ارزیابی عملکرد روش‌های مبتنی بر یادگیری ماشین و روش‌های آماری سنتی در تصحیح اریبی داده‌های بارش مدل‌های اقلیمی CMIP6

Ahmed, K., Sachindra, D. A., Shahid, S., Demirel, M. C., & Chung, E. S. (2019). Selection of multi-model ensemble of general circulation models for the simulation of precipitation and maximum and minimum temperature based on spatial assessment metrics. Hydrology and Earth System Sciences. 23(11), 4803-4824.‌

Almazroui M, Saeed F, Saeed S, Islam MN, Ismail M, Klutse NAB, Siddiqui MH (2020) Projected change in temperature and precipitation over Africa from CMIP6. Earth Systems and Environment. 4(3):455–475

Aryal A, Shrestha S, Babel MS (2019) Quantifying the sources of uncertainty in an ensemble of hydrological climate-impact projections. Theoretical and Applied Climatology. 135(1/2):193–209

Avazpour, F., Hadian, M. R., Talebi, A., & Haghighi, A. T. (2025). Impact of climate change on river flow, using a hybrid model of LARS_WG and LSTM: A case study in the Kashkan Basin. Results in Engineering, 104956.‌

Chen, T. Guestrin, C. XGBoost: A Scalable Tree Boosting System. In KDD ’16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Association for Computing Machinery: New York, NY, USA, 2016; pp. 785–794.

Eyring V, Bony S, Meehl GA, et al. (2016) Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development 9:1937–58

Fang, G., Yang, J., Chen, Y. N., & Zammit, C. 2015. Comparing bias correction methods in downscaling meteorological variables for a hydrologic impact study in an arid area in China. Hydrology and Earth System Sciences, 19(6), 2547-2559.

Gupta H V, Kling H, Yilmaz K K, Martinez G F (2009) Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological Model ling. Journal of Hydrology 377(1-2):80-91‌

He, A., Wang, C., Xu, L., Wang, P., Wang, W., Chen, N., & Chen, Z. (2024). An evaluation of statistical and deep learning-based correction of monthly precipitation over the Yangtze River basin in China based on CMIP6 GCMs. Environment, Development and Sustainability, 1-21.‏

Heshamati, S., Nazari, B., & Nikoo, M. R. (2025). Enhancing accuracy in streamflow prediction under climate change scenarios based on an integrated machine learning–metaheuristic optimization approach. Journal of Water and Climate Change, jwc2025499.

Hirabayashi, Y., Tanoue, M., Sasaki, O., Zhou, X., & Yamazaki, D. (2021). Global exposure to flooding from the new CMIP6 climate model projections. Scientific reports, 11(1), 3740.‌

Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.‌

Houshmand Kouchi D, Esmaili K, Faridhosseini A, Sanaei Nejad SH, Khalili D (2019) Simulation of climate change impacts using fifth assessment report models under RCP scenarios on water resources in the upper basin of Salman Farsi Dam. Iranian Journal of Irrigation and Drainage 2(13):243-258 (In Persian)

Hyndman R J, Koehler A B (2006) another look at measures of forecast accuracy. International Journal of Forecasting 22(4):679-688‌

Jaiswal, R., Mall, R. K., Singh, N., Lakshmi Kumar, T. V., & Niyogi, D. (2022). Evaluation of bias correction methods for regional climate models: Downscaled rainfall analysis over diverse agroclimatic zones of India. Earth and Space Science, 9(2), e2021EA001981.

Javan, K., & Azizzadeh, M. R. (2024). Evaluation of different bias correction methods and Projection of Future Precipitation Changes Using GFDL-ESM4 model in Lake Urmia basin. Journal of Geography and Planning, 28(88), 415-397.‏ (In Persian)

Jongman, B., Ward, P. J., & Aerts, J. C. (2012). Global exposure to river and coastal flooding: Long term trends and changes. Global Environmental Change, 22(4), 823-835.‌

Kim J H, Sung J H, Chung E S, Kim S U, Son M, Shiru M S (2021) Comparison of projection in meteorological and hydrological droughts in the Cheongmicheon Watershed for RCP4. 5 and SSP2-4.5. Sustainability 13(4):2066‌

