Top

Environmental Earth Sciences

Published in:

01-03-2016 | Original Article

Application of integrated ARIMA and RBF network for groundwater level forecasting

Authors: Qiao Yan, Cong Ma

Published in: Environmental Earth Sciences | Issue 5/2016

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

The combination model of autoregressive integrated moving average (ARIMA) and radial basis function network (RBFN) is used for the prediction of monthly groundwater level. The ARIMA model is used to estimate the linear principal of time series, and the RBFN model is used to predict the nonlinear residuals. The proposed hybrid model is applied to forecast the monthly groundwater level fluctuations for two observation wells in the city of Xi’an, China. The monthly groundwater level data from the year 1998 to 2008 are used for training the applied models and the data from the year 2009 to 2010 are reserved for testing. Predicted data from the hybrid model are compared with those from the ARIMA model and RBFN model using the accuracy measures. The result shows that the proposed hybrid model has both the good linear fitting ability of ARIMA model and the great nonlinear mapping ability of RBFN model. The prediction accuracy rate is higher than that of any single model. Therefore, the application of the combination model in the prediction of groundwater level is effective and feasible.

previous article Responses of soil moisture and thermal conductivity to precipitation in the mesa of the Loess Plateau

next article Changes in fluid flux and hydraulic head in a geothermal confined aquifer

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Adamowski J, Chan HF (2011) A wavelet neural network conjunction model for groundwater level forecasting. J Hydrol 407:28–40CrossRef

Aksoy H, Dahamsheh A (2009) Artificial neural network models for forecasting monthly precipitation in Jordan. Stoch Env Res Risk A 23(7):913–931CrossRef

Bates JM, Granger CWJ (1969) The combination of forecasts. Oper Res Q 20(1):451–468CrossRef

Box GEP, Jenkins GM, Reinsel GC (1991) Time series analysis, forecasting and control. Prentice Hall, Englewood Cliffs

Cadenas E, Rivera W (2010) Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA-ANN model. Renew Energ 35:2732–2738CrossRef

Chaabane N (2014) A hybrid ARFIMA and neural network model for electricity price prediction. Int J Elec Power 55:187–194CrossRef

Chang FJ, Chen YC (2003) Estuary water-stage forecasting by using radial basis function neural network. J Hydrol 270(1–2):158–166CrossRef

Chen LH, Chen CT, Pan YG (2010) Groundwater level prediction using SOM-RBFN multisite model. J Hydrol Eng 15(8):624–631CrossRef

Chen LH, Chen CT, Lin DW (2011) Application of integrated back-propagation network and self organizing map for groundwater level forecasting. J Water Res Pl-ASCE 137:352–365CrossRef

DeLurgio SA (1998) Forecasting principles and applications. Tom Gasson, New York

Faruk DO (2010) A hybrid neural network and ARIMA model for water quality time series prediction. Eng Appl Artif Intel 23:586–594CrossRef

Girosi F, Poggio T (1990) Networks and the best approximation property. Biol Cybern 63(3):169–176CrossRef

Govindaraju RS, Rao AR (2000) Artificial neural networks in hydrology. Kluwer, The NetherlandsCrossRef

Haykin S (1999) Neural networks: a comprehensive foundation. Prentice Hall, New Jersey

Jiang SY, Ren ZY, Xue KM, Li CF (2008) Application of BPANN for prediction of backward ball spinning of thin-walled tubular part with longitudinal inner ribs. J Mater Process Tech 196:190–196CrossRef

Khashei M, Bijari M (2011) A novel hybridization of artificial neural networks and ARIMA models for time series forecasting. Appl Soft Comput 11:2664–2675CrossRef

Khashei M, Bijari M, Ardali GAR (2012a) Hybridization of autoregressive integrated moving average (ARIMA) with probabilistic neural networks (PNNs). Comput Ind Eng 63:37–45CrossRef

Khashei M, Bijari M, Hejazi SR (2012b) Combining seasonal ARIMA models with computational intelligence techniques for time series forecasting. Soft Comput 16:1091–1105CrossRef

