An ANN‐based model for spatiotemporal groundwater level forecasting

TLDR

The multilayer aquifer in north‑western Iran has high groundwater levels in urban areas, yet simulating its spatiotemporal dynamics is challenging due to complex aquifer materials. The study evaluates the feasibility of using ANN methods to forecast groundwater levels at piezometers in the north‑western Iranian aquifer and seeks an optimal ANN architecture for accurate simulation. The authors compared six ANN architectures and training algorithms, used the resulting weight and bias spatial regressions to build a spatiotemporal ANN (STANN) model, and benchmarked its performance against hybrid neural‑geostatistics and multivariate time‑series geostatistics models. Experiments demonstrate that a standard feedforward ANN trained with the Levenberg–Marquardt algorithm yields accurate predictions, outperforming hybrid neural‑geostatistics and multivariate time‑series models, and the STANN model delivers acceptable results for the Tabriz multilayer aquifer. © 2008 John Wiley & Sons, Ltd.

Abstract

Abstract This paper evaluates the feasibility of using an artificial neural network (ANN) methodology for estimating the groundwater levels in some piezometers placed in an aquifer in north‐western Iran. This aquifer is multilayer and has a high groundwater level in urban areas. Spatiotemporal groundwater level simulation in a multilayer aquifer is regarded as difficult in hydrogeology due to the complexity of the different aquifer materials. In the present research the performance of different neural networks for groundwater level forecasting is examined in order to identify an optimal ANN architecture that can simulate the piezometers water levels. Six different types of network architectures and training algorithms are investigated and compared in terms of model prediction efficiency and accuracy. The results of different experiments show that accurate predictions can be achieved with a standard feedforward neural network trained usung the Levenberg–Marquardt algorithm. The structure and spatial regressions of the ANN parameters (weights and biases) are then used for spatiotemporal model presentation. The efficiency of the spatio‐temporal ANN (STANN) model is compared with two hybrid neural‐geostatistics (NG) and multivariate time series‐geostatistics (TSG) models. It is found in this study that the ANNs provide the most accurate predictions in comparison with the other models. Based on the nonlinear intrinsic ANN approach, the developed STANN model gives acceptable results for the Tabriz multilayer aquifer. Copyright © 2008 John Wiley & Sons, Ltd.