Lenderink, G., Buishand, A., & Van Deursen, W. (2007). Estimates of future discharges of the river Rhine using two scenario methodologies: direct versus delta approach. Hydrology and Earth System Sciences, 11(3), 1145-1159.‌

Li, H., Zhang, Y., Lei, H., & Hao, X. (2023). Machine learning-based bias correction of precipitation measurements at high altitude. Remote Sensing, 15(8), 2180.‌

Maraun D, Wetterhall F, Ireson AM, Chandler RE, Kendon EJ, Widmann M, Brienen S, Rust HW, Sauter T, Themeßl M, et al. (2010) Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical models and the end user. Reviews of Geophysics 48(3):3003

Nash, J. E., & Sutcliffe, J. V. (1970). River flow forecasting through conceptual Model s part I—A discussion of principles. Journal of hydrology, 10(3), 282-290

Nurdiati, S., Sopaheluwakan, A., Pratama, Y. A., & Najib, M. K. (2021). Statistical bias correction on the climate model for el nino index prediction. Al-Jabar: Jurnal Pendidikan Matematika, 12(2), 273-282.

O’Neill BC, Tebaldi C, van Vuuren DP, et al. (2016) the scenario model intercomparison project (ScenarioMIP) for CMIP6. Geoscientific Model Development 9:3461–82

Panofsky, H. A., & Brier, G. W. (1958). Some applications of statistics to meteorology. Mineral Industries Extension Services, College of Mineral Industries, Pennsylvania State University.‌

Pearson K (1897) Mathematical contributions to the theory of evolution. On a form of spurious correlation which may arise when indices are used in the measurement of organs. Proceedings of the Royal Society of London 60(359-367):489-498‌

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... & Duchesnay, É. (2011). Scikit-learn: Machine learning in Python. the Journal of machine Learning research, 12, 2825-2830.‌

Sachindra DA, Ahmed K, Rashid M, Shahid S, Perera BJC (2018) Statistical downscaling of precipitation using machine learning techniques. Atmospheric Research 212:240–258

Seo, G. Y., & Ahn, J. B. (2023). Comparison of bias correction methods for summertime daily rainfall in South Korea using quantile mapping and machine learning model. Atmosphere, 14(7), 1057.‌

Shiru, M. S., & Park, I. (2020). Comparison of ensembles projections of rainfall from four bias correction methods over Nigeria. Water, 12(11), 3044.‌

Tanimu, B., Bello, A. A. D., Abdullahi, S. A., Ajibike, M. A., Yaseen, Z. M., Kamruzzaman, M., ... & Shahid, S. (2024). Comparison of conventional and machine learning methods for bias correcting CMIP6 rainfall and temperature in Nigeria. Theoretical and Applied Climatology, 155(6), 4423-4452.‌

Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bulletin of the American Meteorological Society 93(4):485–498

Thrasher, B., Wang, W., Michaelis, A., Melton, F., Lee, T., & Nemani, R. (2022). NASA global daily downscaled projections, CMIP6. Scientific data, 9(1), 262.

UNISDR. (2009). UNISDR Terminology on disaster risk reduction, United Nations International Strategy for Disaster Reduction (UNISDR), Geneva, Switzerland, 35 pp

Wang J, Hu L, Li D, Ren M (2020) Potential impacts of projected climate change under CMIP5 RCP scenarios on streamflow in the Wabash River Basin. Advances in Meteorology 2020:9698423

Wilby R, Wigley T (1997) Downscaling general circulation model output: A review of methods and limitations. Progress in Physical Geography: Earth and Environment 21(4):530–548

Wood, A. W., Maurer, E. P., Kumar, A., & Lettenmaier, D. P. (2002). Long‐range experimental hydrologic forecasting for the eastern United States. Journal of Geophysical Research: Atmospheres, 107(D20), ACL-6.‌

Zhang, L., Xue, B., Yan, Y., Wang, G., Sun, W., Li, Z., Shi, H. (2019). Model uncertainty analysis methods for semi-arid watersheds with different characteristics: a comparative SWAT case study. Water, 11(6), 1177.