In recent years, the increasing influence of climate change on the water cycle has emphasized the significance of analyzing and forecasting groundwater level (GWL) for effective water resource planning and management. This study proposes a comparative analysis of multi-step ahead daily GWL prediction by means of two different models. The former is an ensemble model, based on the stacking of two Machine Learning algorithms, Multilayer Perceptron (MLP) and Random Forest (RF). The second model is represented by a Radial Basis Function Neural Network (RBF-NN). For the modelling, a mid-term forecast horizon of up to 30 days was considered, while the precipitation and evapotranspiration were included as exogenous inputs. Three different wells located on the chalk aquifers of the northeastern region of France referred to as PZ, S1, and LS4, were selected for this study. The RBF-NN model demonstrated superior performance compared to the stacked MLP-RF model for wells PZ and S1. Conversely, for well LS4, both models displayed similar performance, albeit with a marginally higher accuracy observed in the stacked MLP-RF model. However, both models yielded accurate predictions of the GWL across all three wells, with R2 values exceeding 0.87 for all the wells and forecasting horizons. Furthermore, the RBF-NN model showed fewer reductions in performance as the forecasting horizon increased compared to the stacked MLP-RF model, leading to more reliable predictions even for a 30-day forecast horizon. An evident trend of decreasing Mean Absolute Percentage Error (MAPE) was observed from the 1st quartile to the 4th quartile of forecasted values. This highlights the models’ improved ability to provide accurate forecasts for deeper GWL values. The future developments of this research will be aimed at overcoming some limitations of the study, including lagged values of the exogenous variables precipitation and evaporation among the predictors, and considering aquifers with different hydrogeological characteristics.

Multi-step ahead groundwater level forecasting in Grand Est, France: Comparison between stacked machine learning model and radial basis function neural network

Giudicianni C.;Creaco E.;
2023-01-01

Abstract

In recent years, the increasing influence of climate change on the water cycle has emphasized the significance of analyzing and forecasting groundwater level (GWL) for effective water resource planning and management. This study proposes a comparative analysis of multi-step ahead daily GWL prediction by means of two different models. The former is an ensemble model, based on the stacking of two Machine Learning algorithms, Multilayer Perceptron (MLP) and Random Forest (RF). The second model is represented by a Radial Basis Function Neural Network (RBF-NN). For the modelling, a mid-term forecast horizon of up to 30 days was considered, while the precipitation and evapotranspiration were included as exogenous inputs. Three different wells located on the chalk aquifers of the northeastern region of France referred to as PZ, S1, and LS4, were selected for this study. The RBF-NN model demonstrated superior performance compared to the stacked MLP-RF model for wells PZ and S1. Conversely, for well LS4, both models displayed similar performance, albeit with a marginally higher accuracy observed in the stacked MLP-RF model. However, both models yielded accurate predictions of the GWL across all three wells, with R2 values exceeding 0.87 for all the wells and forecasting horizons. Furthermore, the RBF-NN model showed fewer reductions in performance as the forecasting horizon increased compared to the stacked MLP-RF model, leading to more reliable predictions even for a 30-day forecast horizon. An evident trend of decreasing Mean Absolute Percentage Error (MAPE) was observed from the 1st quartile to the 4th quartile of forecasted values. This highlights the models’ improved ability to provide accurate forecasts for deeper GWL values. The future developments of this research will be aimed at overcoming some limitations of the study, including lagged values of the exogenous variables precipitation and evaporation among the predictors, and considering aquifers with different hydrogeological characteristics.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11571/1497301
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