Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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    Associative study of NDVI and precipitation in Indian region during monsoon season using satellite and ground measurements [2000-2013]
    (Institute of Electrical and Electronics Engineers Inc., 2016) Mehta, M.; Dubey, S.
    Natural vegetation cover and crop yield vary spatially and temporally in a diverse manner. Understanding this variation requires a robust analysis of important climatic factors such as rainfall, temperature, sunshine hours etc., along with LULC dynamics. In this study, NDVI has been used as an indicator of vegetative greenness and productivity. Based on 0.5°×0.5° spatial resolution data of NDVI obtained from MISR, correlation between NDVI and average seasonal precipitation has been analyzed. The precipitation data used is obtained from two sources, TRMM 3B42-V7 and IMD gridded data, both at spatial resolution of 0.25°×0.25°. The TRMM and IMD data have also been mutually correlated. Data was acquired for the months of June, July, August and September (JJAS) i.e. monsoon season for 14 years, 2000 to 2013. The correlation coefficients thus obtained are reported significant at a confidence level of 99% (p<0.001). © 2015 IEEE.
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    Evaluation of ARIMA, Facebook Prophet and a boosting algorithm framework for monthly precipitation prediction of a semi-arid district of north Karnataka, India
    (Institute of Electrical and Electronics Engineers Inc., 2022) Chowdari, K.K.; Deb Barma, S.D.; Bhat, N.; Girisha, R.; Gouda, K.C.
    This study evaluates ARIMA, Facebook Prophet and a new boosting algorithm framework known as ThymeBoost for time series prediction of monthly precipitation of Belagavi district (semi-arid) in Karnataka. The dataset was divided into three periods (1901-2002, 1951- 2002, and 1971 - 2002). The first 70% of the data for each period was applied for training while the rest for testing. Also, the datasets were used in two different forms for both training and testing. In the first set, raw data was used as it is, and the second set of data was used after normalizing the time series using the min-max concept (between 0 and 1). However, the normalized data were de-normalized for each period for performance metrics estimation. ThymeBoost is the best model for the first period of raw data and the second period of normalized data. In contrast, Prophet outperforms all other models for the normalized data in terms of all four measures. For the second period of raw data, no model emerged as the best model in terms of all performance metrics. Therefore, all three models performed similarly for the third period of raw and normalized data. © 2022 IEEE.