Application of artificial intelligence models for prediction of groundwater level fluctuations: case study (Tehran-Karaj alluvial aquifer)

Human health risk of nitrate in groundwater of Tehran-Karaj plain, Iran

Groundwater pollution by nitrate has is a major concern in the Tehran-Karaj aquifer, Iran, where the wells provide up to 80% of the water supply for a population of more than 18 million-yet detailed human health risks associated with nitrate are unknown due to the lack of accessible data to adequately cover the aquifer in both place and time. Here, using a rich dataset measured annually in more than 75 wells, we mapped the non-carcinogenic risk of nitrate in the aquifer between 2007 and 2018, a window with the most extensive anthropogenic activities in this region. Nitrate concentration varied from ~ 6 to ~ 150 mg/L, around three times greater than the standard level for drinking use, i.e. 50 mg/L. Samples with a non-carcinogenic risk of nitrate, which mainly located in the eastern parts of the study region, threatened children's health, the most vulnerable age group, in almost all of the years during the study period. Our findings revealed that the number of samples with a positive risk of nitrate for adults decreased in the aquifer from 2007 (17 wells) to 2018 (6 wells). Although we hypothesized that unsustainable agricultural practices, the growing population, and increased industrial activities could have increased the nitrate level in the Tehran-Karaj aquifer, improved sanitation infrastructures helped to prevent the intensification of nitrate pollution in the aquifer during the study period. Our compilation of annually mapped non-carcinogenic risks of nitrate is beneficial for local authorities to understand the high-risk zones in the aquifer and for the formulation of policy actions to protect the human health of people who use groundwater for drinking and other purposes in this densely populated region.

Data driven of underground water level using artificial intelligence hybrid algorithms

As the population grows, industry and agriculture have also developed and water resources require quantitative and qualitative management. Currently, the management of water resources is essential in the exploitation and development of these resources. For this reason, it is important to study water level fluctuations to check the amount of underground water storage. It is vital to study the level of underground water in Khuzestan province with a dry climate. The methods which exist for predicting and managing water resources are used in studies according to their strengths and weaknesses and according to the conditions. In recent years, artificial intelligence has been used extensively for groundwater resources worldwide. Since artificial intelligence models have provided good results in water resources up to now, in this study, the hybrid model of three new recombined methods including FF-KNN, ABC-KNN and DL-FF-KNN-ABC-MLP has been used to predict the underground water level in Khuzestan province (Qale-Tol area). The novelty of this technique is that it first does classification by presenting the first block (combination of FF-DWKNN algorithm) and predicts with the second block (combination of ABC-MLP algorithm). The algorithm's ability to decrease data noise will be enabled by this feature. In order to predict this key and important parameter, a part of the data related to wells 1-5 has been used to build artificial intelligence hybrid models and also to test these models, and to check this model three wells 6-8 have been used for the development of these models. After checking the results, it is clear that the statistical RMSE values of this algorithm including test, train and total data are 0.0451, 0.0597 and 0.0701, respectively. According to the results presented in the table reports, the performance accuracy of DL-FF-KNN-ABC-MLP for predicting this key parameter is very high.

Comparative assessment on ammonia nitrogen adsorption onto a saline soil-groundwater environment: distribution, multi-factor interaction, and optimization using response surface methodology and artificial neural network

The adsorption of soil can reduce the leaching of NH₄⁺-N from the external environment into groundwater. The adsorption of NH₄⁺-N is affected by many factors. It is critical to use statistical model to quantitatively describe the effects of interaction between two or more factors on the system response. In this study, HJ-Biplot was used to analyze the correlation characteristics of soil water, salt, and nitrogen, and the response surface methodology and artificial neural network were used to statistically visualize the interaction between factors, including concentration, total dissolved solids (TDS), temperature, and pH. The results showed that the study soil was a typical saline soil, with maximum soil NH₄⁺-N content of 85.45 mg/kg. For the adsorption experiments of NH₄⁺-N on saline soils, the effects of factors on the adsorption capacity were assessed using the RSM model. The RSM model was coupled with an ANN to predict the adsorption of NH₄⁺-N by saline soils. The NH₄⁺-N concentration and water pH were both significant at a linear level (p < 0.0001). The interaction between NH₄⁺-N concentration and pH was also more significant (p < 0.01). Under optimal conditions (concentration: 800 mg/L; temperature: 24 °C; TDS: 637 mg/L; pH: 7.83), the NH₄⁺-N adsorption capacity was 1650.2 ug/g, which was in general agreement with the calculated values from the Box-Behnken and RSM model. In addition, a statistical error criterion for the model showed that the RSM-ANN model had greater predictive ability than RSM model.

A literature review on pumping test analysis (2000-2022)

Accurate and precise values of hydrodynamic parameters are needed for groundwater modeling and management. Pumping test in the aquifer is the standard method to estimate the transmissivity, hydraulic conductivity, and storage coefficient as the key hydrodynamic parameters. Analytical solutions with curve matching and numerical modeling are two methods to estimate these parameters in the aquifer. Graphical analyses are commonly applied to time-drawdown/water table data which are time-consuming and approximate. Graphical type-curve methods as promising tools are used extensively in water resources studies, while applying these methods is still new in pumping test analysis. In the current study, the first effort based on our knowledge, we have reviewed the literature type-curve graphical methods in pumping test analysis. To achieve this goal, we reviewed and compared the journal articles regarding the characteristics and capabilities of the modeling process from 2000 to 2022. We have clustered the reviewed papers into graphical, modeling, and hybrid categories. Then, a comprehensive review of the selected papers was presented to delineate the highlight of every paper. This review could guide researchers in pumping test analysis. Also, we have presented various recommendations for future research to improve the quality of hydrodynamic parameter estimation.