Groundwater Quality: The Application of Artificial Intelligence.

Publisher: Hindawi Pub. Corp Country of Publication: United States NLM ID: 101516361 Publication Model: eCollection Cited Medium: Internet ISSN: 1687-9813 (Electronic) Linking ISSN: 16879805 NLM ISO Abbreviation: J Environ Public Health Subsets: MEDLINE

Original Publication: New York, NY : Hindawi Pub. Corp., [2009]-[2024]

Artificial Intelligence* ; Groundwater*; Fuzzy Logic ; Humans ; Neural Networks, Computer ; Water Quality

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.
Competing Interests: The authors declare that they have no conflicts of interest.
(Copyright © 2022 Mosleh Hmoud Al-Adhaileh et al.)

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Date Created: 20220905 Date Completed: 20220908 Latest Revision: 20220929

20231215

PMC9433268

10.1155/2022/8425798

36060879