Modeling Groundwater Nitrate Contamination Using Artificial Neural Networks

Stylianoudaki, Christina; Trichakis, Ioannis; Karatzas, George P.

doi:10.3390/w14071173

Cited by 11 publications

(3 citation statements)

References 53 publications

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“…The dataset is then trained using a hybrid BANN model. A Bayesian neural network is a type of neural network where the weights are distributed historically [41]. According to the BANN, standard networks should be developed with posterior inference and should take into account a probability distribution of weights rather than a fixed set of weights.…”

Section: Methodsmentioning

confidence: 99%

Climate change impact assessment on groundwater level changes: A study of hybrid model techniques

Afrifa

Zhang

Xin

et al. 2023

IET Signal Processing

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One of the most important sources of water supply is groundwater. However, the groundwater level (GWL) is significantly impacted by the global climate change. Therefore, under these more severe climate change conditions, the accurate and simple forecast of farmland GWL is a crucial component of agricultural water management. A hybrid model (HM) of Bayesian random forest (BRF), Bayesian support vector machine (BSVM), and Bayesian artificial neural network (BANN) is built in this study. The HM is made up of a Bayesian model averaging (BMA) and three machine learning models: random forest (RF), support vector machine (SVM), and artificial neural network. These three HMs are employed to help automate logical inference and decision‐making in business intelligence for groundwater management. For this purpose, data on 8 separate climatic factors that impact GWL changes in the study area were acquired. Nine distinct farming communities' GWL change data were utilised as the dependent variables for each model fit (community data). The effectiveness of the HM techniques was assessed using the evaluation metrics of mean absolute error (MAE), coefficient of determination (R2), mean absolute percent error (MAPE), and root mean square error (RMSE). The model fit in Suhum had the greatest performance with the highest accuracy (R2 varied from 0.9051 to 0.9679) and the lowest error scores (RMSE ranged from 0.0653 to 0.0727, and MAE ranged from 0.0121 to 0.0541), according to the models' evaluation results. The BRF delivered the greatest results when compared to the two independent HMs, the BSVM and BANN. Future GWL and climatic variable data may be trained using the trained HM techniques to determine the effects of climate change. Farmers, businesses, and civil society organisations might benefit from continuous monitoring of GWL data and education on climate change to help control and prevent excessive deteriorations of global climate change on GWL.

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Section: Methodsmentioning

confidence: 99%

Climate change impact assessment on groundwater level changes: A study of hybrid model techniques

Afrifa

Zhang

Xin

et al. 2023

IET Signal Processing

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“…Physicochemical indicators of groundwater can be used to measure water suitability for various types of production and domestic uses ( Shahid et al, 2014 ; Vadiati et al, 2016 ; Dippong et al, 2019 ; Seben et al, 2022 ; Stylianoudaki et al, 2022 ; Wang et al, 2022 ). However, unique and regional hydrogeological features can be heterogeneous in their spatial distribution ( Hernàndez-Diaz et al, 2019 ; Klingler et al, 2021 ; Labat et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Microbial community structural response to variations in physicochemical features of different aquifers

Dai

Zhang²,

Fang³

et al. 2023

Front. Microbiol.

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IntroductionThe community structure of groundwater microorganisms has a significant impact on groundwater quality. However, the relationships between the microbial communities and environmental variables in groundwater of different recharge and disturbance types are not fully understood.MethodsIn this study, measurements of groundwater physicochemical parameters and 16S rDNA high-throughput sequencing technology were used to assess the interactions between hydrogeochemical conditions and microbial diversity in Longkou coastal aquifer (LK), Cele arid zone aquifer (CL), and Wuhan riverside hyporheic zone aquifer (WH). Redundancy analysis indicated that the primary chemical parameters affecting the microbial community composition were NO3–, Cl–, and HCO3–.ResultsThe species and quantity of microorganisms in the river–groundwater interaction area were considerably higher than those in areas with high salinity [Shannon: WH (6.28) > LK (4.11) > CL (3.96); Chao1: WH (4,868) > CL (1510) > LK (1,222)]. Molecular ecological network analysis demonstrated that the change in microbial interactions caused by evaporation was less than that caused by seawater invasion under high-salinity conditions [(nodes, links): LK (71,192) > CL (51,198)], whereas the scale and nodes of the microbial network were greatly expanded under low-salinity conditions [(nodes, links): WH (279,694)]. Microbial community analysis revealed that distinct differences existed in the classification levels of the different dominant microorganism species in the three aquifers.DiscussionEnvironmental physical and chemical conditions selected the dominant species according to microbial functions. Gallionellaceae, which is associated with iron oxidation, dominated in the arid zones, while Rhodocyclaceae, which is related to denitrification, led in the coastal zones, and Desulfurivibrio, which is related to sulfur conversion, prevailed in the hyporheic zones. Therefore, dominant local bacterial communities can be used as indicators of local environmental conditions.

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“…Direct propagation networks, or perceptrons, are mainly used for such tasks. Thus, in [33][34][35], the parameters of the state of river surface waters were modeled in [36]-groundwater. Other paradigms of neural networks were used much less often.…”

Section: Introductionmentioning

confidence: 99%

The Use of Neural Network Modeling Methods to Determine Regional Threshold Values of Hydrochemical Indicators in the Environmental Monitoring System of Waterbodies

Tunakova

Новикова

Валиев

et al. 2023

Sensors

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The regulation of the anthropogenic load on waterbodies is carried out based on water quality standards that are determined using the threshold values of hydrochemical indicators. These applied standards should be defined both geographically and differentially, taking into account the regional specifics of the formation of surface water compositions. However, there is currently no unified approach to defining these regional standards. It is, therefore. appropriate to develop regional water quality standards utilizing modern technologies for the mathematical purpose of methods analysis using both experimental data sources and information system technologies. As suggested by the use of sets of chemical analysis and neural network cluster analysis, both methods of analysis and an expert assessment could identify surface water types as well as define the official regional threshold values of hydrochemical system indicators, to improve the adequacy of assessments and ensure the mathematical justification of developed standards. The process for testing the proposed approach was carried out, using the surface water resource objects in the territory of the Republic of Tatarstan as our example, in addition to using the results of long-term systematic measurements of informative hydrochemical indicators. In the first stage, typing was performed on surface waters using the neural network clustering method. Clustering was performed based on sets of determined hydrochemical parameters in Kohonen’s self-organizing neural network. To assess the uniformity of data, groups in each of the selected clusters were represented by specialists in this subject area’s region. To determine the regional threshold values of hydrochemical indicators, statistical data for the corresponding clusters were calculated, and the ranges of these values were used. The results of testing this proposed approach allowed us to recommend it for identifying surface water types, as well as to define the threshold values of hydrochemical indicators in the territory of any region with different surface water compositions.

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Modeling Groundwater Nitrate Contamination Using Artificial Neural Networks

Cited by 11 publications

References 53 publications

Climate change impact assessment on groundwater level changes: A study of hybrid model techniques

Climate change impact assessment on groundwater level changes: A study of hybrid model techniques

Microbial community structural response to variations in physicochemical features of different aquifers

The Use of Neural Network Modeling Methods to Determine Regional Threshold Values of Hydrochemical Indicators in the Environmental Monitoring System of Waterbodies

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