Confirmation of Candidature - Explainable-AI Driven Deep Learning Modelling to Improve air Temperature dry Bulb Forecasts

Ambient air temperature or dry bulb temperature is crucial for many applications such as short-term electrical load forecasting and subsequent energy generation. In water resources management and agricultural sector, dry bulb temperature modulates the evaporation, evapotranspiration, and soil moisture. For the health and tourism sectors, extreme dry bulb temperature magnitudes are a precursor to cardiovascular disease and cerebrovascular mortality while extreme low temperatures cause hypothermia.
The health indicators such as energy heat index or heat index are derived from dry bulb temperature. In addition, the wildfire risk is also contingent upon the dry bulb temperature as well. Moreover, the infrastructure planning and urban development is also dependent on dry bulb temperature as it is extremely relevant in determining the urban heat island effect. Hence, for appropriate informed decision making and mitigation actions, it is imperative to have an accurate dry bulb temperature forecasting and early warning system to avert the looming issues.

The main objective of this research is to develop Explainable-AI methods based on deep learning techniques for forecasting air temperature Dry Bulb (DB), while also establishing meaningful interpretability and explainability. This study aims to bridge the gap between the complexity of deep learning models and the need for transparent, interpretable insights into climate change phenomena. The research aims to provide valuable information to support researchers and the public in making informed decisions regarding climate change mitigation and adaptation strategies.

This research employs a multifaceted approach to develop Explainable-AI methods based on deep learning techniques for analysing air temperature. There are various methods available to create advanced Deep Learning models for predicting long-term weather patterns such as precipitation, temperature, Explainable-AI driven deep learning modelling to improve air temperature dry bulb forecasts v | Research Proposal for Confirmation of Candidature drought, and evapotranspiration.

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