Groundwater Quality: The Application of Artificial Intelligence

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.

Water Scarcity Management to Ensure Food Scarcity through Sustainable Water Resources Management in Saudi Arabia

“…Groundwater extraction has now reached 17 BCM/year. Groundwater supplies 80% of the water requirements in Saudi Arabia [49,50]. Compared with the annual amounts of water being extracted, groundwater recharge is extremely low.…”

The Use of Artificial Intelligence in Water Management Projects in the Kingdom of Saudi Arabia

“…There are opportunities related to the active employment of AI and IOT in water distribution systems and Apps in the developing countries such as Saudi Arabia. The challenges of such employment should be perceived with significant interests and deep analysis to support the optimum investment in the available opportunities (Al-Adhaileh, 2022).…”

“…The decision maker should allocate balanced efforts toward these dimensions to achieve the desired results from the employment of such technologies in water distribution. (Krishnan, 2022) The poor technological infrastructure is the obvious obstacle which represents the greatest challenge that not only prevents the launching of corporate AI in the water management systems as water distribution projects, but also affects significantly in the probability of effective operation of the proposed systems (Al-Adhaileh, 2022).…”

“…In Saudi Arabia, the water resources either for drinking or for other purposes represents a great challenge, as there are no rivers or freshwater lakes. The kingdom depends mainly on the groundwater wells, and this forces the decision makers in the government to employ the most efficient strategies to improve the distribution process for better utilization of these scarce resources (Al-Adhaileh, 2022). The employment of AI can be viewed as a smart system that can be used heavily in the kingdom in the management of water especially in distribution.…”

Drinking Water Resources Suitability Assessment Based on Pollution Index of Groundwater Using Improved Explainable Artificial Intelligence

“…Most samples met WHO standards, with AI techniques enhancing GWQI predictions, indicating mostly suitable water quality. Mosleh et al [28] conducted a study in the Al-Baha region of Saudi Arabia using a hybrid model combining single exponential smoothing (SES) with BiLSTM and ANFIS to predict water quality. While both models were effective, SES-BiLSTM was slightly more accurate.…”