Effects of input/output parameters on artificial neural network model efficiency for breakthrough contaminant prediction

Abstract

Groundwater quality assessment is characterized by pollution injection rates, pollution injection locations and duration of pollution injection for identifying spatial and temporal variation. In this study, spatial variations are obtained by placing observation wells in the downstream zone. Temporal variations in contaminant concentration has been simulated during the study period. Generally, simulations are carried out using various numerical models, which are subject to the availability of all required input parameters and are necessary for the proper management of contaminated aquifers. In previous publications, artificial neural networks (ANNs) are prescribed in such situations as these modeling methods focus on available input/output datasets, thus resolving the concern of obtaining all inputs that a numerical simulator usually demands. Past studies have predicted groundwater breakthrough contaminants. But the effects of input/output variations need to be discussed. This study aims to quantify the effects of a few input/output datasets in the performance of ANN models to simulate pollutant transport in groundwater systems. The combinations of input/output scenarios have rendered these ANN models sensitive to variations, thus affecting model efficiency. These outcomes can reliably be employed for contaminant estimation and provide a paradigm in data collection that will help hydrogeologists to develop more efficient prediction models. HIGHLIGHTS A brief review on groundwater modeling using artificial neural networks.; Effects of input and output parameters in ANN modeling.; ANN modeling strengths and weakness in varying input/output parameters.; The practical implication of this methodology.;

Keywords

• artificial neural network
• cascade-forward backpropagation
• groundwater contaminant transport
• groundwater quality
• model performance