Mars weather data analysis using machine learning techniques

Curiosity of the human mind and the possibility of settlement in other planets to decrease the likelihood of human extinction have acted as a catalyst in the colonization mission of the planet Mars. Exploration, colonization and human missions to the planet are being supported by many public space agencies. Although there are several factors like toxic soil, low gravity, radiation exposures etc. that rule out the possibility of colonization, the presence of polar ice caps gives abundant hope to scientists towards making Mars habitable. Colonizing the planet also considers factors like atmosphere, soil, water content etc., and there seems to be an ongoing debate on how to make the planet habitable for mankind. In order to strengthen or weaken the claim there is a necessity to explore many other factors that may contribute to Mars’ colonization in the future. Weather is one such factor worth exploring. In this paper we present some artificial intelligence techniques for analyzing Martian weather data. We rely on machine learning models like Convolution Neural Networks (CNN), Gated Recurrent Units (GRU), Long Short Term Memory (LSTM), stacked LSTM, and CNN-LSTM models to analyze the red planet’s weather data. The models have been validated using statistical parameters such as MAE, MSE, RMSE and R-squared coefficient. Our analysis reports that the LSTM model outperforms all the baseline models with the R-squared value as 0.8640, and the MAE value as 0.1257.