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. 2022 Aug 24:2022:8425798.
doi: 10.1155/2022/8425798. eCollection 2022.

Groundwater Quality: The Application of Artificial Intelligence

Mosleh Hmoud Al-Adhaileh  1   2 , Theyazn H H Aldhyani  1   3 , Fawaz Waselallah Alsaade  1   4 , Mohammed Al-Yaari  1   5 , Ali Khalaf Ahmed Albaggar  6
Affiliations

Groundwater Quality: The Application of Artificial Intelligence

Mosleh Hmoud Al-Adhaileh et al. J Environ Public Health. .

Abstract

Humans and all other living things depend on having access to clean water, as it is an indispensable essential resource. Therefore, the development of a model that can predict water quality conditions in the future will have substantial societal and economic value. This can be accomplished by using a model that can predict future water quality circumstances. In this study, we employed a sophisticated artificial neural network (ANN) model. This study intends to develop a hybrid model of single exponential smoothing (SES) with bidirectional long short-term memory (BiLSTM) and an adaptive neurofuzzy inference system (ANFIS) to predict water quality (WQ) in different groundwater in the Al-Baha region of Saudi Arabia. Single exponential smoothing (SES) was employed as a preprocessing method to adjust the weight of the dataset, and the output from SES was processed using the BiLSTM and ANFIS models for predicting water quality. The data were randomly divided into two phases, training (70%) and testing (30%). Efficiency statistics were used to evaluate the SES-BiLSTM and SES-ANFIS models' prediction abilities. The results showed that while both the SES-BiLSTM and SES-ANFIS models performed well in predicting the water quality index (WQI), the SES-BiLSTM model performed best with accuracy (R = 99.95% and RMSE = 0.00910) at the testing phase, where the performance of the SES-ANFIS model was R = 99.95% and RMSE = 2.2941 × 100-07. The findings support the idea that the SES-BilSTM and SES-ANFIS models can be used to predict the WQI with high accuracy, which will help to enhance WQ. The results demonstrated that the SES-BiLSTM and SES-ANFIS models' forecasts are accurate and that both seasons' performances are consistent. Similar investigations of groundwater quality prediction for drinking purposes should benefit from the proposed SES-BiLSTM and SES-ANFIS models. Consequently, the results demonstrate that the proposed SES-BiLSTM and SES-ANFIS models are useful tools for predicting whether the groundwater in Al-Baha city is suitable for drinking and irrigation purposes.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
A generic framework.
Figure 2
Figure 2
LSTM model.
Figure 3
Figure 3
BiLSTM structure algorithm.
Figure 4
Figure 4
Structure of ANFIS model for predicting WQ.
Figure 5
Figure 5
Topology of ANFIS system for predicting WQ.
Figure 6
Figure 6
Flowchart of developing system. (a) SES-ANFIS; (b) SES-BiLSTM model.
Figure 7
Figure 7
Regression plot of the proposed system: (a) SES-ANFIS model and (b) SES-BiLSTM model at training process.
Figure 8
Figure 8
Histogram plot of the proposed system: (a) SES-ANFIS model and (b) SES-BiLSTM model at training process.
Figure 9
Figure 9
Regression plot of the proposed system: (a) SES-ANFIS model and (b) SES-BiLSTM model at testing process.
Figure 10
Figure 10
Histogram plot of the proposed system: (a) SES-ANFIS model and (b) SES-BiLSTM model at testing process.
Figure 11
Figure 11
Important parameters.
See this image and copyright information in PMC

References

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    1. Abderrahman W. A., Rasheeduddin M., Al-Harazin I. M., Esuflebbe M., Eqnaibi B. S. Impacts of management practices on groundwater conditions in the eastern province, Saudi arabia. Hydrogeology Journal . 1995;3(4):32–41. doi: 10.1007/s100400050060. - DOI

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