Can Machine Learning Help Tackle Climate Change?

There has been much discussion and debate in the scientific community regarding the efficacy and suitability of machine learning techniques to help improve our understanding of local and global environments.

Machine learning allows for predictive and probability-based calculations to be undertaken – which are useful tools for evaluating the benefits and costs of our actions in the present. It is useful for those active in climate science to understand the strengths and limitations of current machine learning techniques, as this results in better understanding and criticism of any published findings and conclusions.

What is Machine Learning?

Machine learning falls under the broader term Artificial Intelligence (AI), which is defined in a 2004 paper as “the science and engineering of making intelligent machines, in particular intelligent computer programs”. The true nature of ‘intelligence’ is hotly debated, but for this purpose, intelligence is artificial, in the sense that computer models are used to draw conclusions from complex datasets. Models are usually designed for research that would be impractical or excessively laborious to carry out with conventional analysis.

The diagram below illustrates how popular machine learning terms are related:

AI > ML > DL

It is also important to understand the following five terms:

An algorithm is a set of instructions (in this context, supplied to a computer) that transforms input information into output information. For example, calculating the carbon footprint of an organisation by assessing variables such as fuel or energy consumption, manufacturing processes, and any offset efforts.
A model is the algorithmic representation of a system (such as climate or an economy). Usually, a model comprises multiple algorithms that solve a complex problem.
Structured Data is data that is labelled, where its nature has already been determined, for example, temperature values. Classical machine learning mainly uses structured data.
Unstructured Data is data presented in raw forms, such as images. Deep learning models can operate on both structured and unstructured data to create natural language processing and visual recognition systems. However, these require higher levels of computing power than classical machine learning methods.
Neural networks are one of the most important computational techniques for machine learning. A neural network is a software model consisting of several connected nodes. Both the nodes and the connections are important. Below is a simple diagram of how neural networks can be structured.

Hidden Layer

Each network has inputs from either data or previous nodes, one or more hidden layers (algorithms that can modify the input), and an output. If a node’s algorithm produces a result that exceeds a set threshold value, then the output is activated. Each connection can also be assigned a weight to indicate how useful it is in predicting an overall result. Connections that are more useful in predicting a result receive a higher weight. Less useful connections are assigned a lower weight or may even be dropped.[…]