Artificial neural networks and high and low flows in various climate regimes

Abstract An algorithm coupling linear least squares and simplex optimization (LLSSIM) is used to examine the ability of a three-layer feedforward artificial neural network (ANN) to simulate the high and low flows in various climate regimes over a mountainous catchment (the Mesochora catchment in central Greece). The plot of the long-term annual catchment pseudo-precipitation (rain plus snowmelt) simulated by the snow accumulation and ablation model (SAA) of the US National Weather Service (US NWS) showed trends of three climatically distinct periods, described by clearly descending, rising and moderately descending segments in pseudo-precipitation. The ANN model was calibrated for each of the three climate types and each was validated against the others. A set of statistical measures and graphs adapted for high and low flows showed the robustness of the ANN model under various climates and transient conditions. The ANN model proved capable of simulating well the daily high and low flows when it is calibrated for increasing pseudo-precipitation and validated for moderately decreasing pseudo-precipitation. For the entire period, the ANN model provided a better simulation of high and low flows than the conceptual soil moisture accounting (SMA) model of the US NWS, which was also employed in this study. Because the ANN is not a physically-based model, it is by no means a substitute for the SMA model. However, it is concluded that the ANN approach is an effective alternative for daily high- and low-flow simulation and forecasting in climatically varied regimes, particularly in cases where the internal dynamics of the catchment do not require an explicit representation.

[1]  E. Hansen,et al.  NUMERICAL SIMULATION OF THE RAINFALL-RUNOFF PROCESS ON A DAILY BASIS , 1973 .

[2]  Tiesong Hu,et al.  A Modified Neural Network for Improving River Flow Prediction , 2005 .

[3]  Elfatih A. B. Eltahir,et al.  El Niño and the Natural Variability in the Flow of the Nile River , 1996 .

[4]  Christian W. Dawson,et al.  An artificial neural network approach to rainfall-runoff modelling , 1998 .

[5]  Dionysia Panagouoa.  Hydrological response of a medium-sized mountainous catchment to climate changes , 1992 .

[6]  R. Abrahart,et al.  Comparing neural network and autoregressive moving average techniques for the provision of continuous river flow forecasts in two contrasting catchments , 2000 .

[7]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[8]  Orazio Giustolisi,et al.  Improving generalization of artificial neural networks in rainfall–runoff modelling / Amélioration de la généralisation de réseaux de neurones artificiels pour la modélisation pluie-débit , 2005 .

[9]  Eric A. Anderson,et al.  National Weather Service river forecast system: snow accumulation and ablation model , 1973 .

[10]  Robert M. Hirsch,et al.  SELECTION OF METHODS FOR THE DETECTION AND ESTIMATION OF TRENDS IN WATER QUALITY , 1991 .

[11]  E. L. Peck,et al.  Catchment modeling and initial parameter estimation for the National Weather Service River Forecast System , 1976 .

[12]  D. Helsel,et al.  Statistical analysis of hydrologic data. , 1992 .

[13]  G. Cavadias,et al.  Regional hydrological effects of climate change , 1991 .

[14]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[15]  D. Panagoulia,et al.  Sensitivity of flood events to global climate change , 1997 .

[16]  V. Klemeš.  The Hurst Phenomenon: A puzzle? , 1974 .

[17]  Ana P. Barros,et al.  Quantitative flood forecasting using multisensor data and neural networks , 2001 .

[18]  Leonard M. Lye,et al.  a New Look at Flood Risk Determination , 1989 .

[19]  C. Deser,et al.  Amazon River Discharge and Climate Variability: 1903 to 1985 , 1989, Science.

[20]  Ercan Kahya,et al.  U.S. streamflow patterns in relation to the El Niño/Southern Oscillation , 1993 .

[21]  A. Soldati,et al.  Artificial neural network approach to flood forecasting in the River Arno , 2003 .

[22]  H. E. Hurst,et al.  Long-Term Storage Capacity of Reservoirs , 1951 .

[23]  I. Rodríguez‐Iturbe,et al.  Random Functions and Hydrology , 1984 .

[24]  Sevket Durucan,et al.  River flow prediction using artificial neural networks: generalisation beyond the calibration range. , 2000 .

[25]  Tawatchai Tingsanchali,et al.  Application of tank, NAM, ARMA and neural network models to flood forecasting , 2000 .

[26]  J. ...,et al.  Applied modeling of hydrologic time series , 1980 .

[27]  Peter Jacobsen,et al.  Artificial neural networks as a tool in urban storm drainage , 1997 .

[28]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[29]  D. Stephenson,et al.  Criteria for comparison of single event models , 1986 .

[30]  D. Panagoulia,et al.  Linking space–time scale in hydrological modelling with respect to global climate change: Part 1. Models, model properties, and experimental design , 1997 .

[31]  Kuolin Hsu,et al.  Artificial Neural Network Modeling of the Rainfall‐Runoff Process , 1995 .

[32]  N. Nicholls.  Sea surface temperatures and Australian winter rainfall , 1989 .

