Open Access
Issue
BIO Web Conf.
Volume 97, 2024
Fifth International Scientific Conference of Alkafeel University (ISCKU 2024)
Article Number 00107
Number of page(s) 8
DOI https://doi.org/10.1051/bioconf/20249700107
Published online 05 April 2024
  • X. He et al., “Solar and wind energy enhances drought resilience and groundwater sustainability,” Nature Communications, vol. 10, no. 1, p. 4893, Nov. 2019. [CrossRef] [PubMed] [Google Scholar]
  • B. François, M. Borga, J.D. Creutin, B. Hingray, D. Raynaud, and J.F. Sauterleute, “Complementarity between solar and hydro power: Sensitivity study to climate characteristics in Northern-Italy,” Renewable Energy, vol. 86, pp. 543–553, Feb. 2016. [CrossRef] [Google Scholar]
  • P. Zeng, X. Sun, and D.J. Farnham, “Skillful statistical models to predict seasonal wind speed and solar radiation in a Yangtze River estuary case study,” Scientific Reports, vol. 10, no. 1, p. 8597, May 2020. [CrossRef] [PubMed] [Google Scholar]
  • M.S. Hanoon et al., “Wind speed prediction over Malaysia using various machine learning models: potential renewable energy source,” Engineering Applications of Computational Fluid Mechanics, vol. 16, no. 1, pp. 1673–1689, Dec. 2022. [CrossRef] [Google Scholar]
  • Z. Ma et al., “Application of hybrid model based on double decomposition, error correction and deep learning in short-term wind speed prediction,” Energy Conversion and Management, vol. 205, p. 112–345, Feb. 2020. [Google Scholar]
  • G. Memarzadeh and F. Keynia, “A new short-term wind speed forecasting method based on fine-tuned LSTM neural network and optimal input sets,” Energy Conversion and Management, vol. 213, p. 112824, Jun. 2020. [CrossRef] [Google Scholar]
  • Hussein Alkattan, Sanjar Abdullaev, El-Sayed, M. El-Kenawy. (2023). The «Climate in Weathers» Approach to Processing of Meteorological Series in Mesopotamia: Assessment of Climate Similarity and Climate Change using Data Mining. Journal of Intelligent Systems and Internet of Things, 10 (1), 48–65. [CrossRef] [Google Scholar]
  • C. Wang, H. Zhang, and P. Ma, “Wind power forecasting based on singular spectrum analysis and a new hybrid Laguerre neural network,” Applied Energy, vol. 259, p. 114139, Feb. 2020. [CrossRef] [Google Scholar]
  • A. Khosravi, R.N.N. Koury, L. Machado, and J.J.G. Pabon, “Prediction of wind speed and wind direction using artificial neural network, support vector regression and adaptive neuro-fuzzy inference system,” Sustainable Energy Technologies and Assessments, vol. 25, pp. 146–160, Feb. 2018. [CrossRef] [Google Scholar]
  • D. Zafirakis, G. Tzanes, and J.K. Kaldellis, “Forecasting of Wind Power Generation with the Use of Artificial Neural Networks and Support Vector Regression Models,” Energy Procedia, vol. 159, pp. 509–514, Feb. 2019. [CrossRef] [Google Scholar]
  • T. Brahimi, F. Alhebshi, H. Alnabilsi, A. Bensenouci, and M. Rahman, “Prediction of Wind Speed Distribution Using Artificial Neural Network: The Case of Saudi Arabia,” Procedia Computer Science, vol. 163, pp. 41–48, 2019. [CrossRef] [Google Scholar]
  • R.K.B. Navas, S. Prakash, and T. Sasipraba, “Artificial Neural Network based computing model for wind speed prediction: A case study of Coimbatore, Tamil Nadu, India,” Physica A: Statistical Mechanics and its Applications, vol. 542, p. 123383, Mar. 2020. [CrossRef] [Google Scholar]
  • S.M. Lawan, W.A.W.Z. Abidin, and T. Masri, “Implementation of a topographic artificial neural network wind speed prediction model for assessing onshore wind power potential in Sibu, Sarawak,” The Egyptian Journal of Remote Sensing and Space Science, vol. 23, no. 1, pp. 21–34, Apr. 2020. [CrossRef] [Google Scholar]
  • Z. Lin, X. Liu, and M. Collu, “Wind power prediction based on high-frequency SCADA data along with isolation forest and deep learning neural networks,” International Journal of Electrical Power & Energy Systems, vol. 118, p. 105835, Jun. 2020. [CrossRef] [Google Scholar]
