SNDT WOMEN'S UNIVERSITY
BMK Knowledge Resource Centre
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Santacruz (West) Mumbai - 400049
Machine learning-based verification of satellite weather alerts for heavy rainfall in Ladakh
Nita H. Shah1
Machine learning-based verification of satellite weather alerts for heavy rainfall in Ladakh - pp63-69
The 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
Apriori algorithm
disaster management
heavy rainfall alert
K-means clustering.
Machine learning-based verification of satellite weather alerts for heavy rainfall in Ladakh - pp63-69
The 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
Apriori algorithm
disaster management
heavy rainfall alert
K-means clustering.