Comparison of Self-Organizing Map, Artificial Neural Network, and Co-Active Neuro-Fuzzy Inference System Methods in Simulating Groundwater Quality: Geospatial Artificial Intelligence

Abstract

Water quality experiments are difficult, costly, and time-consuming. Therefore, different modeling methods can be used as an alternative for these experiments. To achieve the research objective, geospatial artificial intelligence approaches such as the self-organizing map (SOM), artificial neural network (ANN), and co-active neuro-fuzzy inference system (CANFIS) were used to simulate groundwater quality in the Mazandaran plain in the north of Iran. Geographical information system (GIS) techniques were used as a pre-processer and post-processer. Data from 85 drinking water wells was used as secondary data and were separated into two splits of (a) 70 percent for training (60% for training and 10% for crossvalidation), and (b) 30 percent for the test stage. The groundwater quality index (GWQI) and the effective water quality factors (distance from industries, groundwater depth, and transmissivity of aquifer formations) were implemented as output and input variables, respectively. Statistical indices (i.e., R squared (R-sqr) and the mean squared error (MSE)) were utilized to compare the performance of three methods. The results demonstrate the high performance of the three methods in groundwater quality simulation. However, in the test stage, CANFIS (R-sqr = 0.89) had a higher performance than the SOM (R-sqr = 0.8) and ANN (R-sqr = 0.73) methods. The tested CANFIS model was used to estimate GWQI values on the area of the plain. Finally, the groundwater quality was mapped in a GIS environment associated with CANFIS simulation. The results can be used to manage groundwater quality as well as support and contribute to the sustainable development goal (SDG)-6,

Use of machine learning and geographical information system to predict nitrate concentration in an unconfined aquifer in Iran

“…The transmissivity of aquifers is one of the most important hydrogeological characteristics of an aquifer and has a significant effect on the quality of groundwater resources and the spread of pollutants (Awasthi et al, 2005;Gholami et al, 2021). The transmissivity of the aquifer has been determined by MRWC by drilling and pumping tests.…”

“…Today, several studies on the quality and quantity of water resources have been conducted worldwide using artificial intelligence and machine learning methods. Artificial intelligence in groundwater quality studies (Chou, 2006;Han et al, 2011;Band et al, 2020;Gholami et al, 2020;Maliqi et al, 2020;Mosaffa et al, 2021) and studies on groundwater depth fluctuations (Dixon, 2004;Saemi and Ahmadi, 2008;Gong et al, 2018;Chen et al, 2020;Gholami et al, 2021) has been widely used. Further, machine learning was used in different hydrological modeling studies with a high performance (Rahmati et al, 2017;Tongal and Booij, 2018;Rahmati et al, 2019;Azizi et al, 2020;Kashani et al, 2020;Javidan and Javidan, 2021;Wells et al, 2021).…”

Modeling of groundwater quality index by using artificial intelligence algorithms in northern Khartoum State, Sudan

“…Their research indicated that deep learning is more effective than the traditional GWQI models in groundwater quality assessment. Gholami et al (2021) operated an AI-based model using a co-active neuro-fuzzy inference system (CANFIS) and ANN, to assess the quality of groundwater in Iran. The study revealed that the fuzzy neural network has the highest performance in simulating water quality parameters over the other techniques.…”

Estimation of groundwater quality using an integration of water quality index, artificial intelligence methods and GIS: Case study, Central Mediterranean Region of Turkey

“…The inverse distance weighted (IDW), ordinary kriging (OK) and empirical Bayesian kriging (EBK)-like several deterministic and statistical interpolation methods have been developed to facilitate the estimation of unknown points and to form a continuous dataset for spatial assessment of the place (Hossain et al 2020). In addition, artificial intelligence-based models have been successfully used to model the spatial and temporal variation of groundwater quality by combining with geographic information systems (Gholami et al 2022). Sahour et al (2020) compared statistical (multiple linear regression (MLR)) and machine learning (deep neural networks (DNN), extreme gradient boosting (EGB)) techniques to map the spatial distribution of groundwater salinity on the Caspian coast.…”