Drought plays a crucial role in agriculture, especially in farming and has a significant impact on the environment. The present study focuses on the forecast of drought using one of the hybrid artificial neural network namely the Adaptive NeuroFuzzy Inference System (ANFIS). For this study, 39 years of monthly precipitation value of Erode district are used. Firstly, using monthly precipitation values, Standard Precipitation Index (SPI) values are computed at three monthly scale since Erode district mainly depends on North-East Monsoon. Secondly, with computed SPI value and mean precipitation value of North-East Monsoon season, different ANFIS forecasting models are constructed with its precursory time period. Further, the outcome of the anticipated ANFIS model and observed values were collated using Root Mean Square Error and Mean Absolute Error values. The model with minimal RMSE value was termed as the best fit model.
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Madras
Agricultural Students Union
Tamil Nadu Agricultural University
Coimbatore - 641 003
Tamil Nadu, India.
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EDITOR-IN-CHIEF
Madras
Agricultural Students Union
Tamil Nadu Agricultural University
Coimbatore - 641 003
Tamil Nadu, India.
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