Prediction of spring flows using nonlinear autoregressive exogenous (NARX) neural network models

• Published in: Environmental monitoring and assessment Vol. 193; no. 6; p. 350
• Main Authors: Di Nunno, Fabio, Granata, Francesco, Gargano, Rudy, de Marinis, Giovanni
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.06.2021; Springer Nature B.V
• Subjects: Aquifers; Atmospheric Protection/Air Quality Control/Air Pollution; Carbonates; Climate change; Climate models; Discharge; Drought; Earth and Environmental Science; Ecology; Ecotoxicology; Environment; Environmental Management; Environmental monitoring; Environmental science; Groundwater; Groundwater resources; Karst; Lag time; Mediterranean climate; Monitoring/Environmental Analysis; Neural networks; Prediction models; Resource management; Spring; Spring (season); Sustainable development; Temporal resolution; Water resources; Water springs; Weekly; Mediterranean Area; Mediterranean area; Karst aquifers; Artificial neural network; NARX; Spring discharge prediction
• ISSN: 0167-6369; 1573-2959
• DOI: 10.1007/s10661-021-09135-6

In the Mediterranean area, climate changes have led to long and frequent droughts with a drop in groundwater resources. An accurate prediction of the spring discharge is an essential task for the proper management of the groundwater resources and for the sustainable development of large areas of the Mediterranean basin. This study shows an unprecedented application of non-linear AutoRegressive with eXogenous inputs (NARX) neural networks to the prediction of spring flows. In particular, discharge prediction models were developed for 9 monitored springs located in the Umbria region, along the carbonate ridge of the Umbria-Marche Apennines. In the modeling, the precipitation was also considered as an exogenous input parameter. Good performances were achieved for all the springs and for both short-term and long-term predictions, passing from a lag time equal to 1 month ( R 2  = 0.9012–0.9842, RAE  = 0.0933–0.2557) to 12 months ( R 2  = 0.9005–0.9838, RAE  = 0.0963–0.2409). The forecasting sensitivity to changes in the temporal resolution, passing from weekly to monthly, was also assessed. The good results achieved recommend the use of the NARX network for spring discharge prediction in other areas characterized by karst aquifers.