@misc{10481/81061,
year = {2023},
month = {4},
url = {https://hdl.handle.net/10481/81061},
abstract = {Groundwater plays a significant role as a strategic resource in reducing the impact of droughts. In spite of its impor-
tance, there are still many groundwater bodies in which there is not enough monitoring data to define classic distrib-
uted mathematical models to forecast future potential levels. The main aim of this study is to propose and evaluate a
novel parsimonious integrated method for the short-term forecasting of groundwater levels. It has low requirements in
term of data, and it is operational and relatively easy to apply. It uses geostatistics, optimal meteorological exogenous
variables and artificial neural networks. We have illustrated our method in the aquifer “Campo de Montiel” (Spain).
The analysis of optimal exogenous variables revealed that, in general, the wells with stronger correlations with precip-
itation are located closer to the central part of the aquifer. NAR, which does not consider secondary information, is the
best approach for 25.5 % of the cases and is associated with well locations with lower R2 between groundwater levels
and precipitation. Amongst the approaches with exogenous variables, the ones that use effective precipitation have
been selected more times as the best experiments. NARX and Elman using effective precipitation had the best ap-
proaches with 21.6 % and 29.4 % of the cases respectively. For the selected approaches, we obtained a mean RMSE
of 1.14 m in the test and 0.76, 0.92, 0.92, 0.87, 0.90, and 1.05 m for the forecasting test for months 1 to 6 respectively
for the 51 wells, but the accuracy of the results can vary depending on the well. The interquartile range of the RMSE is
around 2 m for the test and forecasting test. The uncertainty of the forecasting is also considered by generating multiple
groundwater level series.},
organization = {Regional Ministry of Economic Transformation, Industry, Knowledge and Universities of the Regional Government of Andalusia through the post-doc programme of the Andalusian Plan for Research Development and Innovation (PAIDI 2021) (POSTDOC_21_00154, University of Granada, Antonio-Juan Collados- Lara)},
organization = {Projects SIGLO-AN (RTI2018-101397-B-I00) and SIGLO-PRO (PID2021-128021OB-I00) from the Spanish Ministry of Science and Innovation},
organization = {Spanish Ministry of Science and Innovation and the WP4 (Appraisal, protection & sustainable use of Europe's groundwater resources) of the CSA project A Geological Service for Europe (GSEU) (HORIZON-CL5-2021-D3-02-14CSA)},
organization = {Funding
for open access charge: Universidad de Granada/CBUA},
publisher = {Elsevier},
keywords = {Groundwater levels},
keywords = {Ordinary kriging},
keywords = {Effective precipitation},
keywords = {Artificial neural networks},
title = {A parsimonious methodological framework for short-term forecasting of groundwater levels},
doi = {http://dx.doi.org/10.1016/j.scitotenv.2023.163328},
author = {Collados Lara, Antonio Juan and Pulido Velázquez, David and Baca Ruiz, Luis Gonzaga and Pegalajar Jiménez, María Del Carmen and Pardo Igúzquiza, Eulogio and Baena-Ruiz, Leticia},
}