Context:

The JUNON project, driven by the BRGM, is granted from the Centre-Val de Loire region through ARD program (« Ambition Recherche Développement ») which goal is to develop a research & innovation pole around environmental resources (agriculture, forest, waters…). The main goal of JUNON is to elaborate digital services through large scale digital twins in order to improve the monitoring, understanding and prediction of environmental resources evolution and phenomena, for a better management of natural resources. Digital twins will allow to virtually reproduce natural processes and phenomena using combination of AI and environmental tools.

JUNON will focus on the elaboration of digital twins concerning quality and quantity of ground waters, as well as emissions of greenhouse gases and pollutants with health effects, at the scale of geographical area corresponding to the North part of the Centre-Val-de-Loire region.

Goals:

The goal of this internship will be to benchmark state of the art time series approaches and to propose new methods adapted to the specificities of the environmental data studied (multivariate time series). The benchmark on water resources relies on complex data with different seasonality and frequencies. Forecasting must be from short term to long term predictions. Regarding air pollutants, the benchmark is still to be elaborated.

Skills:

- a good experience in data analysis and machine learning (in python) is required

- some knowledge and experiences in deep learning and associated tools is required

- some knowledge in time series analysis and forecasting will be highly considered

- curiosity and ability to communicate and share your progress and to make written reports and presentations

- ability to propose solutions

- autonomy and good organization skills

How to candidate:

Send the following documents by e-mail to nicolas.ragot [at] univ-tours.fr before 13 of January 2025: a CV, a motivation letter with a short description of projects you worked on and that are related to the topic, your scores including bachelor degree, and references from teachers or people you worked with.

Bibliography:

- G. Zerveas, S. Jayaraman, D. Patel, A. Bhamidipaty, C. Eickhoff. A Transformer-based Framework for Multivariate Time Series Representation Learning. arXiv:2010.02803

- Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., & Sun, L. (2023). Transformers in Time Series: A Survey. https://github.com/qingsongedu/time-series-transformers-review

- Casolaro, A., Capone, V., Iannuzzo, G., & Camastra, F. (2023). Deep Learning for Time Series Forecasting: Advances and Open Problems. In Information (Switzerland) (Vol. 14, Issue 11). MDPI. https://doi.org/10.3390/info14110598

- Gong, Z., Tang, Y., & Liang, J. (2023). PatchMixer: A Patch-Mixing Architecture for Long-Term Time Series Forecasting. http://arxiv.org/abs/2310.00655

- Lim, B., Arik, S. O., Loeff, N., & Pfister, T. (2019). Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting. http://arxiv.org/abs/1912.09363

- Ren, X., Li, X., Ren, K., Song, J., Xu, Z., Deng, K., & Wang, X. (2021). Deep Learning-Based Weather Prediction: A Survey. Big Data Research, 23, 100178. https://doi.org/10.1016/J.BDR.2020.100178

- Tao, H., Hameed, M. M., Marhoon, H. A., Zounemat-Kermani, M., Heddam, S., Sungwon, K., Sulaiman, S. O., Tan, M. L., Sa’adi, Z., Mehr, A. D., Allawi, M. F., Abba, S. I., Zain, J. M., Falah, M. W., Jamei, M., Bokde, N. D., Bayatvarkeshi, M., Al-Mukhtar, M., Bhagat, S. K., … Yaseen, Z. M. (2022). Groundwater level prediction using machine learning models: A comprehensive review. In Neurocomputing (Vol. 489, pp. 271–308). Elsevier B.V. https://doi.org/10.1016/j.neucom.2022.03.014

- Uc-Castillo, J. L., Marín-Celestino, A. E., Martínez-Cruz, D. A., Tuxpan-Vargas, J., & Ramos-Leal, J. A. (2023). A systematic review and meta-analysis of groundwater level forecasting with machine learning techniques: Current status and future directions. In Environmental Modelling and Software (Vol. 168). Elsevier Ltd. https://doi.org/10.1016/j.envsoft.2023.105788

- Zhang, B., Rong, Y., Yong, R., Qin, D., Li, M., Zou, G., & Pan, J. (2022). Deep learning for air pollutant concentration prediction: A review. Atmospheric Environment, 290, 119347. https://doi.org/10.1016/J.ATMOSENV.2022.119347