Problem Overview

Efforts to understand the influence of historical climate change, at global and regional levels, have been increasing over the past decade. In particular, the estimates of air temperatures have been considered as a key factor in climate impact studies on agricultural, ecological, environmental, and industrial sectors.

Mitigating climate change is one of the biggest challenges of humankind. Despite the complexity of predicting the effects of climate change on earth, there is a scientific consensus about its negative impacts. Among them, the affectation of ecosystems, decrease of biodiversity, soil erosion, extreme changes in temperature, sea level rise, and global warming have been identified. Likewise, impacts on the economy, human health, food security and energy consumption are expected.

Specifically, air temperature forecasting has been a crucial climatic factor required for many different applications in areas such as agriculture, industry, energy, environment, tourism, etc. Some of these applications include short-term load forecasting for power utilities, air conditioning and solar energy systems development, adaptive temperature control in greenhouses, prediction and assessment of natural hazards, and prediction of cooling and energy consumption in residential buildings. Therefore, there is a need to accurately predict temperature values because, in combination with the analysis of additional features in the subject of interest, they would help to establish a planning horizon for infrastructure upgrades, insurance, energy policy, and business development. [source of information: mdpi]

Objective

Build a Machine Learning model to predict the future temperature of the city.

Evaluation Criteria

Submissions are evaluated using the Root Mean Squared Error (RMSE).

How do we do it?

Once we release the data, anyone can download it, build a model, and make a submission. We give competitors a set of data (training data) with both the independent and dependent variables.

We also release another set of data (test dataset) with just the independent variables, and we hide the dependent variable that corresponds with this set. You submit the predicted values of the dependent variable for this set and we compare it against the actual values.

The predictions are evaluated based on the evaluation metric defined in the Datathon.

Aurora RoundHacks

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