Open Access
Issue
ITM Web Conf.
Volume 65, 2024
International Conference on Multidisciplinary Approach in Engineering, Technology and Management for Sustainable Development: A Roadmap for Viksit Bharat @ 2047 (ICMAETM-24)
Article Number 03007
Number of page(s) 7
Section Computer Engineering and Information Technology
DOI https://doi.org/10.1051/itmconf/20246503007
Published online 16 July 2024
  1. Retsch, M., Jakob, C., Singh, M.: “Identifying relations between deep convection and the large-scale atmosphere using explainable artificialintelligence”. Journal of Geophysical Research: Atmospheres 127(3), 2021–035388 (2022) [CrossRef] [Google Scholar]
  2. Schultz, M.G., Betancourt, C., Gong, B., Kleinert, F., Langguth, M., Leufen, L.H., Mozaffari, A., Stadtler, S.: “Can deep learning beat numerical weather prediction?” Philosophical Transactions of the Royal Society A 379(2194), 20200097 (2021) [CrossRef] [Google Scholar]
  3. Jiang, S., Bevacqua, E., Zscheischler, J.: “River flooding mechanisms and their changes in europe revealed by explainable machine learning”. Hydrology and Earth System Sciences 26(24), 6339–6359 (2022) [CrossRef] [Google Scholar]
  4. Abdellaoui, I.A., Mehrkanoon, S.: “Deep multi-stations weather forecasting: explainable recurrent convolutional neural networks”. arXiv preprint arXiv:2009.11239 (2020) [Google Scholar]
  5. Arcodia, M.C., Barnes, E.A., Mayer, K.J., Lee, J., Ordonez, A., Ahn, M.S.: “Assessing decadal variability of subseasonal forecasts of opportunity using explainable AI”. Environmental Research: Climate 2(4), 045002 (2023) [CrossRef] [Google Scholar]
  6. Kaspi, M., Kuleshov, Y.: “Flood hazard assessment in australian tropical cycloneprone regions”. Climate 11(11), 229 (2023) [CrossRef] [Google Scholar]
  7. Rampal, N., Gibson, P.B., Sood, A., Stuart, S., Fauchereau, N.C., Brandolino, C., Noll, B., Meyers, T.: “High resolution downscaling with interpretable deep learning: Rainfall extremes over new Zealand”. Weather and Climate Extremes 38, 100525 (2022) [CrossRef] [Google Scholar]
  8. Wu, J., Wang, Z., Dong, J., Cui, X., Tao, S., Chen, X.: “Robust runoff prediction with explainable artificial intelligence and meteorological variables from deep learning ensemble model”. Water Resources Research 59(9), 2023–035676 (2023) [Google Scholar]
  9. Prasanth Kadiyala, S., Woo, W.L.: “Flood prediction and analysis on the relevance of features using explainable artificial intelligence”. In: 2021 2nd Artificial Intelligence and Complex Systems Conference, pp. 1–6 (2021) [Google Scholar]
  10. Ba ̧sa ̆gao ̆glu, H., Chakraborty, D., Lago, C.D., Gutierrez, L., S ̧ahinli, M.A., Giacomoni, M., Furl, C., Mirchi, A., Moriasi, D., S ̧eng ̈or, S.S.: “A review on interpretable and explainable artificial intelligence in hydroclimatic applications”. Water 14(8), 1230 (2022) [CrossRef] [Google Scholar]
  11. Dikshit, A., Pradhan, B.: “Interpretable and explainable ai (xai) model for spatial drought prediction”. Science of the Total Environment 801, 149797 (2021) [CrossRef] [Google Scholar]
  12. Jesus, S., Beĺem, C., Balayan, V., Bento, J., Saleiro, P., Bizarro, P., Gama, J.: “How can i choose an explainer? an application-grounded evaluation of post-hoc explanations”. In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, pp. 805–815 (2021) [Google Scholar]
  13. He, R., Zhang, L., Chew, A.W.Z.: “Data-driven multi-step prediction and analysis of monthly rainfall using explainable deep learning”. Expert Systems with Applications 235, 121160 (2024) [CrossRef] [Google Scholar]
  14. Senocak, A.U.G., Yilmaz, M.T., Kalkan, S., Yucel, I., Amjad, M.: “An explainable two-stage machine learning approach for precipitation forecast”. Journal of Hydrology 627, 130375 (2023) [CrossRef] [Google Scholar]
  15. Jing, X., Luo, J., Zuo, G., Yang, X.: “Interpreting runoff forecasting of long shortterm memory network: An investigation using the integrated gradient method on runoff data from the Han River basin”. Journal of Hydrology: Regional Studies 50, 101549 (2023) [CrossRef] [Google Scholar]
  16. Prasad, P.S.H., Satyanarayana, A.: “Assessment of outdoor thermal comfort during the last decade using landsat 8 imagery with machine learning tools over the three metropolitan cities of India”. Environmental Sciences Proceedings 29(1), 37 (2023) [Google Scholar]
  17. Kadiyala, S.P., Woo, W.L.: “Flood prediction and analysis on the relevance of features using explainable artificial intelligence”. arXiv preprint arXiv:2201.05046 (2022) [Google Scholar]
  18. Ma, S., Zayed, T., Xing, J., Shao, Y.: “A state-of-the-art review for the prediction of overflow in urban sewer systems”. Journal of Cleaner Production, 139923 (2023) [Google Scholar]
  19. Sun, Z., Sandoval, L., Crystal-Ornelas, R., Mousavi, S.M., Wang, J., Lin, C., Cristea, N., Tong, D., Carande, W.H., Ma, X., et al.: “A review of earth artificial intelligence”. Computers & Geosciences 159, 105034 (2022) [CrossRef] [Google Scholar]
  20. Van Straaten, C., Whan, K., Coumou, D., Hurk, B., Schmeits, M.: “Using explainable machine learning forecasts to discover subseasonal drivers of high summer temperatures in western and central Europe”. Monthly Weather Review 150(5), 1115–1134 (2022) [CrossRef] [Google Scholar]
  21. Sahakyan, M., Aung, Z., Rahwan, T.: “Explainable artificial intelligence for tabular data: A survey”. IEEE access 9, 135392–135422 (2021) [CrossRef] [Google Scholar]
  22. Vlahek, D., Mongus, D.: “An efficient iterative approach to explainable feature learning”. IEEE Transactions on Neural Networks and Learning Systems (2021) [Google Scholar]
  23. Aydin, H.E., Iban, M.C.: “Predicting and analyzing flood susceptibility using boosting-based ensemble machine learning algorithms with shapley additive explanations”. Natural Hazards 116(3), 2957–2991 (2023) [CrossRef] [Google Scholar]

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