Machine Learning and Artificial Intelligence-Based Approaches

The skill of Artificial Intelligence (AI)-based computational mechanisms to model important nonlinear hydrological processes is addressed in this chapter. Three major themes are illustrated: (1) conventional data-based nonlinear concepts such as Box and Jenkins Models, ARX, ARIMAX, and intelligent computing tools such as LLR, ANN, ANFIS , and SVMs ; (2) the discrete wavelet transform (DWT), a powerful signal processing tool and its application in hydrology , and (3) conjunction models of DWT, namely neuro-wavelet models, Wavelet-ANFIS models, and Wavelet-SVM s. This chapter gives a detailed description of the training algorithms used in this book and points out the conceptual advantages of Levenberg–Marquardt (LM) algorithms over Broyden-Fletcher-Goldfarb-Shanno (BFGS) training algorithms and Conjugate Gradient (CG) training algorithms.

[1]  B. Kosko.  Fuzzy systems as universal approximators , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.

[2]  P. Tao,et al.  Seasonal and Nonseasonal ARMA Models in Hydrology , 1976 .

[3]  Dawei Han,et al.  Flood forecasting using support vector machines , 2007 .

[4]  Ozgur Kisi,et al.  Evapotranspiration modelling from climatic data using a neural computing technique , 2007 .

[5]  null null,et al.  Artificial Neural Networks in Hydrology. II: Hydrologic Applications , 2000 .

[6]  Ö. Kisi.  Generalized regression neural networks for evapotranspiration modelling , 2006 .

[7]  Nand Kishor,et al.  Nonlinear predictive control for a NNARX hydro plant model , 2006, Neural Computing and Applications.

[8]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[9]  K. P. Sudheer,et al.  Short‐term flood forecasting with a neurofuzzy model , 2005 .

[10]  Vincenzo Cena,et al.  Stochastic simulation of hourly global radiation sequences , 1979 .

[11]  K. P. Sudheer,et al.  Identification of physical processes inherent in artificial neural network rainfall runoff models , 2004 .

[12]  Frank Rosenblatt,et al.  PRINCIPLES OF NEURODYNAMICS. PERCEPTRONS AND THE THEORY OF BRAIN MECHANISMS , 1963 .

[13]  F. C. van Geera,et al.  An extension of Box-Jenkins transfer/noise models for spatial interpolation of groundwater head series , 1997 .

[14]  Nicholas A Alexander,et al.  Correcting data from an unknown accelerometer using recursive least squares and wavelet de-noising , 2007 .

[15]  Linda See,et al.  Applying soft computing approaches to river level forecasting , 1999 .

[16]  Yan-Fang Sang,et al.  Discrete wavelet‐based trend identification in hydrologic time series , 2013 .

[17]  L. K. Sherman.  Streamflow from rainfall by the unit-graph method , 1932 .

[18]  C. Torrence,et al.  A Practical Guide to Wavelet Analysis. , 1998 .

[19]  David Labat,et al.  Recent advances in wavelet analyses: Part 1. A review of concepts , 2005 .

[20]  Özlem Terzi,et al.  Modeling of Daily Pan Evaporation , 2005 .

[21]  K. P. Sudheer,et al.  A neuro-fuzzy computing technique for modeling hydrological time series , 2004 .

[22]  Yan-Fang Sang,et al.  A review on the applications of wavelet transform in hydrology time series analysis , 2013 .

[23]  Pao-Shan Yu,et al.  Pruning of support vector networks on flood forecasting , 2007 .

[24]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[25]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[26]  R. Fletcher.  Practical Methods of Optimization , 1988 .

[27]  Özlem Terzi,et al.  Estimating Evaporation Using ANFIS , 2006 .

[28]  Dawei Han,et al.  Identification of Support Vector Machines for Runoff Modelling , 2004 .

[29]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[30]  K. W. Chau,et al.  River stage prediction based on a distributed support vector regression , 2008 .

[31]  Simon Li,et al.  Uncertainties in real‐time flood forecasting with neural networks , 2007 .

[32]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[33]  Athanasios Sfetsos,et al.  Univariate and multivariate forecasting of hourly solar radiation with artificial intelligence techniques , 2000 .

[34]  Saman Razavi,et al.  Long‐lead seasonal rainfall forecasting using time‐delay recurrent neural networks: a case study , 2008 .

