Optimal Groundwater Remediation Using Artificial Neural Networks

Rogers, Leah L.; Johnson, Virginia M.; Dowla, Farid

doi:10.1007/978-94-015-9341-0_8

Cited by 1 publication

(1 citation statement)

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“…Three approaches, the standard mathematical averaging methods, stochastic methods, and some regularization techniques, have mostly been applied for upscaling [6]. Many researchers have recently tried using the stochastic method, particularly artificial neural networks (ANNs) that are recognized as extremely nonlinearity analysis [7]. ANNs have been used to successfully capture trends with less knowledge of the behavior of the system in terms of interactions between biological, geological, chemical, hydro-morphological, and physical processes affecting the modeled system [8,9].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Scale Effects on Dynamics of Water Quality and Quantity for Sustainable Paddy Field Agriculture

Kim¹,

Kim²,

Lee³

et al. 2010

Environmental Engineering Research

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Modeling non-point pollution across multiple scales has become an important environmental issue. As a more representative and practical approach in quantifying and qualifying surface water, a modular neural network (MNN) was implemented in this study. Two different site-scales (1.5 × 10 5 and 1.62 × 10 6 m 2 ) with the same plants, soils, and paddy field management practices, were selected. Hydrologic data (rainfall, irrigation and surface discharge) and water quality data (time-series nutrient loadings) were continuously monitored and then used for the verification of MNN performance. Correlation coefficients (R) for the results predicted from the networks versus measured values were within the range of 0.41 to 0.95. The small block could be extrapolated to the large field for the rainfallsurface drainage process. Nutrient prediction produced less favorable results due to the complex phenomena of nutrients in the drainage water. However, the feasibility of using MNN to generate improved prediction accuracy was demonstrated if more hydrologic and environmental data are provided. The study findings confirmed the estimation accuracy of the upscaling from a small-segment block to large-scale paddy field, thereby contributing to the establishment of water quality management for sustainable agriculture.

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