Abstract
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 (R2 = 0.9012–0.9842, RAE = 0.0933–0.2557) to 12 months (R2 = 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.
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Data availability
The data are freely available online on the website https://apps.arpa.umbria.it/acqua/contenuto/Portata-delle-Sorgenti.
Code availability
The code was developed by the authors in the Matlab environment and is available upon request.
References
Aghelpour, P., & Varshavian, V. (2020). Evaluation of stochastic and artificial intelligence models in modeling and predicting of river daily flow time series. Stochastic Environmental Research and Risk Assessment, 34, 33–50. https://doi.org/10.1007/s00477-019-01761-4.
Allocca, V., Manna, F., & De Vita, P. (2014). Estimating annual groundwater recharge coefficient for karst aquifers of the southern Apennines (Italy). Hydrology and Earth System Sciences, 18, 803–817. https://doi.org/10.5194/hess-18-803-2014
Alsumaiei A.A. (2020). A nonlinear autoregressive modeling approach for forecasting groundwater level fluctuation in urban aquifers. Water, 12(3), https://doi.org/10.3390/w12030820
Aquino, L. S., Timm, L. C., Reichardt, K., Barbosa, E. P., Parfitt, J. M. B., Nebel, A. L. C., & Penning, L. H. (2015). State-space approach to evaluate effects of land levelling on the spatial relationships of soil properties of a lowland area. Soil Tillage Research, 145, 135–147. https://doi.org/10.1016/j.still.2014.09.007
Angelini, P. and Dragoni, W. (1997). The problem of modeling limestone springs: the case of Bagnara (North Apennines, Italy). Groundwater, 35(4), https://doi.org/10.1111/j.1745-6584.1997.tb00126.x
ARPA Umbria (2011). Lo Stato Chimico dei corpi idrici sotterranei in Umbria ai sensi del DLgs 30/2009.
Barfield, B., Felton, G., Stevens, E., & McCann, M. (2004). A simple model of karst spring flow using modified NRCS procedures. Journal of Hydrology, 287(1), 34–48. https://doi.org/10.1016/j.jhydrol.2003.09.031
Bicalho, C. C., Batiot-Guilhe, C., Seidel, J. L., Van Exter, S., & Jourde, H. (2012). Geochemical evidence of water source characterization and hydrodynamic responses in a karst aquifer. Journal of Hydrology, 450–451, 206–218. https://doi.org/10.1016/j.jhydrol.2012.04.059
Bishop, C. M. (1995). Neural networks for pattern recognition. Oxford University Press.
Cambi, C., Valigi, D., & Di Matteo, L. (2010). Hydrogeological study of data-scarce limestone massifs: the case of Gualdo Tadino and Monte Cucco structures (central Apennines, Italy). Bollettino di Geofisica Teorica ed Applicata, 51(4), 345–360.
Chen, Z., Grasby, S. E., & Osadetz, K. G. (2004). Relation between climate variability and groundwater levels in the upper carbonate aquifer, southern Manitoba. Canada Journal of Hydrology, 290(1), 43–62. https://doi.org/10.1016/j.jhydrol.2003.11.029
Coulibaly, P., Anctil, F., Aravena, R., & Bobee, B. (2001). Artificial neural network modeling of water table depth fluctuations. Water Resources Research, 37(4), 885–896. https://doi.org/10.1029/2000WR900368
de Rooij, R., Perrochet, P., & Graham, W. (2013). From rainfall to spring discharge: coupling conduit flow, subsurface matrix flow and surface flow in karst systems using a discrete-continuum model. Advances in Water Resources, 61, 29–41. https://doi.org/10.1016/j.advwatres.2013.08.009
Desouky, M. A. A., & Abdelkhalik, O. (2019). Wave prediction using wave rider position measurements and NARX network in wave energy conversion. Applied Ocean Research, 82, 10–21. https://doi.org/10.1016/j.apor.2018.10.016