[35]  Yu Guo,et al.  Mid- and long term hydrologic forecasting for drainage area based on WNN and FRM , 2006, Sixth International Conference on Intelligent Systems Design and Applications.

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

[37]  Mohcine Zouak,et al.  A comparison of linear and neural network ARX models applied to a prediction of the indoor temperature of a building , 2004, Neural Computing & Applications.

[38]  Chuntian Cheng,et al.  Using support vector machines for long-term discharge prediction , 2006 .

[39]  Narendra Singh Raghuwanshi,et al.  Estimating Evapotranspiration using Artificial Neural Network , 2002 .

[40]  R. Penrose.  On best approximate solutions of linear matrix equations , 1956, Mathematical Proceedings of the Cambridge Philosophical Society.

[41]  F. Anctil,et al.  An exploration of artificial neural network rainfall-runoff forecasting combined with wavelet decomposition , 2004 .

[42]  Stephen Boon Kean Tan,et al.  Modelling hourly and daily open‐water evaporation rates in areas with an equatorial climate , 2007 .

[43]  Alexander J. Smola,et al.  Support Vector Regression Machines , 1996, NIPS.

[44]  Hafzullah Aksoy,et al.  Using wavelets for data generation , 2001 .

[45]  Jonathan Lawry,et al.  River Flow Modelling Using Fuzzy Decision Trees , 2002 .

[46]  Michael I. Jordan.  Serial Order: A Parallel Distributed Processing Approach , 1997 .

[47]  Yunlong Cai,et al.  Wavelet analysis of rainfall variation in the Hebei Plain , 2005 .

[48]  Peter C Young,et al.  Advances in real–time flood forecasting , 2002, Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[49]  N. Null.  Artificial Neural Networks in Hydrology. I: Preliminary Concepts , 2000 .

[50]  PAUL J. WERBOS,et al.  Generalization of backpropagation with application to a recurrent gas market model , 1988, Neural Networks.

[51]  Jose D. Salas,et al.  Multivariate Periodic ARMA(1,1) Processes , 1988 .

[52]  Dawei Han,et al.  Evaporation Estimation Using Support Vector Machines Technique , 2008 .

[53]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[54]  Jason Weston,et al.  A user's guide to support vector machines. , 2010, Methods in molecular biology.

[55]  R. Hozalski,et al.  Zone of influence of a gas permeable membrane system for delivery of gases to groundwater , 2005 .

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

[57]  Peter C. Young,et al.  Identification and estimation of continuous-time, data-based mechanistic (DBM) models for environmental systems , 2006, Environ. Model. Softw..

[58]  M. Keskin,et al.  Fuzzy logic model approaches to daily pan evaporation estimation in western Turkey / Estimation de l’évaporation journalière du bac dans l’Ouest de la Turquie par des modèles à base de logique floue , 2004 .

[59]  I. Daubechies.  Orthonormal bases of compactly supported wavelets , 1988 .

[60]  Ozgur Kisi,et al.  Reply to comment on ‘Kisi O. 2007. Evapotranspiration modelling from climatic data using a neural computing technique. Hydrological Processes 21:1925–1934’ , 2008 .

[61]  Robert M. Pap,et al.  Handbook of neural computing applications , 1990 .

[62]  Antonia J. Jones,et al.  New tools in non-linear modelling and prediction , 2004, Comput. Manag. Sci..

[63]  Z. Vryzas,et al.  Spatial and temporal distribution of pesticide residues in surface waters in northeastern Greece. , 2009, Water research.

[64]  K. P. Sudheer,et al.  Rainfall‐runoff modeling through hybrid intelligent system , 2007 .

[65]  A. Grossmann,et al.  DECOMPOSITION OF HARDY FUNCTIONS INTO SQUARE INTEGRABLE WAVELETS OF CONSTANT SHAPE , 1984 .

[66]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[67]  H. Aksoy,et al.  Annual and monthly rainfall data generation schemes , 2004 .

[68]  Asaad Y. Shamseldin,et al.  A non-linear combination of the forecasts of rainfall-runoff models by the first-order Takagi–Sugeno fuzzy system , 2001 .

[69]  Huien Han,et al.  Estimation of daily soil water evaporation using an artificial neural network , 1997 .

[70]  K. P. Sudheer,et al.  Modelling evaporation using an artificial neural network algorithm , 2002 .

