Performance of machine learning methods in predicting water quality index based on irregular data set: application on Illizi region (Algerian southeast)

Saber Kouadri,Abu Reza Md Towfiqul Islam,Ahmed Elbeltagi,Samir Kateb

doi:10.1007/s13201-021-01528-9

Saber Kouadri, Abu Reza Md Towfiqul Islam + Show 2 more

Open Access

https://doi.org/10.1007/s13201-021-01528-9

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Abstract

Groundwater quality appraisal is one of the most crucial tasks to ensure safe drinking water sources. Concurrently, a water quality index (WQI) requires some water quality parameters. Conventionally, WQI computation consumes time and is often found with various errors during subindex calculation. To this end, 8 artificial intelligence algorithms, e.g., multilinear regression (MLR), random forest (RF), M5P tree (M5P), random subspace (RSS), additive regression (AR), artificial neural network (ANN), support vector regression (SVR), and locally weighted linear regression (LWLR), were employed to generate WQI prediction in Illizi region, southeast Algeria. Using the best subset regression, 12 different input combinations were developed and the strategy of work was based on two scenarios. The first scenario aims to reduce the time consumption in WQI computation, where all parameters were used as inputs. The second scenario intends to show the water quality variation in the critical cases when the necessary analyses are unavailable, whereas all inputs were reduced based on sensitivity analysis. The models were appraised using several statistical metrics including correlation coefficient (R), mean absolute error (MAE), root mean square error (RMSE), relative absolute error (RAE), and root relative square error (RRSE). The results reveal that TDS and TH are the key drivers influencing WQI in the study area. The comparison of performance evaluation metric shows that the MLR model has the higher accuracy compared to other models in the first scenario in terms of 1, 1.4572*10–08, 2.1418*10–08, 1.2573*10–10%, and 3.1708*10–08% for R, MAE, RMSE, RAE, and RRSE, respectively. The second scenario was executed with less error rate by using the RF model with 0.9984, 1.9942, 3.2488, 4.693, and 5.9642 for R, MAE, RMSE, RAE, and RRSE, respectively. The outcomes of this paper would be of interest to water planners in terms of WQI for improving sustainable management plans of groundwater resources.

Highlights

Groundwater quality assessment and monitoring is a crucial task for sustainable optimal management of groundwater resources(Egbueri 2020; Kawo and Karuppannan 2018; Li et al 2018; Islam et al 2020a)
The deterioration of water quality could be caused by many factors, e.g., inadequate proper sanitation, pollutants derived from industries and excessive use of fertilizer in agricultural practices, climate change, and poor groundwater management plan (Loecke et al 2017; Alam et al 2007; Trevett et al 2005; Islam et al 2018)
With 284,618 km2 Illizi county is the third largest wilayah by area. It is located in the extreme southeast of Algeria, and it borders with three countries on a 1,233 km border with: Tunisia and Libya from the east and Niger from the south, where Ouargla county and Tamanrasset county border it from the north and the west, respectively (Kouadri and Samir 2021)

Summary

Introduction

Groundwater quality assessment and monitoring is a crucial task for sustainable optimal management of groundwater resources(Egbueri 2020; Kawo and Karuppannan 2018; Li et al 2018; Islam et al 2020a). Ongley (2000) found that water quality appraisal using traditional methods triggers losses in the economic aspect which influences the policy-making ability for groundwater quality management plans In addition to this circumstance, the recent Corona pandemic made laboratories suffer from the lack of chemical analysis reactors used for water analysis after the remarkable reduction of the quantities of imported goods in several countries. To overcome these circumstances, it is necessary to use a promising and cost-effect tool for rapid and precise water quality appraisal. The artificial intelligence (AI) model is an alternative option to generate models during the pandemic period that would help predict the overall quality of groundwater based on the results of analyses that do not need expensive reactors or very developed measurement instruments

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