Rainfall-Runoff Modeling of Sutlej River Basin (India) Using Soft Computing Techniques

The prediction of the runoff generated within a watershed is an important input in the design and management of water resources projects. Due to the tremendous spatial and temporal variability in precipitation, rainfall-runoff relationship becomes one of the most complex hydrologic phenomena. Under such circumstances, using soft computing approaches have proven to be an efficient tool in modeling of runoff. These models are capable of predicting river runoff values that can be used for hydrologic and hydraulic engineering design and water management purposes. It has been observed that the artificial neural networks (ANN) model performed well compared to other soft computing techniques such as fuzzy logic and radial basis function investigated in this study. In addition, comparison of scatter plots indicates that the values of runoff predicted by the ANN model are more precise than those found by RBF or Fuzzy Logic model.

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

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

[3]  Naresh Kumar,et al.  Effect of orography on precipitation in the western Himalayan region , 1997 .

[4]  Claus Rautenstrauch,et al.  Environmental information systems in industry and public administration , 2001 .

[5]  Asaad Y. Shamseldin,et al.  Development of rainfall–runoff models using Takagi–Sugeno fuzzy inference systems , 2006 .

[6]  Stephen L. Chiu,et al.  Fuzzy Model Identification Based on Cluster Estimation , 1994, J. Intell. Fuzzy Syst..

[7]  S. Simonovic,et al.  An Artificial Neural Network model for generating hydrograph from hydro-meteorological parameters , 2005 .

[8]  M. Franchini,et al.  Fuzzy unit hydrograph , 2006 .

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

[10]  Uwe Jasnoch,et al.  Metadata information systems for geospatial data , 2001 .

[11]  S. Suhaimi,et al.  Rainfall runoff modeling using radial basis function neural network for sungai Tinjar catchment, Miri, Sarawak , 2009 .

[12]  N. K. Goel,et al.  Takagi–Sugeno fuzzy inference system for modeling stage–discharge relationship , 2006 .

[13]  Karim Solaimani,et al.  A study of rainfall forecasting models based on artificial neural network. , 2009 .

[14]  Bernard De Baets,et al.  Comparison of data-driven TakagiSugeno models of rainfalldischarge dynamics , 2005 .

[15]  Mahmud Güngör,et al.  River flow estimation using adaptive neuro fuzzy inference system , 2007, Math. Comput. Simul..

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

[17]  Ozgur Kisi,et al.  Streamflow Forecasting Using Different Artificial Neural Network Algorithms , 2007 .

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

[19]  K. P. Sudheer,et al.  A data‐driven algorithm for constructing artificial neural network rainfall‐runoff models , 2002 .

[20]  Ashu Jain,et al.  Development of effective and efficient rainfall‐runoff models using integration of deterministic, real‐coded genetic algorithms and artificial neural network techniques , 2004 .

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

[22]  Paul F. Boulos,et al.  DISCRETE SIMULATION ApPROACH FOR NETWORK-WATER-QUALITY MODELS , 1995 .

[23]  Athanasios Manitsaris,et al.  Factors Affecting Customers' Perceptions and Firms' Decisions Concerning Online Fast Food Ordering , 2015, Int. J. Agric. Environ. Inf. Syst..

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

[25]  Vijay P. Singh,et al.  ANN and Fuzzy Logic Models for Simulating Event-Based Rainfall-Runoff , 2006 .

[26]  T. Sathish,et al.  River Flow Forecasting using Recurrent Neural Networks , 2004 .

[27]  Narendra Singh Raghuwanshi,et al.  Flood Forecasting Using ANN, Neuro-Fuzzy, and Neuro-GA Models , 2009 .

[28]  Ozgur Kisi,et al.  Suspended sediment concentration estimation by an adaptive neuro-fuzzy and neural network approaches using hydro-meteorological data , 2009 .

[29]  M. Priyanka,et al.  Agricultural Waste Management for Bioethanol Production , 2019, Biotechnology.

[30]  Ashu Jain,et al.  A comparative analysis of training methods for artificial neural network rainfall-runoff models , 2006, Appl. Soft Comput..

[31]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[32]  Witold F. Krajewski,et al.  Rainfall forecasting in space and time using a neural network , 1992 .

[33]  Jason Smith,et al.  Neural-Network Models of Rainfall-Runoff Process , 1995 .

[34]  T. Rientjes,et al.  Constraints of artificial neural networks for rainfall-runoff modelling: trade-offs in hydrological state representation and model evaluation , 2005 .

[35]  Zacharoula S. Andreopoulou,et al.  Exploring Information Technology and Total Quality Management Implementation by Food and Drink Manufacturing Enterprises , 2019, Int. J. Agric. Environ. Inf. Syst..

[36]  C Chandre Gowda,et al.  Runoff Modelling using different member ship functions in Adaptive Neuro fuzzy inference system , 2014 .

[37]  U. C. Kothyari,et al.  Artificial neural networks for daily rainfall—runoff modelling , 2002 .

[38]  Paresh Deka,et al.  A fuzzy neural network model for deriving the river stage—discharge relationship , 2003 .