Rainfall prediction using time-delay wavelet neural network (TDWNN) model for assessing agrometeorological risk

Authors

  • MRINMOY RAY ICAR-Indian Agricultural Statistics Research Institute, New Delhi-110012, India
  • K. N. SINGH ICAR-Indian Agricultural Statistics Research Institute, New Delhi-110012, India
  • SOUMEN PAL ICAR-Indian Agricultural Statistics Research Institute, New Delhi-110012, India
  • AMIT SAHA Ministry of Statistics and Programme Implementation, Govt. of India, New Delhi-110001, India
  • KANCHAN SINHA ICAR-Indian Agricultural Statistics Research Institute, New Delhi-110012, India
  • RAJEEV RANJAN KUMAR ICAR-Indian Agricultural Statistics Research Institute, New Delhi-110012, India

DOI:

https://doi.org/10.54386/jam.v25i1.1895

Keywords:

Rainfall, Forecasting, TDWNN, TDNN

Abstract

In an agriculturally dependent nation like India, accurate and effective rainfall forecasting methods are crucial for assessing agrometeorological risk. Forecasting rainfall is perhaps one amongst the most arduous tasks in this context due to the prevalence of a non-linear pattern. One of the most promising and frequently employed approaches for forecasting rainfall data is the Time-Delay Neural Network (TDNN) model. TDNN's non-parametric, data-driven, and self-adaptive characteristics make it increasingly attractive for modelling nonlinear dynamics and generating nonlinear forecasts. Nevertheless, since the conventional TDNN uses the sigmoid activation function, there is always a chance that the training process may converge to local minima. This study addresses the usage of a Time-delay wavelet neural network (TDWNN), which employs a TDNN architecture with a hidden layer activation function derived from the orthonormal wavelet family, in order to circumvent this issue. TDWNN has been empirically demonstrated using annual rainfall data from two districts in the Indian state of West Bengal. According to the findings of this study, the TDWNN model is superior than the conventional TDNN method to assess agrometeorological risk.

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Published

17-02-2023

How to Cite

MRINMOY RAY, K. N. SINGH, SOUMEN PAL, AMIT SAHA, KANCHAN SINHA, & RAJEEV RANJAN KUMAR. (2023). Rainfall prediction using time-delay wavelet neural network (TDWNN) model for assessing agrometeorological risk. Journal of Agrometeorology, 25(1), 151–157. https://doi.org/10.54386/jam.v25i1.1895