Abstract
This paper focuses on a concept of using dimensionless variables as input and output to Artificial Neural Network (ANN) and discusses the improvement in the results in terms of various performance criteria as well as simplification of ANN structure for modeling rainfall-runoff process in certain Indian catchments. In the present work, runoff is taken as the response (output) variable while rainfall, slope, area of catchment and forest cover are taken as input parameters. The data used in this study are taken from six drainage basins in the Indian provinces of Madhya Pradesh, Bihar, Rajasthan, West Bengal and Tamil Nadu, located in the different hydro-climatic zones. A standard statistical performance evaluation measures such as root mean square (RMSE), Nash–Sutcliffe efficiency and Correlation coefficient were employed to evaluate the performances of various models developed. The results obtained in this study indicate that ANN model using dimensionless variables were able to provide a better representation of rainfall–runoff process in comparison with the ANN models using process variables investigated in this study.
Highlights
The rainfall-runoff relationship is one the most complex hydrological phenomenon due to the tremendous spatial and temporal variability of watershed characteristics and rainfall patterns as well as a number of variables involved in the physical processes
The results obtained in this study indicate that Artificial Neural Network (ANN) model using dimensionless variables were able to provide a better representation of rainfall–runoff process in comparison with the ANN models using process variables investigated in this study
All River Basins ANN models using process variables have been developed using all river basin data and the best identified NN architecture was 4-6-1 of model ANNPAM4 for which root mean square (RMSE) was in the range of 18.70-31.30 and NashSutcliffe efficiency was in the range of 0.689-0.907 while RMSE was in the range of 2.79-5.11, Nash-Sutcliffe efficiency was 0.45-0.73 and CC was in the range of 0.729-0.910 for model DAAM1 using dimensional analysis technique
Summary
The rainfall-runoff relationship is one the most complex hydrological phenomenon due to the tremendous spatial and temporal variability of watershed characteristics and rainfall patterns as well as a number of variables involved in the physical processes. This process is non-linear in nature and difficult to arrive at explicit solutions [1,2]. Several attempts have been made to model the non-linearity of the rainfall–runoff process, arising from intrinsic non-linearity of the rainfall–runoff process and from seasonality These rainfall-runoff models generally fall into these broad categories; namely, black box or system theoretical models, conceptual models and physically-based models [3,4,5]. A dimensional analysis technique has been developed and used to obtain mean annual flood estimation in several Indian catchments [8]
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