EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 576 (2024) E3S Web Conf., 576 (2024) 03003 References
Open Access
Issue
E3S Web Conf.
Volume 576, 2024
The 13th Engineering International Conference “Sustainable Development Through Green Engineering and Technology” (EIC 2024)
Article Number 03003
Number of page(s) 12
Section Natural Disaster Mitigation
DOI https://doi.org/10.1051/e3sconf/202457603003
Published online 03 October 2024
  1. R. Wigati and S. Oktaviani, Analisis kekeringan dengan menggunakan metode theory of run studi kasus DAS Ciujung, Jis, 1, 2, pp. 1–113 (2016) [CrossRef] [Google Scholar]
  2. R. Neritarani, Identifikasi dan strategi mitigasi bencana kekeringan potensial di Kabupaten Semarang, Plano Madani : Jpwk, 8, 1, pp. 72–84 (2019) [Google Scholar]
  3. C. M. Botai et al., Hydrological drought assessment based on the standardized streamflow index: A case study of the three cape provinces of South Africa, Water (Switzerland), 13, 24 (2021). https://doi.org/10.3390/w13243498 [Google Scholar]
  4. K. Sundararajan et al., A contemporary review on drought modeling using machine learning approaches, CMES Computer Modeling in Engineering and Sciences, 128, 2, pp. 447–487 (2021). https://doi.org/10.32604/cmes.2021.015528 [CrossRef] [Google Scholar]
  5. Puslitbang Sumber Daya Air, Naskah Ilmiah Analisa Kekeringan Untuk Pengelolaan Sumber Daya Air, Kpu, 22, pp. 1–48 (2014) [Google Scholar]
  6. A. P. Dewi, S. Sangkawati, and S. Edhisono, Analisis lokasi embung berdasarkan kriteria SPI berbasis sistem informasi geografis, Rtj, 5, 2, pp. 281–290 (2022) [CrossRef] [Google Scholar]
  7. G. A. Dipayana, A. Cahyadi, and E. Nurjani, Analisis trend kejadian kekeringan di sebagian wilayah Povinsi DI Yogyakarta dan dampak el-nino terhadapnya, Sng, 5, (2014) [Google Scholar]
  8. A. Mokhtar et al., Estimation of SPEI meteorological drought using machine learning algorithms, IEEE Access, 9, pp. 65503–65523 (2021). https://doi.org/10.1109/ACCESS.2021.3074305 [CrossRef] [Google Scholar]
  9. S. Suhaedi, E. Febriana, H. RPN, and I. Ardiansyah, ANN back propagation for forecasting and simulation hydroclimatology data, Aula Handayani IKIP Mataram, pp. 130–143 (2017) [Google Scholar]
  10. W. Almikaeel, L. Čubanová, and A. Šoltész, Hydrological drought forecasting using machine learning—Gidra River case study, Water (Switzerland), 14, 3 (2022). https://doi.org/10.3390/w14030387 [Google Scholar]
  11. R. R. Hadiani, B. Suharto, A. Suharyanto, and Suhardjono, Hydrological drought index based on discharge, IntechOpen, Drought-Impacts and Management (2022) https://dx.doi.org/10.5772/intechopen.104625 [Google Scholar]
  12. I. W. Yasa, A. Setiawan, I. D. G. J. Negara, H. Saidah, and A. H. Dirgantara, Sebaran kekeringan hidrologi berdasarkan debit aliran di Kabupaten Bima, Jgs, 17, September 2022, pp. 72–80 (2023) [Google Scholar]
  13. Kementerian Pekerjaan Umum Direktorat Jenderal Sumber Daya Air, Standar Perencanaan Irigasi, Kpu, pp. 1–253 (2013) [Google Scholar]
  14. S. Salahuddin, S. Anwar, and H. Mulyono, Performance Analysis of Irrigation Area in Pemali Brebes, Jgst, IV, 3, pp. 129–135 (2020) [Google Scholar]
  15. T. S. Bubarna, Y. Feriska, and A. Khamid, Efektifitas Pemberian Air Permukaan dari Bendung Notog ke Saluran Sekunder Gegerkunci Kabupaten Brebes (Studi Kasus Saluran Sekunder Gegerkunci), Era Sains: Jurnal Penelitian Sains, Keteknikan dan Informatika, 1, 3, pp. 42–53 (2023). https://jurnal.eraliterasi.com/index.php/erasains/article/view/109 [Google Scholar]
  16. W. Hatmoko, R. W. Triweko, and I. K. Hadihardaja, Kekeringan semakin sering terjadi pada daerah irigasi di Jawa Tengah, INACID (2013) [Google Scholar]
  17. W. Hatmoko, R. W. Triweko, and I. K. Hadihardaja, Kekeringan pada bendung irigasi di wilayah sungai Pemali-Comal, January (2013) [Google Scholar]
  18. B. D. Purwantoro, R. Hadiani, and Solichin, Analisis kekeringan hidrologi berdasarkan metode ambang batas (threshold level method), e-Jurnal Matriks Teknik Sipil, pp. 42–50, (2019) [Google Scholar]
  19. M. Jehanzaib, S. A. Shah, H. J. Son, S. H. Jang, and T. W. Kim, Predicting hydrological drought alert levels using supervised machine learning classifiers, KSCE Journal of Civil Engineering, 26, 6, pp. 3019–3030 (2022). https://doi.org/10.1007/s12205-022-1367-8 [CrossRef] [Google Scholar]
  20. O. Aiyelokun, A. Ojelabi, and O. Agbede, Performance evaluation of machine learning models in predicting dry and wet climatic phases, Jsc in Civil Engineering, 4, 1, pp. 29–48 (2020). https://doi.org/10.22115/scce.2020.213319.1154 [Google Scholar]
  21. E. Dzisofi Amelia, S. Wahyuni, and D. Harisuseno, Evaluasi Kesesuaian Data Satelit sebagai Alternatif Ketersediaan Data Evaporasi di Waduk Wonorejo, Jtp, 12, 2, pp. 127–138 (2021). https://doi.org/10.21776/ub.pengairan.2021.012.02.05 [Google Scholar]
  22. Y. Sari, Y. F. Arifin, N. Novitasari, and M. R. Faisal, Deep Learning Approach Using the GRU-LSTM Hybrid Model for Air Temperature Prediction on Daily Basis, International Journal of Intelligent Systems and Applications in Engineering, 10, 3, pp. 430–436 (2022) [Google Scholar]
  23. B. D. Primadita et al., Studi Komparasi Peta Bahaya Kekeringan Meteorologi dan Hidrologi WS Pemali Comal, Jtsda, 3, 2, pp. 141–152 (2023). https://doi.org/10.56860/jtsda.v3i2.85 [Google Scholar]
  24. M. Dimyati et al., Spatiotemporal relation of satellite-based meteorological to agricultural drought in the downstream Citarum watershed, Indonesia, Esi 22, February, p. 100339 (2024). https://doi.org/10.1016/j.indic.2024.100339 [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.