GeoClust: Feature engineering based framework for location-sensitive disaster event detection using AHP-TOPSIS
作者：
Highlights：
• Feature engineering based framework for location-sensitive disaster event detection.
• Augmentation of context-free and context-based features with place of occurrence.
• Evaluation with unsupervised machine learning algorithm with 09 performance metrics.
• AHP-TOPSIS based selection of an efficient machine learning algorithm and feature set.
• Location-augmented context-based features outperformed traditional textual features.
摘要
•Feature engineering based framework for location-sensitive disaster event detection.•Augmentation of context-free and context-based features with place of occurrence.•Evaluation with unsupervised machine learning algorithm with 09 performance metrics.•AHP-TOPSIS based selection of an efficient machine learning algorithm and feature set.•Location-augmented context-based features outperformed traditional textual features.
论文关键词：Location-sensitive disaster event detection,Feature engineering,Multiple-criteria decision making (MCDM),AHP-TOPSIS,Context-free and context-based feature sets
论文评审过程：Received 21 December 2021, Revised 16 July 2022, Accepted 5 August 2022, Available online 9 August 2022, Version of Record 18 August 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.118461
