Abstract
A general issue in climate science is the handling of big data and running complex and computationally heavy simulations. In this paper, we explore the potential of using machine learning (ML) to spare computational time and optimize data usage. The paper analyzes the effects of changes in land cover (LC), such as deforestation or urbanization, on local climate. Along with green house gas emission, LC changes are known to be important causes of climate change. ML methods were trained to learn the relation between LC changes and temperature changes. The results showed that random forest (RF) outperformed other ML methods, and especially linear regression models representing current practice in the literature. Explainable artificial intelligence (XAI) was further used to interpret the RF method and analyze the impact of different LC changes on temperature. The results mainly agree with the climate science literature, but also reveal new and interesting findings, demonstrating that ML methods in combination with XAI can be useful in analyzing the climate effects of LC changes. All parts of the analysis pipeline are explained including data pre-processing, feature extraction, ML training, performance evaluation, and XAI.
Highlights
Published: 15 October 2021One of the peculiar features of climate science is the accumulation of an enormous amount of data [1,2]
We have presented a framework based on machine learning (ML) and XAI to analyze the effects of land cover (LC) changes on temperature
The results show that the random forest (RF) model documented better prediction performance that linear regression based models, that is the current practice in the literature [14,25]
Summary
One of the peculiar features of climate science is the accumulation of an enormous amount of data [1,2]. Information is collected by thousands of ground-based weather instruments all over the world, such as weather stations, as well as by a large number of satellites that perform measurements from kilometers above the ground. These data need to be processed and transformed to formats that are comparable with each other. Climate science multimedia systems exists, but are still underinvestigated compared to other areas such as social media or medicine [4,5]
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