[71]  Chih-Chiang Wei.  Wavelet Support Vector Machines for Forecasting Precipitation in Tropical Cyclones: Comparisons with GSVM, Regression, and MM5 , 2012 .

[72]  T. J. Chang,et al.  Application of Discrete Autoregressive Moving Average models for estimation of daily runoff , 1987 .

[73]  O. Kisi,et al.  SVM, ANFIS, regression and climate based models for reference evapotranspiration modeling using limited climatic data in a semi-arid highland environment , 2012 .

[74]  R. Penrose.  A Generalized inverse for matrices , 1955 .

[75]  Dawei Han,et al.  Rainfall-runoff modelling using a wavelet-based hybrid SVM scheme. , 2009 .

[76]  I-Fan Chang,et al.  Support vector regression for real-time flood stage forecasting , 2006 .

[77]  Ozgur Kisi,et al.  Stream flow forecasting using neuro‐wavelet technique , 2008 .

[78]  Ozgur Kisi,et al.  Evapotranspiration estimation using feed-forward neural networks , 2006 .

[79]  M. Janga Reddy,et al.  Ensemble prediction of regional droughts using climate inputs and the SVM–copula approach , 2014 .

[80]  O. Kisi,et al.  Wavelet and neuro-fuzzy conjunction model for precipitation forecasting , 2007 .

[81]  Christian W. Dawson,et al.  Hydrological modelling using artificial neural networks , 2001 .

[82]  E. Conway,et al.  Stochastic modelling and forecasting of solar radiation , 1998 .

[83]  Reza Kerachian,et al.  Characterizing an unknown pollution source in groundwater resources systems using PSVM and PNN , 2010, Expert Syst. Appl..

[84]  Yoshiki Uchikawa,et al.  On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm , 1992, IEEE Trans. Neural Networks.

[85]  E. Todini.  Using a desk-top computer for an on-line flood warning system , 1978 .

[86]  Wei Wu,et al.  Soil Water Content Forecasting by Support Vector Machine in Purple Hilly Region , 2007, CCTA.

[87]  N. Erdem Unal,et al.  Discussion of “Generalized regression neural networks for evapotranspiration modelling” , 2007 .

[88]  Marshall E. Moss,et al.  Autocorrelation structure of monthly streamflows , 1974 .

[89]  Özlem Terzi,et al.  Artificial Neural Network Models of Daily Pan Evaporation , 2006 .

[90]  M. Sharp,et al.  Wavelet analysis of inter‐annual variability in the runoff regimes of glacial and nival stream catchments, Bow Lake, Alberta , 2003 .

[91]  Hafzullah Aksoy,et al.  Nonparametric streamflow simulation by wavelet or Fourier analysis , 2001 .

[92]  Daoliang Li,et al.  IFIP Advances in information and communication technology , 2007 .

[93]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[94]  Henrik Madsen,et al.  Conditional parametric models for storm sewer runoff , 2007 .

[95]  Thong Ngee Goh,et al.  Stochastic modeling and forecasting of solar radiation data , 1977 .

[96]  William H. Press,et al.  Numerical Recipes in C, 2nd Edition , 1992 .

[97]  Emery Coppola,et al.  Comparative Study of SVMs and ANNs in Aquifer Water Level Prediction , 2010, J. Comput. Civ. Eng..

[98]  Stuart N. Lane,et al.  Assessment of rainfall‐runoff models based upon wavelet analysis , 2007 .

[99]  Dong Wang,et al.  Entropy-Based Wavelet De-noising Method for Time Series Analysis , 2009, Entropy.

[100]  Özlem Terzi,et al.  Fuzzy Logic Model Approaches to Daily Pan Evaporation Estimation in Western Turkey , 2004 .

[101]  Bernard Bobée,et al.  Daily reservoir inflow forecasting using artificial neural networks with stopped training approach , 2000 .

[102]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[103]  Demetris Koutsoyiannis,et al.  Discussion of “Generalized regression neural networks for evapotranspiration modelling” , 2007 .

[104]  R. Tuğrul Oğulata,et al.  Solar radiation on Adana, Turkey , 2002 .

[105]  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..

[106]  Ping Wang,et al.  Multiscale characteristics of the rainy season rainfall and interdecadal decaying of summer monsoon in North China , 2003 .

[107]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .