value and the actual value.…”

“…In Equations (23) and (24), pt i represents the model prediction data, mt i represents the actually measured data, and n indicates the number of samples that were used for the network training or testing. Table 7 showed the results of the RMSE and VAF through calculations.…”

“…Therefore, it is difficult to make simple linear regression be approximate to the real function relationship, and as a result, it is impossible to predict the TOC content accurately with the well log. In recent years, artificial intelligence has attracted researchers' wide attention and has also been involved in many areas of research [21][22][23]. Actually speaking, the research showed that the artificial intelligence methods were quite close to nonlinear implicit functions.…”