Di Nunno, F., Granata, F. (2020). Groundwater level prediction in Apulia region (Southern Italy) using NARX neural network Environmental Research, 190 https://doi.org/10.1016/j.envres.2020.110062
Di Nunno, F., Granata, F., Gargano, R., & de Marinis, G. (2021). Forecasting of extreme storm tide events using NARX neural network-based models. Atmosphere, 12(4), 512. https://doi.org/10.3390/atmos12040512
Di Nunno, F., de Marinis, G., Gargano, R., & Granata, F. (2021). Tide prediction in the Venice Lagoon using nonlinear autoregressive exogenous (NARX) neural network. Water, 13(9), 1173. https://doi.org/10.3390/w13091173
Diodato, N., Bellocchi, G., Fiorillo, F., & Ventafridda, G. (2017). Case study for investigating groundwater and the future of mountain spring discharges in Southern Italy. Journal of Mountain Science, 14(9), 1791–1800. https://doi.org/10.1007/s11629-017-4445-5
Fiorillo, F., & Doglioni, A. (2010). The relation between karst spring discharge and rainfall by cross-correlation analysis (Campania, Southern Italy). Hydrogeology Journal, 18(8), 1881–1895. https://doi.org/10.1007/s10040-010-0666-1
Foresee, F.D. & Hagan, M.T. (1997). Gauss-Newton approximation to Bayesian learning. Proceedings of International Conference on Neural Networks (ICNN'97), 3, pp. 1930–1935, https://doi.org/10.1109/ICNN.1997.614194
Fratini, S., Francesconi, F., Lanzi, D., Checcucci, R., Frondini, F. & Spinsanti, R. (2013). Acquifero vulcanico vulsino in Umbria: studio idrogeologico per la caratterizzazione della presenza di arsenico ed alluminio ed il corretto utilizzo delle acque sotterranee. Acque Sotterranee - Italian Journal of Groundwater, 2, pp. 9–22, https://doi.org/10.7343/AS-048-13-0075
Granata, F., Saroli, M., de Marinis, G., & Gargano, R. (2018). Machine learning models for spring discharge forecasting. Geofluids. https://doi.org/10.1155/2018/8328167
Granata, F. (2019). Evapotranspiration evaluation models based on machine learning algorithms—A comparative study. Agricultural Water Management, 217, 303–315. https://doi.org/10.1016/j.agwat.2019.03.015
Granata, F., Gargano, R., & de Marinis, G. (2020). Artificial intelligence based approaches to evaluate actual evapotranspiration in wetlands. Science of the Total Environment, 703, 135653. https://doi.org/10.1016/j.scitotenv.2019.135653
Granata, F. & Di Nunno, F. (2021). Artificial Intelligence models for prediction of the tide level in Venice. Stochastic Environmental Research and Risk Assessment, https://doi.org/10.1007/s00477-021-02018-9
Guzman, S. M., Paz, J. O., & Tagert, M. L. M. (2017). The use of NARX neural networks to forecast daily groundwater levels. Water Resources Management, 31, 1591–1603. https://doi.org/10.1007/s11269-017-1598-5
Guzman, S. M., Paz, J. O., Tagert, M. L. M., & Mercer, A. E. (2019). Evaluation of seasonally classified inputs for the prediction of daily groundwater levels: NARX networks vs support vector machines. Environmental Modeling and Assessment, 24, 223–234. https://doi.org/10.1007/s10666-018-9639-x
Hu, C., Hao, Y., Yeh, T. C. J., Pang, B., & Wu, Z. (2008). Simulation of spring flows from a karst aquifer with an artificial neural network. Hydrological Processes, 22(5), 596–604. https://doi.org/10.1002/hyp.6625
Iannello, J. P. (1982). Time delay estimation via cross-correlation in the presence of large estimation errors. IEEE Transactions on Signal Processing, 30(6), 998–1003. https://doi.org/10.1109/tassp.1982.1163992
Jukić, D., & Denić-Jukić, V. (2015). Investigating relationships between rainfall and karst-spring discharge by higher-order partial correlation functions. Journal of Hydrology, 530, 24–36. https://doi.org/10.1016/j.jhydrol.2015.09.045
