تقييم أساليب التعدين في البيانات والذكاء الاصطناعي للتنبؤ بهطول الأمطار اليومي

Abdullah, J. B., and Hamaamin, Y. A., 2020. Temporal variation of drinking water quality parameters for Sulaimani city, Kurdistan region, Iraq. UHD Journal of Science and Technology, 4(2), pp.99-106. https://doi.org/10.21928/uhdjst.v4n2y2020.pp99-106

Aftab, S., Ahmad, M., Hameed, N., Bashir, M. S., Ali, I., Nawaz, Z., 2018. Rainfall prediction using data mining techniques: A systematic literature review. International journal of advanced computer science and applications, 9(5). https://dx.doi.org/10.14569/IJACSA.2018.090518

AL-Suhaili, R. H., and Karim, R. A., 2014. Spatial prediction of monthly precipitation in Sulaimani governorate using artificial neural network models. Journal of Engineering, 20(03), pp.15-27. https://doi.org/10.31026/j.eng.2014.03.02

Aoulmi, Y., Marouf, N., Amireche, M., 2021. The assessment of artificial neural network rainfall-runoff models under different input meteorological parameters: Case study: Seybouse basin, Northeast Algeria. Journal of Water and Land Development, pp. 38-47. https://doi.org/10.24425/jwld.2021.138158

Beale, M. H., Hagan, M. T., and Demuth, H. B., 2017. Neural network toolbox. User’s Guide. The MathWorks, Inc.

Chu, J., Liu, X., Zhang, Z., Zhang, Y., and He, M., 2021. A novel method overcoming overfitting of artificial neural networks for accurate prediction: application on thermophysical properties of natural gas. Case Studies in Thermal Engineering, 28, P. 101406. https://doi.org/10.1016/j.csite.2021.101406

Frost, J., 2019. Introduction to statistics: An intuitive guide for analyzing data and unlocking discoveries. Amazon: Bellevue, WA, USA, 2020.

Frost, J., 2020. Hypothesis Testing: An Intuitive Guide for Making Decisions. Statistics by Jim Publishing; Amazon: Bellevue, WA, USA, 2020.

Gore, R. D., and Gawali, B. W. 2023. Analysis of weather parameters using machine learning. In the First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies. (ACVAIT 2022) (pp. 569-589). Atlantis Press. https://doi.org/10.2991/978-94-6463-196-8_44

Gupta, G. K., 2014. Introduction to data mining with case studies. PHI Learning Pvt. Ltd. ISBN-13 ‏ : ‎ 978 8120350021.

Hamaamin, Y. A., and Faraj, B.A., 2023. Optimal locations for BMPs stormwater management for Sulaymaniyah city industrial zone, KRG, Iraq. Environment and Ecology Research 11(3), pp.411-420. https://doi.org/10.13189/eer.2023.110301

Hamaamin, Y. A., 2017. Developing of rainfall intensity-duration-frequency model for Sulaimani city. Journal of Zankoy Sulaimani, 19(3-4), pp.10634. http://dx.doi.org/10.17656/jzs.10634

Hamaamin, Y. A., Rashed, K. A., Ali, Y. M., and Abdalla, T. A., 2022. Evaluation of ANFIS and regression techniques in estimating soil compression index for cohesive soils. Journal of Engineering, 28(10), pp.28-41. https://doi.org/10.31026/j.eng.2022.10.03

Hamdan, A., Ibekwe, K. I., Etukudoh, E. A., Umoh, A. A., and Ilojianya, V. I., 2024. AI and machine learning in climate change research: A review of predictive models and environmental impact. World Journal of Advanced Research and Reviews, 21(1), pp. 1999-2008. https://doi.org/10.30574/wjarr.2024.21.1.0257

Han, S. H., Kim, K. W., Kim, S., and Youn, Y. C., 2018. Artificial neural network: Understanding the basic concepts without mathematics. Dementia and neurocognitive disorders, 17(3), pp.83-89. https://doi.org/10.12779%2Fdnd.2018.17.3.83

Judd, C. M., McClelland, G. H., and Ryan, C. S., 2017. Data analysis: A model comparison approach to regression, ANOVA, and beyond. Routledge. ISBN 9781138819832.

