Artificial Neural Network Model for the Prediction of Groundwater Quality

Abstract

The present article delves into the examination of groundwater quality, based on WQI, for drinking purposes in Baghdad City. Further, for carrying out the investigation, the data was collected from the Ministry of Water Resources of Baghdad, which represents water samples drawn from 114 wells in Al-Karkh and Al-Rusafa sides of Baghdad city. With the aim of further determining WQI, four water parameters such as (i) pH, (ii) Chloride (Cl), (iii) Sulfate (SO4), and (iv) Total dissolved solids (TDS), were taken into consideration. According to the computed WQI, the distribution of the groundwater samples, with respect to their quality classes such as excellent, good, poor, very poor and unfit for human drinking purpose, was found to be 14.9 %, 39.5 %, 22.8 %, 6.1 %, and 16.7 %, respectively. Additionally, to anticipate changes in groundwater WQI, IBM® SPSS® Statistics 19 software (SPSS) was used to develop an artificial neural network model (ANNM). With the application of this ANNM model, the results obtained illustrated high prediction efficiency, as the sum of squares error functions (for training and testing samples) and coefficient of determination (R2), were found to be (0.038 and 0.005) and 0.973, respectively. However, the parameters pH and Cl influenced model prediction significantly, thereby becoming crucial factors in the anticipation carried out by using ANNM model.

Water quality assessment along Tigris River (Iraq) using water quality index (WQI) and GIS software

“…This is due to that the river water receives the amount of pollution from different sources such as domestic sewage and different industrial effluents. Khudair et al (2018) used WQI method to evaluate the quality of groundwater for drinking purposes in Baghdad city. Where water samples were drawn from 114 wells distributed within the Baghdad city.…”

Estimation of irrigation water quality index in a semi-arid environment using data-driven approach

“…Abbaa et al (2017), for example, used a combination of multilinear regression, ANN, and adaptive neuro-fuzzy interference system tools to forecast the dissolved oxygen concentration downstream of Agra city. Similarly, Khudair et al (2018) assessed groundwater quality for drinking purposes in Baghdad using an ANN model. Furthermore, Alves et al (2018) proposed a new alternative approach to determining the water quality index that combines ultra-visible spectrophotometry with an ANN.…”

Prediction of watershed processes based on morphometric features using feature selection and neural network algorithms

“…Predictions of the composition and forms of alluvial fans have been improved by the rapidly developing science of arti cial intelligence and neural networks to extract environmental data [20][21][22] . These methods more clearly reveal the morphometric features affecting watershed processes.…”