International Conference on Technology Innovations for Crisis Management Vol.01 [ICTICM] 2026

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Subject: search.filters.subject.Disaster management

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    PublicationOpen Access
    A Data-Driven Framework for Prioritizing Post-Disaster Non-Food Relief Needs: Evidence from District-Level Analysis following the ”Dithwa” disaster in Sri Lanka
    (Sri Lanka Institute of Information Technology, 2026-05-21) Lokuliyana, S; Wijesiri, P; Jayakody, A; Peiris, H
    Effective disaster response requires timely and data-driven allocation of relief resources. This study presents a statistical analysis of district-level and camp-level non-food relief requirements following the Dithwa disaster in Sri Lanka. A consolidated dataset comprising multiple districts and relief camps was analyzed using descriptive statistics, frequency analysis, cross-tabulation, and inferential statistical tests, including the Chi-square test and Kruskal–Wallis test. The results reveal that relief demand is highly heterogeneous across districts, with a small number of regions accounting for the majority of total requirements. Shelter and bedding items dominate the demand profile, followed by water, sanitation, and hygiene (WASH) supplies, indicating significant needs related to temporary living conditions and public health. Frequently requested items such as bed sheets, blankets, and sanitary packs suggest the feasibility of developing standardized core relief packages. However, statistically significant differences across districts highlight the necessity for adaptive, location-specific allocation strategies. The findings demonstrate the value of transforming operational disaster data into structured statistical insights to support evidence-based decision-making. The proposed approach contributes to improving resource prioritization and enhancing the efficiency of humanitarian response planning in disaster-prone regions.
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    PublicationOpen Access
    Automated Flood Impact Detection from Remote Sensing Data using Transfer Learning: A New Zealand Case Study
    (Sri Lanka Institute of Information Technology, 2026-05-21) Pathirana, N; Ranasinghe, M; Prasanna, R
    Proper flood damage assessment is required for rapid emergency response and recovery planning. Highresolution drone imagery provides greater visual detail compared to satellite imagery and can support more accurate damage assessment after flood events. This work-in-progress study investigates the use of transfer learning with a pre-trained ResNet50 convolutional neural network to classify post-flood drone images. A manually annotated dataset of drone images representing damaged and non-damaged areas was used to finetune the pre-trained model and evaluate its performance using several metrics. The training process employed hyperparameter tuning to select the best model which achieved an accuracy of approximately 87%. These preliminary results demonstrate the potential of transfer learning for flood damage classification using a limited drone image dataset. Future work will focus on comparing the performance of additional deep learning models, incorporating satellite imagery and extending the approach from image-level classification to object-level damage detection.