Explainability-Driven Computational Approaches in Smart Sensing Systems for Climate Observation and Emergency Anticipation

Abstract

The increasing complexity of climate-related challenges and environmental emergencies has created a critical demand for intelligent sensing systems capable of continuous observation, accurate prediction, and reliable decision support. Smart sensing infrastructures based on the Internet of Things (IoT), mobile sensing platforms, and machine learning technologies have significantly improved the ability to monitor environmental conditions, particularly in areas such as air quality assessment, pollution detection, and emergency risk forecasting. However, the growing dependence on computational models has introduced a major limitation: many advanced artificial intelligence systems operate as opaque decision-making mechanisms, reducing user confidence and limiting their adoption in critical environmental applications. This research presents an explainability-driven computational framework for smart sensing systems designed to enhance climate observation and emergency anticipation through transparent and trustworthy artificial intelligence approaches. The proposed framework integrates heterogeneous environmental sensing, machine learning-based sensor calibration, feature representation, predictive analytics, and explainable artificial intelligence mechanisms into a unified architecture. The study investigates how computational intelligence can transform raw environmental observations into interpretable knowledge while maintaining prediction accuracy and operational efficiency. Existing smart sensing approaches demonstrate strong capabilities in collecting environmental information through fixed, mobile, vehicle-based, and crowdsourced sensing networks. However, challenges related to sensor uncertainty, data inconsistency, calibration errors, and lack of model transparency remain significant barriers to practical deployment. The framework emphasizes that explainability should be incorporated throughout the complete sensing lifecycle, including data acquisition, preprocessing, model training, prediction generation, and emergency decision support. Interpretable computational mechanisms allow environmental stakeholders to understand the influence of different factors contributing to climate observations and risk predictions. Hasan et al. (2025) demonstrated the importance of interpretable AI models in IoT-enabled environmental monitoring and disaster risk forecasting, highlighting that transparent decision-making improves reliability and user acceptance of intelligent environmental systems.