Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
