Open Access
| Issue |
BIO Web Conf.
Volume 216, 2026
The 6th Sustainability and Resilience of Coastal Management (SRCM 2025)
|
|
|---|---|---|
| Article Number | 10001 | |
| Number of page(s) | 16 | |
| Section | Artificial Intelligence (AI) and Internet of Things (IoT) for Climate and Disaster Resilience | |
| DOI | https://doi.org/10.1051/bioconf/202621610001 | |
| Published online | 05 February 2026 | |
- BNPB, Jumlah kejadian bencana menurut jenis bencana. (2025). Diakses dari https://data.bnpb.go.id/dataset/data-bencana-indonesia/resource/9b41007e-c998-456b-8cbc-385b17986e46 [Google Scholar]
- S. Berkhahn, L. Fuchs, I. Neuweiler, An ensemble neural network model for real-time prediction of urban floods, J. Hydrol. 575, 743 (2019). https://doi.org/10.1016/J.JHYDROL.2019.05.066 [Google Scholar]
- M. Min, C. Bai, J. Guo, F. Sun, C. Liu, F. Wang, H. Xu, S. Tang, B. Li, D. Di, L. Dong, J. Li, Estimating Summertime Precipitation from Himawari-8 and Global Forecast System Based on Machine Learning, IEEE Trans. Geosci. Remote Sens. 57, 2557 (2019). https://doi.org/10.1109/TGRS.2018.2874950 [Google Scholar]
- M. Putra, M.S. Rosid, D. Handoko, High-Resolution Rainfall Estimation Using Ensemble Learning Techniques and Multisensor Data Integration, Sensors 24, 5030 (2024). https://doi.org/10.3390/s24155030 [Google Scholar]
- S. Kundu, S.K. Biswas, D. Tripathi, R. Karmakar, S. Majumdar, S. Mandal, A review on rainfall forecasting using ensemble learning techniques, e-Prime 6, 100296 (2023). https://doi.org/10.1016/j.prime.2023.100296 [Google Scholar]
- H. Hang, J. Mallick, S. Alqadhi, A.A. Bindajam, H.G. Abdo, Exploring forest fire susceptibility and management strategies in Western Himalaya: Integrating ensemble machine learning and explainable AI for accurate prediction and comprehensive analysis, Environ. Technol. Innov. 35, 103655 (2024). https://doi.org/10.1016/j.eti.2024.103655 [Google Scholar]
- B. Wu, P. Chen, M. Wei, Bayesian optimization-based XGBoost for performance Prediction of Carbon Nanotube Membranes. (2024). https://doi.org/10.21203/RS.3.RS-4562640/V1 [Google Scholar]
- S. Zhou, Y. Wang, Q. Yuan, L. Yue, L. Zhang, Spatiotemporal estimation of 6-hour high-resolution precipitation across China based on Himawari-8 using a stacking ensemble machine learning model, J. Hydrol. 609, 127718 (2022). https://doi.org/10.1016/j.jhydrol.2022.127718 [Google Scholar]
- G.E.A.P.A. Batista, R.C. Prati, M.C. Monard, A study of the behavior of several methods for balancing machine learning training data, ACM SIGKDD Explor. Newsl. 6, 20 (2004). https://doi.org/10.1145/1007730.1007735 [Google Scholar]
- T. Chen, C. Guestrin, XGBoost: A scalable tree boosting system, in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2016), pp. 785-794. https://doi.org/10.1145/2939672.2939785 [Google Scholar]
- A.U.G. Senocak, M.T. Yilmaz, S. Kalkan, I. Yucel, M. Amjad, An explainable twostage machine learning approach for precipitation forecast, Journal of Hydrology. 627, 130375 (2023). https://doi.org/10.1016/j.jhydrol.2023.130375 [Google Scholar]
- J. Ko, K. Lee, H. Hwang, S.G. Oh, S.W. Son, K. Shin, Effective training strategies for deep-learning-based precipitation nowcasting and estimation, Computers & Geosciences 161, 105072 (2022). https://doi.org/10.1016/J.CAGEO.2022.105072 [Google Scholar]
- B. Sohn, G. Ryu, H. Song, Observational Characteristics of Warm-Type Heavy Rainfall, Advances in Global Change Research 69, pp. 745-759 (2020). https://doi.org/10.1007/978-3-030-35798-6_15 [Google Scholar]
- C. Kidd, V. Levizzani, Status of satellite precipitation retrievals, Hydrology and Earth System Sciences 15, pp. 1109-1116 (2011). https://doi.org/10.5194/hess-15-1109-2011 [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.