Runoff Prediction Using Artificial Neural
Network and SCS-CN Method: A Case
Study of Mayurakshi River Catchment,
India
Subhadeep Mandal and Sujata Biswas
Abstract Runoff estimation, as well as forecasting, is a challenging hydroclimatological topic since governing physical processes is complex, and in reality,
it is hardly represented by a system of the equations. Due to the complex nature
and extreme spatial-temporal variability of the processes which control runoff, it is
difficult to set up a reliable framework for runoff prediction and forecasting based
on available observations only. In this research, two kinds of methods have been
approached. The first one is a conceptual method, Soil Conservation Service Curve
Number (SCS-CN) method, which combines the climatic factors and watershed
parameters in one unit called the Curve Number (CN). The other method is the
Artificial Neural Network (ANN) modeling, where two different kinds of models,
Feed Forward Back Propagation (FFBP) and Cascade Forward Back Propagation
(CFBP) model have been applied. The runoff-rainfall coefficient has been chosen as
the standard parameter of the study result. Among 16 years, the year 2000 has the
highest annual, seasonal monthly total runoff (monsoon season, July to Sept.). In
artificial neural network models, generated coefficient correlation (R) values varied
from 0.96 to 0.99 range, which indicated a good correlation between the rainfallrunoff data set. The models developed for the present study can be utilized for further
basin hydrologic analysis.
Keywords Curve number · GIS · Mayurakshi catchment · Neural network ·
Rainfall-runoff
S. Mandal
School of Water Resources Engineering, IIT Kharagpur, Kharagpur, India
S. Biswas (B)
Department of Civil Engineering, IIEST Shibpur, Howrah, India
e-mail: sujata@civil.iiests.ac.in
© Springer Nature Singapore Pte Ltd. 2021
C. Bhuiyan et al. (eds.), Water Security and Sustainability,
Lecture Notes in Civil Engineering 115,
https://doi.org/10.1007/978-981-15-9805-0_4
27
Network and SCS-CN Method: A Case
Study of Mayurakshi River Catchment,
India
Subhadeep Mandal and Sujata Biswas
Abstract Runoff estimation, as well as forecasting, is a challenging hydroclimatological topic since governing physical processes is complex, and in reality,
it is hardly represented by a system of the equations. Due to the complex nature
and extreme spatial-temporal variability of the processes which control runoff, it is
difficult to set up a reliable framework for runoff prediction and forecasting based
on available observations only. In this research, two kinds of methods have been
approached. The first one is a conceptual method, Soil Conservation Service Curve
Number (SCS-CN) method, which combines the climatic factors and watershed
parameters in one unit called the Curve Number (CN). The other method is the
Artificial Neural Network (ANN) modeling, where two different kinds of models,
Feed Forward Back Propagation (FFBP) and Cascade Forward Back Propagation
(CFBP) model have been applied. The runoff-rainfall coefficient has been chosen as
the standard parameter of the study result. Among 16 years, the year 2000 has the
highest annual, seasonal monthly total runoff (monsoon season, July to Sept.). In
artificial neural network models, generated coefficient correlation (R) values varied
from 0.96 to 0.99 range, which indicated a good correlation between the rainfallrunoff data set. The models developed for the present study can be utilized for further
basin hydrologic analysis.
Keywords Curve number · GIS · Mayurakshi catchment · Neural network ·
Rainfall-runoff
S. Mandal
School of Water Resources Engineering, IIT Kharagpur, Kharagpur, India
S. Biswas (B)
Department of Civil Engineering, IIEST Shibpur, Howrah, India
e-mail: sujata@civil.iiests.ac.in
© Springer Nature Singapore Pte Ltd. 2021
C. Bhuiyan et al. (eds.), Water Security and Sustainability,
Lecture Notes in Civil Engineering 115,
https://doi.org/10.1007/978-981-15-9805-0_4
27