The Innovative Combination of Time Series Analysis Methods for the Forecasting of Groundwater Fluctuations

Najafabadipour, Amirhossein; Kamali, Gholamreza; Nezamabadi‐pour, Hossein

doi:10.1134/s0097807822020026

Cited by 7 publications

(1 citation statement)

References 18 publications

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“…Due to the inherent non-linear and non-stationary nature of groundwater level time series, intelligent data-driven methodologies have showcased promising results. Across the literature, various popular forecasting approaches have been tested on specific applications of groundwater level forecasting, including Auto-Regressive Integrated Moving Average (ARIMA), Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM) as well as hybrid approaches such as ARIMA-LSTM [38][39][40][41]. Comparative studies have consistently shown that machine learning-based methods outperform traditional numerical approaches [42] with superior prediction performance and capturing complex and non-linear relationships between input and output variables [43].…”

Section: Introductionmentioning

confidence: 99%

Towards Groundwater-Level Prediction Using Prophet Forecasting Method by Exploiting a High-Resolution Hydrogeological Monitoring System

Fronzi,

Narang,

Galdelli

et al. 2023

Water

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Forecasting of water availability has become of increasing interest in recent decades, especially due to growing human pressure and climate change, affecting groundwater resources towards a perceivable depletion. Numerous research papers developed at various spatial scales successfully investigated daily or seasonal groundwater level prediction starting from measured meteorological data (i.e., precipitation and temperature) and observed groundwater levels, by exploiting data-driven approaches. Barely a few research combine the meteorological variables and groundwater level data with unsaturated zone monitored variables (i.e., soil water content, soil temperature, and bulk electric conductivity), and—in most of these—the vadose zone is monitored only at a single depth. Our approach exploits a high spatial-temporal resolution hydrogeological monitoring system developed in the Conero Mt. Regional Park (central Italy) to predict groundwater level trends of a shallow aquifer exploited for drinking purposes. The field equipment consists of a thermo-pluviometric station, three volumetric water content, electric conductivity, and soil temperature probes in the vadose zone at 0.6 m, 0.9 m, and 1.7 m, respectively, and a piezometer instrumented with a permanent water-level probe. The monitored period started in January 2022, and the variables were recorded every fifteen minutes for more than one hydrologic year, except the groundwater level which was recorded on a daily scale. The developed model consists of three “virtual boxes” (i.e., atmosphere, unsaturated zone, and saturated zone) for which the hydrological variables characterizing each box were integrated into a time series forecasting model based on Prophet developed in the Python environment. Each measured parameter was tested for its influence on groundwater level prediction. The model was fine-tuned to an acceptable prediction (roughly 20% ahead of the monitored period). The quantitative analysis reveals that optimal results are achieved by expoiting the hydrological variables collected in the vadose zone at a depth of 1.7 m below ground level, with a Mean Absolute Error (MAE) of 0.189, a Mean Absolute Percentage Error (MAPE) of 0.062, a Root Mean Square Error (RMSE) of 0.244, and a Correlation coefficient of 0.923. This study stresses the importance of calibrating groundwater level prediction methods by exploring the hydrologic variables of the vadose zone in conjunction with those of the saturated zone and meteorological data, thus emphasizing the role of hydrologic time series forecasting as a challenging but vital aspect of optimizing groundwater management.

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