Application of Artificial Neural Network (ANN) Model for Estimation of Runoff Yield at Kalidevi Watershed of Dhar District in Madhya Pradesh


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Authors

  • Satish K Sharma JNKVV College of Agriculture, GanjBasoda ,Vidisha 464 221 (MP)
  • MK Hardaha JNKVV, College of Agricultural Engineering , Jabalpur 482 004 MP
  • DH Ranade RVSKVV, College of Agriculture, Indore 452 001 MP

Keywords:

Artificial neural network(ANN), Antecedent precipitation index(API), Levenverg- Marquardt (LM) algorithm, Minmax, Standard deviation,Back propagation (BP)

Abstract

The runoff at any point is a complex function of watershed characteristics and hydrological parameters in space and time.  Accurate estimation of runoff yield is required for proper watershed management and other developmental work in the watershed area. The study was conducted in Kalidevi watershed of Dhar District in Madhya Pradesh. Artificial neural network (ANN) model was used and most preferred which is developed  for the estimation of runoff with input parameters.  The Daily rainfall, average temperature,  API (Antecedent precipitation index) and the days were taken as input variables for   the study period  (year 2003 to 2005) during the rainy seasons and the same were used for the analysis and application of ANN model for estimation of runoff yield for the watershed area.  The ANN model for runoff had correlation coefficient of 0.973 and found superior over other tested.

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Submitted

2023-08-03

Published

2023-08-13

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Articles

How to Cite

Satish K Sharma, MK Hardaha, & DH Ranade. (2023). Application of Artificial Neural Network (ANN) Model for Estimation of Runoff Yield at Kalidevi Watershed of Dhar District in Madhya Pradesh. Journal of Soil Salinity and Water Quality, 13(2), 228-236. https://epubs.icar.org.in/index.php/JoSSWQ/article/view/140165