Browsing by Author "Girisha, R."

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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.
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    Trends of seasonal and annual rainfall of semi-arid districts of Karnataka, India: application of innovative trend analysis approach
    (Springer, 2023) Chowdari, K.; Deb Barma, S.D.; Bhat, N.; Girisha, R.; Gouda, K.C.; Mahesha, A.
    Trend analysis of rainfall is often carried out in water resources management to understand its distribution over a given region. The cumulative seasonal and annual rainfall derived from monthly datasets spanning 102 years (1901–2002) for 11 districts of the semi-arid Karnataka, India, was used for the trend analysis. The two-step homogeneous test approach was carried out on all the time series. Then, lag-1 autocorrelation was conducted only on homogeneous time series. Only 78.18 % of the total time series data were detected as homogeneous, and 95.35% of time series data were found to have insignificant autocorrelation. Then, the Innovative Trend Analysis (ITA) method was applied to 43 homogeneous rainfall time series, as well as to 41 time series using the MK and SR tests, and to two time series using the mMK test. The MK and SR tests detected a significant trend in 14.63% of the time series, while the ITA method was able to detect a trend in 93.02% of the total time series data. The MK and SR tests revealed significant trends in winter and post-monsoon season precipitation for two districts, but only for one district in the case of summer and annual rainfall. No trend was identified for monsoon season precipitation. The mMK test showed a positive trend for the post-monsoon season in a district, while the ITA method revealed significant trends for all seasons in most districts. The sub-trend analysis revealed trends that traditional methods were unable to detect. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.