Kisi, O., Shiri, J., and Tombul, M., 2013. Modeling rainfall-runoff process using soft computing techniques. Computers & Geosciences, 51, pp.108-117. https://doi.org/10.1016/j.cageo.2012.07.001

Lind, P., 2023. Kilometer-scale climate modeling of precipitation in the Nordic region, PhD dissertation, Department of Meteorology, Stockholm University.

Li, H., Li, S., and Ghorbani, H., 2024. Data-driven novel deep learning applications for the prediction of rainfall using meteorological data. Frontiers in Environmental Science, 12, 1445967. https://doi.org/10.3389/fenvs.2024.1445967

Liyew, C. M., and Melese, H. A., 2021. Machine learning techniques to predict daily rainfall amount. Journal of Big Data, 8, pp.1-11. https://doi.org/10.1186/s40537-021-00545-4

Liu, H., Fung, J. C., Lau, A. K., and Li, Z., 2024. Enhancing quantitative precipitation estimation of NWP models with fundamental meteorological variables and a transformer-based deep learning model. Earth and Space Science, 11(4), e2023EA003234. https://doi.org/10.1029/2023EA003234

Mucherino, A., Papajorgji, P., and Pardalos, P. M., 2009. Data mining in agriculture. Springer Science & Business Media. eBook ISBN 978-0-387-88615-2

MathWorks, 2018. MATLAB Fuzzy Logic Toolbox User’s Guide2, The MathWorks, Inc.

Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., and Veith, T. L. 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50(3), 885-900.

Ott, R. L., and Longnecker, M. 2015. An introduction to statistical methods and data analysis. Cengage Learning Inc. ISBN-13: ‎978-1305269477

Pathan, M. S., Wu, J., Lee, Y. H., Yan, J., and Dev, S., 2021. Analyzing the impact of meteorological parameters on rainfall prediction. In 2021 IEEE USNC-URSI Radio Science Meeting (Joint with AP-S Symposium), pp. 100-101. https://doi.org/10.23919/USNC-URSI51813.2021.9703664

Rahman, M. A., 2020. Improvement of rainfall prediction model by using fuzzy logic. American Journal of Climate Change, 9(4), pp.391-399. https://doi.org/10.4236/ajcc.2020.94024

Rashid, H. M., 2024. Drought assessment based on different meteorological drought indices in Sulaymaniyah governorate, KRG, Iraq. Journal of Engineering, 30(9), pp.190-215. https://doi.org/10.31026/j.eng.2024.09.10

Raval, M., Sivashanmugam, P., Pham, V., Gohel, H., Kaushik, A., and Wan, Y., 2021. Automated predictive analytics tool for rainfall forecasting. Scientific Reports, 11(1), p.17704. https://doi.org/10.1038/s41598-021-95735-8

Rojas-Campos, A., Langguth, M., Wittenbrink, M., and Pipa, G., 2023. Deep learning models for generation of precipitation maps based on numerical weather prediction. Geoscientific Model Development, 16(5), pp.1467-1480. https://doi.org/10.5194/gmd-16-1467-2023

Sammen, S. S., Kisi, O., Ehteram, M., El-Shafie, A., Al-Ansari, N., Ghorbani, M. A., and Shahid, S., 2023. Rainfall modeling using two different neural networks improved by metaheuristic algorithms. Environmental Sciences Europe, 35(1), p.112. https://doi.org/10.1186/s12302-023-00818-0

Shabbir, M., Chand, S., and Iqbal, F., 2023. A novel hybrid framework to model the relationship of daily river discharge with meteorological variables. Meteorology, Hydrology, and Water Management. Research and Operational Applications, 11(2), pp.70-94. https://doi.org/10.26491/mhwm/187899

Shamshirband, S., Gocić, M., Petković, D., Saboohi, H., Herawan, T., Kiah, M. L. M., and Akib, S., 2014. Soft-computing methodologies for precipitation estimation: A case study. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(3), pp.1353-1358. https://doi.org/10.1109/JSTARS.2014.2364075

Turhan, E., 2021. A comparative evaluation of the use of artificial neural networks for modeling the rainfall-runoff relationship in water resources management. Journal of Ecological Engineering, 22(5). https://doi.org/10.12911/22998993/135775