Rainfall Forecasting Using Various Artificial Neural Network Techniques - A Review

Authors

  • Nisha Thakur  Computer Science and Engineering, Chhattisgarh Swami Vivekanand Technical university, Bhilai Institute of Technology, Chhattisgarh, India
  • Sanjeev Karmakar  Computer Science and Engineering, Chhattisgarh Swami Vivekanand Technical university, Bhilai Institute of Technology, Chhattisgarh, India
  • Sunita Soni  Computer Science and Engineering, Chhattisgarh Swami Vivekanand Technical university, Bhilai Institute of Technology, Chhattisgarh, India

DOI:

https://doi.org//10.32628/CSEIT2173159

Keywords:

Artificial Neural Network (ANN), Backpropagation algorithm, ANN Architecture, Auto-Regressive Moving Average (ARIMA), Adaptive Basis Function Neural Network (ABFNN).

Abstract

The present review reports the work done by the various authors towards rainfall forecasting using the different techniques within Artificial Neural Network concepts. Back-Propagation, Auto-Regressive Moving Average (ARIMA), ANN , K- Nearest Neighbourhood (K-NN), Hybrid model (Wavelet-ANN), Hybrid Wavelet-NARX model, Rainfall-runoff models, (Two-stage optimization technique), Adaptive Basis Function Neural Network (ABFNN), Multilayer perceptron, etc., algorithms/technologies were reviewed. A tabular representation was used to compare the above-mentioned technologies for rainfall predictions. In most of the articles, training and testing, accuracy was found more than 95%. The rainfall prediction done using the ANN techniques was found much superior to the other techniques like Numerical Weather Prediction (NWP) and Statistical Method because of the non-linear and complex physical conditions affecting the occurrence of rainfall.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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2021-06-30

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Volume 7, Issue 3, May-June-2021

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[1]
Nisha Thakur, Sanjeev Karmakar, Sunita Soni, " Rainfall Forecasting Using Various Artificial Neural Network Techniques - A Review, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 7, Issue 3, pp.506-526, May-June-2021. Available at doi : https://doi.org/10.32628/CSEIT2173159