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100			_aNita H. Shah1
245			_aMachine learning-based verification of satellite weather alerts for heavy rainfall in Ladakh
300			_app63-69
520			_aThe present study investigates heavy rainfall patterns and their variability in Ladakh by using K-means clustering with the Apriori algorithm, which uncovers the co-occurrence pattern of heavy rainfall alerts obtained from the ISRO MOSDAC portal. The proposed algorithms reveal that location (lat.: min. = 32.82, max. = 35.71; long.: min. = 77.59, max. = 79.91) and location (lat.: min. = 33.11, max. = 35.76; long.: min. = 75.82, max. = 77.75) exhibit the highest confidence (79.55%) and frequency (48.61%) among all patterns, indicating strong interdependencies. This suggests that the alerts in one location can potentially impact the other, offering actionable guidance for disaster preparedness. The present study highlights a significant match between predicted alerts and actual heavy rainfall events, underscoring the utility of machine learning in refining weather alert systems
654			_aApriori algorithm _adisaster management _aheavy rainfall alert _a K-means clustering.
700			_aJyoti Chahal1
773	0		_0125299 _9113439 _tCurrent Science _x 0011-3891
942			_cJA
999			_c132574 _d132574