  • H. Hersbach et al., “The, E.R.A5 global reanalysis,” Quarterly Journal of the Royal Meteorological Society, vol. 146, no. 730, pp. 1999–2049, Jul. 2020. [CrossRef] [Google Scholar]
  • L. Breiman, “Random Forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001. [Google Scholar]
  • T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edi. Springer New York, NY, 2009. [Google Scholar]
  • S. Shalev-Shwartz and S. Ben-David, Understanding Machine Learning: From Theory to Algorithms. USA: Cambridge University Press, 2014. [CrossRef] [Google Scholar]
  • V. Nikolić, V.V. Mitić, L. Kocić, and D. Petković, “Wind speed parameters sensitivity analysis based on fractals and neuro-fuzzy selection technique,” Knowledge and Information Systems, vol. 52, no. 1, pp. 255–265, Jul. 2017. [CrossRef] [Google Scholar]
  • J. Wang, Q. Li, and B. Zeng, “Multi-layer cooperative combined forecasting system for short-term wind speed forecasting,” Sustainable Energy Technologies and Assessments, vol. 43, p. 100946, Feb. 2021. [CrossRef] [Google Scholar]
  • L. Wang, Y. Guo, M. Fan, and X. Li, “Wind speed prediction using measurements from neighboring locations and combining the extreme learning machine and the AdaBoost algorithm,” Energy Reports, vol. 8, pp. 1508–1518, Nov. 2022. [Google Scholar]
  • Al-Mahdawi, H.K., Albadran, Z., Alkattan, H., Abotaleb, M., Alakkari, K., & Ramadhan, A.J. (2023, December). Using the inverse Cauchy problem of the Laplace equation for wave propagation to implement a numerical regularization homotopy method. AIP Conference Proceedings (Vol. 2977, No. 1). AIP Publishing. [Google Scholar]
  • Kenea, U., Adeba, D., Regasa, M.S., Nones, M. Hydrological responses to land use land cover changes in the Fincha'a watershed. Ethiopia Land. 2021;10(9):910.3390/land10090916. [Google Scholar]
  • Aga, H.T. Effect of land cover change on water balance components in Gilgel Abay catchment using swat model. Netherlands: University of Twente; 2019. [Google Scholar]
  • Al-Nuaimi, B.T., Al-Mahdawi, H.K., Albadran, Z., Alkattan, H., Abotaleb, M., & Elkenawy, E.S.M. (2023). Solving of the inverse boundary value problem for the heat conduction equation in two intervals of time. Algorithms, 16(1), 33. [CrossRef] [Google Scholar]
  • Gyamfi, C., Ndambuki, J.M., Salim, R.W. Hydrological responses to land use/cover changes in the Olifants Basin, South Africa. Water. 2016;8(12):588. 10.3390/w8120588. [CrossRef] [Google Scholar]
  • Worku, T., Khare, D., Tripathi, S. Modeling runoff-sediment response to land use/land cover changes using integrated GIS and SWAT model in the Beressa watershed. Environ Earth Sci. 2017;76:1–14. 10.1007/s12665-017-6883-3. [CrossRef] [Google Scholar]
  • Akbari, E., Mollajafari, M., Al-Khafaji, H.M.R., Alkattan, H., Abotaleb, M., Eslami, M., & Palani, S. (2022). Improved salp swarm optimization algorithm for damping controller design for multimachine power system. IEEE Access, 10, 82910–82922. [CrossRef] [Google Scholar]
  • Ehsan Khodadadi, S.K. Towfek, Hussein Alkattan. (2023). Brain Tumor Classification Using Convolutional Neural Network and Feature Extraction. Fusion: Practice and Applications, 13(2), 34–41. [CrossRef] [Google Scholar]
  • Da Silva, V.D.P., Silva, M.T., Souza, E.P.D. Influence of land use change on sediment yield: a case study of the sub-middle of the São Francisco river basin. Eng Agríc. 2016;36:1005–15. 10.1590/1809-4430-eng.agric.v36n6p1005-1015/2016 [Google Scholar]
  • dos Santos, J.Y.G., Montenegro, S.M.GL, Silva, R.M., Santos, C.A.G, Quinn, N.W., Dantas, A.P.X, et al. Modeling the impacts of future LULC and climate change on runoff and sediment yield in a strategic basin in the Caatinga/Atlantic forest ecotone of Brazil. Catena. 2021;203:105308. 10.1016/j.catena.2021.105308. [CrossRef] [Google Scholar]

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