Kong A.S.L., Johannet, A., Borrell, E.V. and Pistre, S. (2015). Neural networks for karst groundwater management. Case of the Lez spring (Southern France). Environmental Earth Sciences, 74(12), pp. 7617–7632, https://doi.org/10.1007/s12665-015-4708-9
Lambrakis, N., Andreou, A. S., Polydoropoulos, P., Georgopoulos, E., & Bountis, T. (2000). Nonlinear analysis and forecasting of a brackish karstic spring. Water Resources Research, 36(4), 875–884. https://doi.org/10.1029/1999WR900353
Li, G., Goldscheider, N., & Field, M. S. (2016). Modeling karst spring hydrograph recession based on head drop at sinkholes. Journal of Hydrology, 542, 820–827. https://doi.org/10.1016/j.jhydrol.2016.09.052
Li, Z., Xu, X., Liu, M., Li, X., Zhang, R., Wang, K., & Xu, C. (2017). State-space prediction of spring discharge in a karst catchment in southwest China. Journal of Hydrology, 549, 264–276. https://doi.org/10.1016/j.jhydrol.2017.04.001
Liu, Y., Wang, B., Zhan, H., Fan, Y., Zha, Y., & Hao, Y. (2017). Simulation of nonstationary spring discharge using time series models. Water Resources Management, 31(3), 4875–4890. https://doi.org/10.1007/s11269-017-1783-6
MacKay, D. J. C. (1992). Bayesian interpolation. Neural Computation, 4, 415–447. https://doi.org/10.1162/neco.1992.4.3.415
Mastrorillo, L., Baldoni, T., Banzato, F., Boscherini, A., Cascone, D., Checcucci, R., et al. (2009). Quantitative hydrogeological analysis of the carbonate domain in the Umbria region. Italian Journal of Engineering Geology and Environment, 1, 137–155.
Mastrorillo, L., & Petitta, M. (2010). Effective infiltration variability in the Umbria-Marche carbonate aquifers of central Italy. Journal of Mediterranean Earth Sciences, 2, 9–18. https://doi.org/10.3304/JMES.2010.002
MathWorks (2020). MATLAB Deep Learning Toolbox Release 2020a. Natick, Massachusetts, United States.
Mohammadi, B., Mehdizadeh, S., Ahmadi, F., Lien, N.T.T., Linh, N.T.T. and Pham, Q.B. (2020). Developing hybrid time series and artificial intelligence models for estimating air temperatures. Stochastic Environmental Research and Risk Assessment, https://doi.org/10.1007/s00477-020-01898-7
Moore, D.S., Notz, W.I. and Flinger, M.A. (2018). The basic practice of statistics. W.H. Freeman and Company, 8th Edition, p. 654.
Najafzadeh, M., & Saberi-Movahed, F. (2018). GMDH-GEP to predict free span expansion rates below pipelines under waves. Marine Georesources & Geotechnology, 37(2), 1–18. https://doi.org/10.1080/1064119X.2018.1443355
Najafzadeh, M., Saberi-Movahed, F., & Sarkamaryan, S. (2018). NF-GMDH-based self-organized systems to predict bridge pier scour depth under debris flow effects. Marine Georesources & Geotechnology, 36(5), 589–602. https://doi.org/10.1080/1064119X.2017.1355944
Najafzadeh, M., & Oliveto, G. (2020). Riprap incipient motion for overtopping flows with machine learning models. Journal of Hydroinformatics, 22(4), 749–767. https://doi.org/10.2166/hydro.2020.129
Paleologos, E., Skitzi, I., Katsifarakis, K. and Darivianakis, N. (2013). Neural network simulation of spring flow in karst environments. Stochastic Environmental Research and Risk Assessment, 27(8), https://doi.org/10.1007/s00477-013-0717-y
Panagopoulos, G. P., & Lambrakis, N. (2006). The contribution of time series analysis to the study of the hydrodynamic characteristics of the karst systems: Application on two typical karst aquifers of Greece (Trifilia, Almyros Crete). Journal of Hydrology, 329(3), 368–376. https://doi.org/10.1016/j.jhydrol.2006.02.023
Raeisi, E., & Karami, G. (1997). Hydrochemographs of Berghan karst spring as indicators of aquifer characteristics. Journal of Cave and Karst Studies, 59(3), 112–118.
