Assessment and forecasting of agricultural drought for the district of Tiruppur
Induja I, Nirmala Devi M, Radha M, Kokilavani S and Vanitha G
Drought is a key factor in agriculture, particularly in farming, as well as having a significant environmental impact. In this aspect, the focus of this research is on drought forecasting utilising the Adaptive Neuro-Fuzzy Inference System (ANFIS), a hybrid artificial neural network. The Tiruppur district's monthly precipitation data for the past 39 years was used in this study as this district is mostly dependent on the North-East Monsoon. SPI values are calculated on a three-month scale using monthly precipitation measurements. Secondly, different ANFIS forecasting models are created with their precursory period using the computed SPI value and mean precipitation value of the North-East Monsoon season. Furthermore, the RMSE, MAE, and coefficient of determination (R2) values were used to compare predicted values with actual values. The best fit model was defined as one with the lowest RMSE, MAE, and high R2 values.
How to cite this article:
Induja I, Nirmala Devi M, Radha M, Kokilavani S and Vanitha G. Assessment and forecasting of agricultural drought for the district of Tiruppur. The Pharma Innovation Journal. 2021; 10(10S): 712-718.