[33]  K. Lam,et al.  River flow time series prediction with a range-dependent neural network , 2001 .

[34]  P. Young,et al.  Recursive estimation: A unified approach to the identification estimation, and forecasting of hydrological systems , 1985 .

[35]  Upmanu Lall,et al.  The Great Salt Lake: A Barometer of Low-Frequency Climatic Variability , 1995 .

[36]  Peter K. Kitanidis,et al.  Adaptive filtering through detection of isolated transient errors in rainfall‐runoff models , 1980 .

[37]  Dennis P. Lettenmaier,et al.  Hydrologic sensitivities of the Sacramento‐San Joaquin River Basin, California, to global warming , 1990 .

[38]  A. Soldati,et al.  River flood forecasting with a neural network model , 1999 .

[39]  M. Dettinger,et al.  Large-Scale Atmospheric Forcing of Recent Trends toward Early Snowmelt Runoff in California , 1995 .

[40]  M. Franchini,et al.  Comparative analysis of several conceptual rainfall-runoff models , 1991 .

[41]  D. Ramírez,et al.  ANALYSIS OF UNCERTAINTY , 1998 .

[42]  S. Burges,et al.  An assessment of a conceptual rainfall‐runoff model's ability to represent the dynamics of small hypothetical catchments: 1. Models, model properties, and experimental design , 1990 .

[43]  H. Maier,et al.  The Use of Artificial Neural Networks for the Prediction of Water Quality Parameters , 1996 .

[44]  N. Crawford,et al.  DIGITAL SIMULATION IN HYDROLOGY' STANFORD WATERSHED MODEL 4 , 1966 .

[45]  George E. P. Box,et al.  Time Series Analysis: Forecasting and Control , 1977 .

[46]  R. Abrahart,et al.  Detection of conceptual model rainfall—runoff processes inside an artificial neural network , 2003 .

[47]  Nachimuthu Karunanithi,et al.  Neural Networks for River Flow Prediction , 1994 .

[48]  Gail M. Brion,et al.  A neural network approach to identifying non-point sources of microbial contamination , 1999 .

[49]  Murray Smith,et al.  Neural Networks for Statistical Modeling , 1993 .

[50]  Hikmet Kerem Cigizoglu,et al.  Estimation, forecasting and extrapolation of river flows by artificial neural networks , 2003 .

[51]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[52]  D. Bae,et al.  Climatic variability of soil water in the American Midwest: Part 2. Spatio-temporal analysis , 1994 .

[53]  Kevin E. Trenberth,et al.  Origins of the 1988 North American Drought , 1988, Science.

[54]  Peter K. Kitanidis,et al.  Real‐time forecasting with a conceptual hydrologic model: 1. Analysis of uncertainty , 1980 .

[55]  S. Thomas Ng,et al.  A Modified Neural Network for Improving River Flow Prediction/Un Réseau de Neurones Modifié pour Améliorer la Prévision de L'Écoulement Fluvial , 2005 .

[56]  J. R. Wallis,et al.  Noah, Joseph, and Operational Hydrology , 1968 .

[57]  W. L. Lane,et al.  Applied Modeling of Hydrologic Time Series , 1997 .

[58]  Qing Zhang,et al.  Forecasting raw-water quality parameters for the North Saskatchewan River by neural network modeling , 1997 .

[59]  V. Rao Vemuri,et al.  Artificial Neural Networks - Forecasting Time Series , 1993 .

[60]  Philipp Slusallek,et al.  Introduction to real-time ray tracing , 2005, SIGGRAPH Courses.

[61]  Harry F. Lins,et al.  Streamflow Variability in the United States: 1931–78 , 1985 .

[62]  Francesco Lisi,et al.  Statistical considerations on the randomness of annual maximum daily rainfall , 1997 .

[63]  Vijay P. Singh,et al.  Hydrologic Systems: Rainfall-Runoff Modeling , 1988 .

[64]  A. Bradley.  Regional frequency analysis methods for evaluating changes in hydrologic extremes , 1998 .

[65]  H. Raman,et al.  Multivariate modelling of water resources time series using artificial neural networks , 1995 .

[66]  J. L. Crespo,et al.  Drought estimation with neural networks , 1993 .

[67]  J. Němec,et al.  Sensitivity of water resource systems to climate variation , 1982 .

[68]  E. Todini.  Rainfall-runoff modeling — Past, present and future , 1988 .

[69]  D. Panagoulia.  Hydrological modelling of a medium-size mountainous catchment from incomplete meteorological data , 1992 .

[70]  Nazif Tepedelenlioglu,et al.  A fast new algorithm for training feedforward neural networks , 1992, IEEE Trans. Signal Process..

[71]  Roy W. Koch,et al.  Surface Climate and Streamflow Variability in the Western United States and Their Relationship to Large‐Scale Circulation Indices , 1991 .

[72]  D. Panagoulia.  Impacts of GISS-modelled climate changes on catchment hydrology , 1992 .