Daily groundwater level fluctuation forecasting using soft computing technique

The study presented here deals with forecasting daily groundwater level fluctuation (GLF) for monitoring of GLF pattern. The calculation model is based on the adaptive neuro-fuzzy inference system (ANFIS) and two algorithms of artificial neural networks (ANN) models, namely Levenberg- Marquardt (LM) and radial basis function (RBF). The objective in this study is to predict daily GLF for monitoring purposes. The first step was to investigate the effect of the number time lags as inputs for one- day-ahead prediction using the ANFIS algorithm. It was found that three input nodes containing three time- lag of well studied gave good prediction results. The second experiment was to predict the GLF one to seven steps ahead using the three input nodes. In this experiment, the three soft computing techniques were applied. The results indicate that the performances were decreasing by increasing the time step ahead, and in general there was no significant difference between the three techniques used. The best accuracy was for one-day-ahead prediction. The results obtained in this study suggest that GLF monitoring can be conducted by a forecasting model with considering time-lag as inputs. (Nature and Science. 2007;5(2):1-10).

[1]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[2]  Gwo-Fong Lin,et al.  A non-linear rainfall-runoff model using radial basis function network , 2004 .

[3]  P. Gelder,et al.  Forecasting daily streamflow using hybrid ANN models , 2006 .

[4]  ARTIFICIAL NEURAL NETWORKS IN HYDROLOGY . I : PRELIMINARY CONCEPTS By the ASCE Task Committee on Application of Artificial Neural Networks in Hydrology , 2022 .

[5]  Paulin Coulibaly,et al.  Groundwater level forecasting using artificial neural networks , 2005 .

[6]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[7]  I. Zaheer,et al.  APPLICATION OF ARTIFICIAL NEURAL NETWORK FOR WATER QUALITY MANAGEMENT , 2003 .

[8]  P. K. Kenabatho,et al.  Forecasting runoff coefficients using ANN for water resources management: The case of Notwane catchment in Eastern Botswana , 2006 .

[9]  P. C. Nayak,et al.  A neuro-fuzzy computing technique for modeling hydrological time series , 2004 .

[10]  B. Adams,et al.  Integration of artificial neural networks with conceptual models in rainfall-runoff modeling , 2006 .

[11]  Saro Lee,et al.  Determination and application of the weights for landslide susceptibility mapping using an artificial neural network , 2004 .

[12]  Fakhri Karray,et al.  Minimizing variance of reservoir systems operations benefits using soft computing tools , 2003, Fuzzy Sets Syst..

[13]  R. Govindaraju,et al.  Geomorphology-based artificial neural networks (GANNs) for estimation of direct runoff over watersheds , 2003 .

[14]  Shang-Lien Lo,et al.  Diagnosing reservoir water quality using self-organizing maps and fuzzy theory. , 2002, Water research.

[15]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[16]  V. Singh,et al.  Fuzzy logic algorithm for runoff-induced sediment transport from bare soil surfaces , 2003 .

[17]  U. C. Kothyari,et al.  Modeling of the daily rainfall-runoff relationship with artificial neural network , 2004 .

[18]  Ozgur Kisi,et al.  Suspended sediment estimation using neuro-fuzzy and neural network approaches/Estimation des matières en suspension par des approches neurofloues et à base de réseau de neurones , 2005 .

[19]  Fi-John Chang,et al.  Adaptive neuro-fuzzy inference system for prediction of water level in reservoir , 2006 .

[20]  Holger R. Maier,et al.  Neural networks for the prediction and forecasting of water resource variables: a review of modelling issues and applications , 2000, Environ. Model. Softw..

[21]  Ashish Sharma,et al.  A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting , 2000 .

[22]  Ferenc Szidarovszky,et al.  Application of Artificial Neural Networks to Complex Groundwater Management Problems , 2003 .

[23]  N. J. Ferreira,et al.  Artificial neural network technique for rainfall forecasting applied to the São Paulo region , 2005 .

[24]  J. Eheart,et al.  Neural network-based screening for groundwater reclamation under uncertainty , 1993 .

[25]  Stefano Alvisi,et al.  Water level forecasting through fuzzy logic and artificial neural network approaches , 2005 .

[26]  Yen-Ming Chiang,et al.  Comparison of static-feedforward and dynamic-feedback neural networks for rainfall -runoff modeling , 2004 .

[27]  P. C. Nayak,et al.  Groundwater Level Forecasting in a Shallow Aquifer Using Artificial Neural Network Approach , 2006 .

[28]  E. Toth,et al.  Comparison of short-term rainfall prediction models for real-time flood forecasting , 2000 .

[29]  Ebrahim Mamdani,et al.  Applications of fuzzy algorithms for control of a simple dynamic plant , 1974 .

[30]  S. Murty Bhallamudi,et al.  Optimal Groundwater Management in Deltaic Regions using Simulated Annealing and Neural Networks , 2003 .

[31]  B. Tutmeza,et al.  Modelling electrical conductivity of groundwater using an adaptive neuro-fuzzy inference system , 2006 .

[32]  ARTIFICIAL NEURAL NETWORKS IN HYDROLOGY . II : HYDROLOGIC APPLICATIONS By the ASCE Task Committee on Application of Artificial Neural Networks in Hydrology , 2022 .

[33]  B. S. Thandaveswara,et al.  A non-linear rainfall–runoff model using an artificial neural network , 1999 .

[34]  Tamás D. Gedeon,et al.  Rainfall prediction model using soft computing technique , 2003, Soft Comput..

[35]  B. Bobée,et al.  Artificial neural network modeling of water table depth fluctuations , 2001 .

[36]  Hiromitsu Saegusa,et al.  Runoff analysis in humid forest catchment with artificial neural network , 2000 .

[37]  S. Lallahem,et al.  On the use of neural networks to evaluate groundwater levels in fractured media , 2005 .

[38]  Hiroyuki Ohno,et al.  Effect of bridge construction on floodplain hydrology—assessment by using monitored data and artificial neural network models , 2004 .

[39]  Theodore B. Trafalis,et al.  Data mining techniques for improved WSR-88D rainfall estimation , 2002 .

[40]  Kuolin Hsu,et al.  Improved streamflow forecasting using self-organizing radial basis function artificial neural networks , 2004 .