Konikow LF, Kendy E (2005) Groundwater depletion: a global problem. Hydrogeol J 13(1):317–320CrossRef

Krishna B, Satyaaji Rao YR, Vijaya T (2008) Modeling groundwater levels in an urban coastal aquifer using artificial neural networks. Hydrol Process 22(8):1180–1188CrossRef

Lin GF, Chen LH (2005) Time series forecasting by combining the radial basis function network and the self-organizing map. Hydrol Process 19(10):1925–1937CrossRef

Liu H, Chen C, Tian HQ, Li Y (2012a) A hybrid model for wind speed prediction using empirical mode decomposition and artificial neural networks. Renew Energ 48:545–556CrossRef

Liu H, Tian HQ, Li YF (2012b) Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction. Appl Energ 98:415–424CrossRef

Maiti S, Tiwari RK (2014) A comparative study of artificial neural networks, Bayesian neural networks and adaptive neuro-fuzzy inference system. Environ Earth Sci 71:3147–3160. doi:10.1007/s12665-013-2702-7 CrossRef

MATLAB (2010) The MathWorks Inc., Naick, MA

Mohanty S, Jha MK, Kumar A, Sudheer KP (2010) Artificial neural network modeling for groundwater level forecasting in a river island of Eastern India. Water Resour Manag 24:1845–1865CrossRef

Noori R, Abdoli MA, Farokhnia A, Abbasi M (2009) Results uncertainty of solid waste generation forecasting by hybrid of wavelet transform-ANFIS and wavelet transform-neural network. Expert Syst Appl 36:9991–9999CrossRef

Qu FF, Zhang Q, Lu Z, Zhao CY, Yang CS, Zhang J (2014) Land subsidence and ground fissures in Xi’an, China 2005-2012 revealed by muti-band InSAR time-series analysis. Remote Sens Environ 155:366–376CrossRef

Sahho GB, Raya C (2006) Flow forecasting for a Hawaii stream using rating curves and neural networks. J Hydrol 317:63–80CrossRef

Schwenker F, Kestler HA, Palm G (2001) Three learning phases for radial-basis-function networks. Neural Netw 14(4):439–458CrossRef

Shahwan T, Odening M (2007) Computational intelligence in economics and finance. Springer, Berlin, pp 63–74CrossRef

Tsanis IK, Coulibay P, Daliakopoulos N (2008) Improving groundwater level forecasting with a feedforward neural network and linearly regressed projected precipitation. J Hydroinform 10(4):317–330CrossRef

Wasserman PD (1993) Advanced methods in neural computing. Van Nostrand Reinhold, New York

Xie T, Yu H, Wilamowski B (2011) Comparison between traditional neural and radial basis function network. In: Proceedings of the IEEE International Symposium on Industrial Electronics, pp 1194–1199

Yang ZP, Lu WX, Long YQ, Li P (2009) Application and comparison of two prediction models for groundwater levels: a case study in Western Jilin Province, China. J Arid Environ 73:487–492CrossRef

Zhang PG (2003) Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50:159–175CrossRef

Title: Application of integrated ARIMA and RBF network for groundwater level forecasting
Authors: Qiao Yan
Cong Ma
Publication date: 01-03-2016
Publisher: Springer Berlin Heidelberg
Published in: Environmental Earth Sciences / Issue 5/2016
Print ISSN: 1866-6280
Electronic ISSN: 1866-6299
DOI: https://doi.org/10.1007/s12665-015-5198-5

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Other articles of this Issue 5/2016

Cu(II) adsorption from aqueous solution using red mud activated by chemical and thermal treatment

Incorporating ancillary data into Landsat 8 image classification process: a case study in Hoa Binh, Vietnam

Changes in fluid flux and hydraulic head in a geothermal confined aquifer

Comparison study of sedimentary humic substances isolated from contrasting coastal marine environments by chemical and spectroscopic analysis

Modeling of landslide generated impulsive waves considering complex topography in reservoir area

Assessing and prioritizing environmental hazards associated with abandoned mines in Gangwon-do, South Korea: the Total Mine Hazards Index