Modelling Nitrate Prediction of Groundwater and Surface Water Using Artificial Neural Networks

Benzer, Semra; Benzer, Recep

doi:10.2339/politeknik.385434

Cited by 4 publications

(1 citation statement)

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“…A simpler model using pH, temperature, electrical conductivity, and aquifer level as input parameters has been presented as well [25]. If long time series are available, neural networks can be used for long-term prediction of nitrate concentrations in groundwater [26]. Soil characteristics (organic matter, clay and nitrogen content) have been proposed as inputs variables for the assessment of the spatial distribution of nitrate pollution [27].…”

Section: Introductionmentioning

confidence: 99%

Modeling Groundwater Nitrate Contamination Using Artificial Neural Networks

Stylianoudaki

Trichakis

Karatzas

2022

Water

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The scope of the present study is the estimation of the concentration of nitrates (NO3−) in groundwater using artificial neural networks (ANNs) based on easily measurable in situ data. For the purpose of the current study, two feedforward neural networks were developed to determine whether including land use variables would improve the model results. In the first network, easily measurable field data were used, i.e., pH, electrical conductivity, water temperature, air temperature, and aquifer level. This model achieved a fairly good simulation based on the root mean squared error (RMSE in mg/L) and the Nash–Sutcliffe Model Efficiency (NSE) indicators (RMSE = 26.18, NSE = 0.54). In the second model, the percentages of different land uses in a radius of 1000 m from each well was included in an attempt to obtain a better description of nitrate transport in the aquifer system. When these variables were used, the performance of the model increased significantly (RMSE = 15.95, NSE = 0.70). For the development of the models, data from chemical and physical analyses of groundwater samples from wells located in the Kopaidian Plain and the wider area of the Asopos River Basin, both in Greece, were used. The simulation that the models achieved indicates that they are a potentially useful tools for the estimation of groundwater contamination by nitrates and may therefore constitute a basis for the development of groundwater management plans.

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Section: Introductionmentioning

confidence: 99%