Seasonal rainfall forecast using a Neo-Fuzzy Neuron Model

Castro, Thiago N. de; Souza, Francisco; Alves, José Maria Brabo; Pontes, Ricardo S. T.; Firmino, Mosefran B. M.; Pereira, Thiago Martini

doi:10.1109/indin.2011.6034975

Cited by 5 publications

(1 citation statement)

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“…NNARX and ANFIS [10] models were also found accurate for rainfall-runoff prediction and reliable forecasting. Model using Neuro-Fuzzy Neuron Technique [11] was designed and compared with Dynamic Downscaling Model. It is found that model produced improved results with low computation cost for seasonal rainfall forecast.…”

Section: Related Workmentioning

confidence: 99%

Development and Analysis of Artificial Neural Network Models for Rainfall Prediction by Using Time-Series Data

Mishra¹,

Soni²,

Sharma³

et al. 2018

IJISA

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Abstract-Time Series data is large in volume, highly dimensional and continuous updating. Time series data analysis for forecasting, is one of the most important aspects of the practical usage. Accurate rainfall forecasting with the help of time series data analysis will help in evaluating drought and flooding situations in advance. In this paper, Artificial Neural Network (ANN) technique has been used to develop one-month and twomonth ahead forecasting models for rainfall prediction using monthly rainfall data of Northern India. In these model, Feed Forward Neural Network (FFNN) using Back Propagation Algorithm and Levenberg-Marquardt training function has been used. The performance of both the models has been assessed based on Regression Analysis, Mean Square Error (MSE) and Magnitude of Relative Error (MRE). Proposed ANN model showed optimistic results for both the models for forecasting and found one month ahead forecasting model perform better than two months ahead forecasting model. This paper also gives some future directions for rainfall prediction and time series data analysis research.

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Section: Related Workmentioning

confidence: 99%