Bhavya, R.; Sivaraj, K.; Elango, L. Ant Colony Based Artificial Neural Network for Predicting Spatial and Temporal Variation in Groundwater Quality. Water2023, 15, 2222.
Bhavya, R.; Sivaraj, K.; Elango, L. Ant Colony Based Artificial Neural Network for Predicting Spatial and Temporal Variation in Groundwater Quality. Water 2023, 15, 2222.
Bhavya, R.; Sivaraj, K.; Elango, L. Ant Colony Based Artificial Neural Network for Predicting Spatial and Temporal Variation in Groundwater Quality. Water2023, 15, 2222.
Bhavya, R.; Sivaraj, K.; Elango, L. Ant Colony Based Artificial Neural Network for Predicting Spatial and Temporal Variation in Groundwater Quality. Water 2023, 15, 2222.
Abstract
Data-driven models based on artificial intelligence are efficiently used to solve complex problems. The quality of groundwater is of utmost importance, as it directly impacts human health and the environment. In major parts of the world groundwater is the main source of drinking water, it is essential to periodically monitor water quality. Conventional water quality monitoring techniques involve periodical collection of water samples and analysis in the laboratory. This process is expensive, time consuming and involves lot of manual labor. The aim of our study is to build an ant colony optimized neural network for predicting groundwater quality parameters. We have proposed artificial neural network comprising of six hidden layers. The approach was validated using our groundwater quality dataset of a hard rock region in the northern part of Karnataka, India. Groundwater samples were collected by us periodically from March 2014 to October 2020 from 50 wells in this region. These samples where analysed for measuring the pH, Electrical Conductivity, Na+, Ca+, Na+, K+, Mg2+, F-, Cl- and U+. The temporal dataset was split for training, testing and validation of our model. Metrics such as R2 (Coefficient of Determination), RMSE (Root Mean Squared Error), NSE (Nash–Sutcliffe efficiencies) and MAE (Mean Absolute Error) were used to evaluate the prediction error and model performance. These performance evaluation metrics indicated the efficiency of our model in predicting the temporal variation in groundwater quality parameters. The method proposed by us can be used for prediction and the temporal frequency of sample collection can be reduced to save time and cost. The results also confirm that the combination of artificial neural network with ACO is a promising tool to optimize weights while training the network, and hence will help in reasonable prediction of groundwater quality parameters.
Keywords
Artificial neural network; Ant colony optimization; Groundwater quality; Prediction
Subject
Environmental and Earth Sciences, Water Science and Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
