Performance Evaluation of Regional Heatwave Prediction Using Statistical and Deep Learning Models
DOI:
https://doi.org/10.54386/jam.v28i2.3400Keywords:
Heatwave Prediction, Seasonal Segmentation, Statistical Methods, Regional Segmentation, Climate Adaptation, Sustainability, Weather-Based Decision SupportAbstract
Rising temperatures are increasing the frequency and impact of heat extreme events in India. So, this is increasing the need for early warning and climate-risk management. Heat risks and behaviors vary across regions, so national model can miss local temperature dynamics. We present a season-aware, region-wise framework to forecast daily maximum temperature and identify heatwave days in IMD defined Central India region. In Central India's severe pre-monsoon heat is recurrent and closely linked to crop-weather stress, irrigation demand, and outdoor labor exposure. Heatwaves are first categorized at grid-cell level using operational threshold-and -departure rules. Then heatwaves are aggregated to a region-day label using a spatial coverage threshold and minimum 2-day duration. In this paper, we benchmark conventional statistical time-series models against recurrent sequence models under same splits and evaluation window using national meteorological temperature data. Performance is evaluated with temperature-error metrics and event-based measures for heatwave-day detection. Results show that recurrent models give the strongest overall skill, with the LSTM model delivering the promising improvements. Seasonal statistical modeling improves over non-seasonal baselines by capturing the seasonal cycle. This framework offers a compact regional benchmark and practical guidance for region specific early warning systems. This work supports agrometeorological advisories for farm operations, water planning and heat-stress risk management during extreme heat.
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