International Journal of Agricultural Engineering

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Performance of ANN for Rainfall-Runoff Prediction


Affiliations
1 Department of Soil and Water Conservation Engineering, College of Agricultural Engineering and Technology, Dr. Balasaheb Sawant Konkan Krishi Vidyapeeth, Dapoli (M.S.), India
     

   Subscribe/Renew Journal


The use of artificial neural network is becoming increasingly common in the analysis of hydrology and water resource problems. In present study, the observed rainfall and runoff data of four years (2010, 2011, 2013 and 2014) were used as input data. In ANN, input data was divided in three segment 70 per cent, 15 per cent and 15 per cent for training, validation and testing purpose, respectively. Rainfall-runoff relationship is an important component in water resource evaluation and therefore, the predicted runoff of 70 numbers of different types of model was tested statistically with observed runoff using statistical parameter, i.e. ischolar_main mean square error (RMSE), mean absolute error (MAE), co-efficient of determination (R2) and correlation (r). This study showed that out of 70 ANN architectures, ANN architectures 1-48-1 could be adopted to estimate runoff from ungauged watershed with rainfall as input.

Keywords

ANN, Rainfall-Runoff Modeling, Co-Efficient of Determination, Correlation.
Subscription Login to verify subscription
User
Notifications
Font Size


  • Anonymous (2000). ASCE task committee on application of the artificial neural networks in hydrology. Artificial Neural Network in Hydrology II; Preliminary Concepts. J. Hydrologic Engineering, 5 (2): 124-137.

  • Junsawang, P., Asavanant, J. and Lursinsap, C. (2005). Artificial neural network model for rainfall-runoff relationship. Advanced Virtual and Intelligent Computing Center (AVIC). Department of Mathematics, Faculty of Science, Chulalongkorn University, Bangkok, 10330, Thailand.

  • Mehendale, G. M. (2013). Study of rainfall runoff relationship using SCS-CN and ANN models for Bench Terraces, Dapoli. M. Tech Thesis, Dr. Balasaheb Sawant Konkan Krishi Vidyapeeth, Dapoli, M.S. (India).

  • Rumelhart, D. and McClelland, J. (1986). Parallel distributed processing. MIT Press, Cambridge.

  • Salunke, J.R. (2013). Rainfall- runoff modelling of Priyadarshini watershed using artificial neural network. B. Tech. Thesis, Dr. Balasaheb Sawant Konkan Krishi Vidyapeeth, Dapoli, M.S. (India).


Abstract Views: 249

PDF Views: 0




  • Performance of ANN for Rainfall-Runoff Prediction

Abstract Views: 249  |  PDF Views: 0

Authors

S. K. Kothe
Department of Soil and Water Conservation Engineering, College of Agricultural Engineering and Technology, Dr. Balasaheb Sawant Konkan Krishi Vidyapeeth, Dapoli (M.S.), India
B. L. Ayare
Department of Soil and Water Conservation Engineering, College of Agricultural Engineering and Technology, Dr. Balasaheb Sawant Konkan Krishi Vidyapeeth, Dapoli (M.S.), India
H. N. Bhange
Department of Soil and Water Conservation Engineering, College of Agricultural Engineering and Technology, Dr. Balasaheb Sawant Konkan Krishi Vidyapeeth, Dapoli (M.S.), India
S. B. Nandgude
Department of Soil and Water Conservation Engineering, College of Agricultural Engineering and Technology, Dr. Balasaheb Sawant Konkan Krishi Vidyapeeth, Dapoli (M.S.), India
D. M. Mahale
Department of Soil and Water Conservation Engineering, College of Agricultural Engineering and Technology, Dr. Balasaheb Sawant Konkan Krishi Vidyapeeth, Dapoli (M.S.), India

Abstract


The use of artificial neural network is becoming increasingly common in the analysis of hydrology and water resource problems. In present study, the observed rainfall and runoff data of four years (2010, 2011, 2013 and 2014) were used as input data. In ANN, input data was divided in three segment 70 per cent, 15 per cent and 15 per cent for training, validation and testing purpose, respectively. Rainfall-runoff relationship is an important component in water resource evaluation and therefore, the predicted runoff of 70 numbers of different types of model was tested statistically with observed runoff using statistical parameter, i.e. ischolar_main mean square error (RMSE), mean absolute error (MAE), co-efficient of determination (R2) and correlation (r). This study showed that out of 70 ANN architectures, ANN architectures 1-48-1 could be adopted to estimate runoff from ungauged watershed with rainfall as input.

Keywords


ANN, Rainfall-Runoff Modeling, Co-Efficient of Determination, Correlation.

References