Awards & Nominations

Curiosity has received the following awards and nominations. Way to go!

Global Nominee

The Challenge | Make Sense Out of Mars

Develop a sensor to be used by humans on Mars.

Modelling Solar radiation on Mars Using Artificial Neural Network and determine solar belt

Using artificial neural network in order to build a model that predicts the value of solar radiation that reaches the surface of Mars in order to use it for any design for photovoltaic system and determine Solar belt.


Living on Mars may soon be a fact. colonies need a power source. The most compatible power source is photovoltaic system. The sun is available but weather conditions are necessary to be considered. average temperature is -63, pressure is 4-8.6 mb, atmosphere is very thin (1% thickness of Earth's). Atmosphere contains a lot of dust, gases and water ice clouds.

To design a technology that helps people live on Mars, we developed a model that helps to design photovoltaic systems. The model used feed-forward back-propagation artificial neural network to predict solar radiation in terms of longitude, latitude, time of the day, temperature, altitude, pressure, amount of dust and volume mixing ratio of water ice clouds. The source of Data is Mars Climate Database (MCD). In (MCD), the data sets are based on observations of the Martian atmosphere from April 1999 to July 2013 made by different orbiting instruments: The Thermal Emission Spectrometer (TES) aboard Mars Global Surveyor, The Thermal Emission Imaging System (THEMIS) aboard Mars Odyssey, and the Mars Climate Sounder (MCS) aboard Mars Reconnaissance Orbiter (MRO). In (MCD), they have access to the daily evolution of Martian Atmospheric dust loading as well as to the daily evaluation of the EUV received at Mars from the sun for Mars years 24 to 31 (earth years 1999 to 2013). The associated outputs correspond to the best representation of the Martian climate over these specific years.

Then we used the model to determine the best locations for installing photovoltaic systems and determined the solar belt which is found to be be between latitudes 20°S to 15°N.


APPELBAUM, J. & FLOOD, D. J. 1990. Solar radiation on Mars. Solar Energy, 45, 353-363.

APPELBAUM, J., LANDIS, G. A. & SHERMAN, I. 1993. Solar radiation on Mars—update 1991. Solar Energy, 50, 35-51.

BADESCU, V. 2001. Inference of atmospheric optical depth from near-surface meteorological parameters on Mars. Renewable Energy, 24, 45-57.

BADESCU, V. 2009. Available Solar Energy and Weather Forecasting on Mars Surface. Mars. Springer.




FADARE, D. 2009. Modelling of solar energy potential in Nigeria using an artificial neural network model. Applied energy, 86, 1410-1422.

GIERASCH, P. J. & GOODY, R. M. 1972. The effect of dust on the temperature of the Martian atmosphere. Journal of the Atmospheric Sciences, 29, 400-402.

GOLOMBEK, M., GRANT, J. A., PARKER, T., KASS, D., CRISP, J., SQUYRES, S. W., HALDEMANN, A., ADLER, M., LEE, W. & BRIDGES, N. 2003. Selection of the Mars Exploration Rover landing sites. Journal of Geophysical Research: Planets, 108.

GRANT, J. A., GOLOMBEK, M. P., GROTZINGER, J. P., WILSON, S. A., WATKINS, M. M., VASAVADA, A. R., GRIFFES, J. L. & PARKER, T. J. 2011. The science process for selecting the landing site for the 2011 Mars Science Laboratory. Planetary and Space Science, 59, 1114-1127.

KADIRGAMA, K., AMIRRUDDIN, A. & BAKAR, R. 2014. Estimation of solar radiation by artificial networks: east coast Malaysia. Energy Procedia, 52, 383-388.

KHATIB, T., MOHAMED, A., SOPIAN, K. & MAHMOUD, M. 2012. Solar energy prediction for Malaysia using artificial neural networks. International Journal of Photoenergy, 2012.

KUMAR, R., AGGARWAL, R. & SHARMA, J. 2015. Comparison of regression and artificial neural network models for estimation of global solar radiations. Renewable and Sustainable Energy Reviews, 52, 1294-1299.

LANDIS, G. A. & JENKINS, P. P. 2002, Dust mitigation for mars solar arrays, Photovoltaic Specialists Conference, Conference Record of the Twenty-Ninth IEEE, 2002. IEEE, 812-815.

MADELEINE, J. B., FORGET, F., MILLOUR, E., NAVARRO, T. & SPIGA, A. 2012. The influence of radiatively active water ice clouds on the Martian climate. Geophysical Research Letters, 39.

MATZ, E., APPELBAUM, J., TAITEL, Y. & FLOOD, D. 1998. Solar cell temperature on Mars. Journal of propulsion and power, 14, 119-125.

MOHANDES, M., REHMAN, S. & HALAWANI, T. 1998. Estimation of global solar radiation using artificial neural networks. Renewable Energy, 14, 179-184.

NASA. 2000. Color-coded elevations on Mars, MOLA Altimeter, MGS Mission . NASA Goddard Spaceflight Center.

NASA. 2017. Robotic Mars Exploration [Online]. National Aeronautics and Space Administration. Available: https:// 2018].

REHMAN, S. & MOHANDES, M. 2009. Estimation of diffuse fraction of global solar radiation using artificial neural networks. Energy Sources, Part A, 31, 974-984.

SÖZEN, A., ARCAKLIOǦLU, E., ÖZALP, M. & KANIT, E. G. 2004. Use of artificial neural networks for mapping of solar potential in Turkey. Applied Energy, 77, 273-286.

TYMVIOS, F. S., MICHAELIDES, S. C. & SKOUTELI, C. S. 2008. Estimation of surface solar radiation with artificial neural networks. Modeling solar radiation at the earth’s surface. Springer.

VICENTE-RETORTILLO, Á., VALERO, F., VÁZQUEZ, L. & MARTÍNEZ, G. M. 2015. A model to calculate solar radiation fluxes on the Martian surface. Journal of Space Weather and Space Climate, 5, A33.

WILLIAMS, D. D. R. 2016. Mars Fact Sheet [Online]. Greenbelt, MD 20771: NASA Goddard Space Flight Center. Available: 2018].


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