Raju, M.M., Srivastava, R.K., Bisht, D.C.S., Sharma, H.C. and Kumar, A. (2011). Development of artificial neural-network-based models for the simulation of spring discharge. Advances in Artificial Intelligence, https://doi.org/10.1155/2011/686258
Romanazzi, A., Gentile, F., & Polemio, M. (2015). Modelling and management of a Mediterranean karstic coastal aquifer under the effects of seawater intrusion and climate change. Environment and Earth Sciences, 74, 115–128.
Saberi-Movahed, F., Najafzadeh, M., & Mehrpooya, A. (2020). Receiving more accurate predictions for longitudinal dispersion coefficients in water pipelines: Training group method of data handling using extreme learning machine conceptions. Water Resources Management, 34, 529–561. https://doi.org/10.1007/s11269-019-02463-w
Sappa, G., De Filippi, F.M., Iacurto, S. and Grelle, G. (2019a). Evaluation of minimum karst spring discharge using a simple rainfall-input model: The case study of Capodacqua di Spigno Spring (Central Italy). Water, 11(807), https://doi.org/10.3390/w11040807
Sappa, G., Iacurto, S., Ferranti, F., & De Filippi, F. M. (2019). Groundwater quality assessment in a karst coastal region of the West Aurunci Mountains (Central Italy). Geofluids, 2019, 1–14. https://doi.org/10.1155/2019/3261713
Schwen, A., Yang, Y. and Wendroth, O. (2013). State-space models describe the spatial variability of bromide leaching controlled by land use, irrigation, and pedologic characteristics. Vadose Zone Journa, 12(4), https://doi.org/10.2136/vzj2012.0196
Tamburini, A. and Menichetti, M. (2020). Groundwater circulation in fractured and karstic aquifers of the Umbria-Marche Apennine. Water, 12(4), https://doi.org/10.3390/w12041039
Vergni, L., & Todisco, F. (2011). Spatio-temporal variability of precipitation, temperature and agricultural drought indices in Central Italy. Agricultural and Forest Meteorology, 151(3), 301–313. https://doi.org/10.1016/j.agrformet.2010.11.005
Wunsch, A., Liesch, T., & Broda, S. (2018). Forecasting groundwater levels using nonlinear autoregressive networks with exogenous input (NARX). Journal of Hydrology, 567, 743–758. https://doi.org/10.1016/j.jhydrol.2018.01.045
Zakhem, A.B. and Kattaa, B. (2016). Cumulative drought effect on Figeh karstic spring discharge (Damascus basin, Syria). Environmental Earth Sciences, 75(2), https://doi.org/10.1007/s12665-015-5013-3
Zhang, J., Zhang, X., Niu, J., Hu B. X., Soltanian, M. R., Qiu H., Yang, L. (2019) Prediction of groundwater level in seashore reclaimed land using wavelet and artificial neural network-based hybrid model. Journal of Hydrology, 577 https://doi.org/10.1016/j.jhydrol.2019.123948
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Conceptualization, F.G.; methodology, F.G. and F.D.N.; software, F.D.N.; investigation, F.D.N., F.G., R.G., and G.d.M.; writing—original draft preparation, F.D.N. and F.G.; writing—review and editing, R.G., G.d.M., F.D.N., and F.G.
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Highlights
• NARX neural networks were applied for the spring flows prediction.
• Prediction models for 9 monitored springs in the Umbria region were developed.
• Forecasting sensitivity to changes in the temporal resolution was assessed.
• Good performances for both short-term and long-term predictions were achieved.
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Di Nunno, F., Granata, F., Gargano, R. et al. Prediction of spring flows using nonlinear autoregressive exogenous (NARX) neural network models. Environ Monit Assess 193, 350 (2021). https://doi.org/10.1007/s10661-021-09135-6
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DOI: https://doi.org/10.1007/s10661-021-09135-6