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A new Monte Carlo Feature Selection (MCFS) algorithm-based weighting scheme for multi-model ensemble of precipitation

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Abstract

Changes in patterns of meteorological parameters, like precipitations, temperature, wind, etc., are causing significant increases in various extreme events. And these extreme events, i.e., floods, heatwaves, hurricanes, droughts, etc., lead to a shortage of water resources, crop failures, wildfires, and economic losses. However, Global Circulation Models (GCMs) are considered the most important tools for quantifying climate change. Therefore, we selected 20 different GCMs of precipitation in our research, as the frequency of extreme events, like drought and flood, is highly related to changes in precipitation patterns. However, this research introduced a new weighting scheme — MCFSAWS-Ensemble: Monte Carlo Feature Selection Adaptive Weighting Scheme to Ensemble multiple GCMs, whereas, Monte Carlo Feature Selection (MCFS) is one of the most popular algorithms for discovering important variables. However, the proposed weighting scheme (MCFSAWS-Ensemble) is mainly based on two sources. Initially, it evaluates the prior performance of each GCM model to define their relative importance using MCFS. Then, it computes value by value difference between the observed and simulated model. In addition, the application of this paper is based on the monthly time series data of precipitation in the Tibet Plateau region of China. In addition, we used twenty GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to analyze the implications of the MCFSAWS-Ensemble. Further, we compared the performance of the MCFSAWS-Ensemble scheme with Simple Model Averaging (SMA) through Mean Average Error (MAE) and correlation statistics. The results of this research indicate that the proposed weighting scheme (MCFSAWS-Ensemble) is more accurate than the SMA approach. Consequently, we recommend the use of advanced machine learning algorithms such as MCFS for making accurate multi-model ensembles.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  • Alfonso S, Gesto M, Sadoul B (2021) Temperature increase and its effects on fish stress physiology in the context of global warming. J Fish Biol 98(6):1496–1508

    Google Scholar 

  • Alhakami W, ALharbi A, Bourouis S, Alroobaea R, Bouguila N (2019) Network anomaly intrusion detection using a nonparametric Bayesian approach and feature selection. IEEE Access 7:52181–52190

    Google Scholar 

  • Alirezanejad M, Enayatifar R, Motameni H, Nematzadeh H (2020) Heuristic filter feature selection methods for medical datasets. Genomics 112(2):1173–1181

    Google Scholar 

  • Baldos UL, Hertel TW, Moore FC (2019) Understanding the spatial distribution of welfare impacts of global warming on agriculture and its drivers. Am J Agric Econ 101(5):1455–1472

    Google Scholar 

  • Çakmak EE, Acar S (2022) The nexus between economic growth, renewable energy and ecological footprint: an empirical evidence from most oil-producing countries. J Clean Prod 352:131548

    Google Scholar 

  • Çakmak S, Demir T, Canpolat E, Aytaç AS (2021) Evaluation of the effects of precipitation and flow characteristics on suspended sediment transport in mountain-type Mediterranean climate; Korkuteli Stream sample, Antalya, Turkey. Arab J Geosci 14(19):1–17

    Google Scholar 

  • Chen H, Li X, Wu Y, Zuo L, Lu M, Zhou Y (2022a) Compressive strength prediction of high-strength concrete using long short-term memory and machine learning algorithms. Buildings 12(3):302

    Google Scholar 

  • Chen R, Duan K, Shang W, Shi P, Meng Y, Zhang Z (2022) Increase in seasonal precipitation over the Tibetan Plateau in the 21st century projected using CMIP6 models. Atmos Res 277:106306

    Google Scholar 

  • Davies-Barnard T, Zaehle S, Friedlingstein P (2022) Assessment of the impacts of biological nitrogen fixation structural uncertainty in CMIP6 earth system models. Biogeosciences 19(14):3491–3503

    Google Scholar 

  • Dey A, Sahoo DP, Kumar R, Remesan R (2022) A multimodel ensemble machine learning approach for CMIP6 climate model projections in an Indian River basin. Int J Climatol 42(16):9215–9236

    Google Scholar 

  • Dong F, Javed A, Saber A, Neumann A, Arnillas CA, Kaltenecker G, Arhonditsis G (2021) A flow-weighted ensemble strategy to assess the impacts of climate change on watershed hydrology. J Hydrol 594:125898

    Google Scholar 

  • Dou X, Ma X, Zhao C, Li J, Yan Y, Zhu J (2022) Risk assessment of soil erosion in Central Asia under global warming. Catena 212:106056

    Google Scholar 

  • Dramiński M, Kierczak M, Koronacki J, Komorowski J (2010) Monte Carlo feature selection and interdependency discovery in supervised classification. Advances in Machine Learning II: Dedicated to the Memory of Professor Ryszard S. Michalski 2:371–385

    Google Scholar 

  • Dramiński M, Rada-Iglesias A, Enroth S, Wadelius C, Koronacki J, Komorowski J (2008) Monte Carlo feature selection for supervised classification. Bioinformatics 24(1):110–117

    Google Scholar 

  • Duan Q, Duan A (2020) The energy and water cycles under climate change. Natl Sci Rev 7(3):553–557

    Google Scholar 

  • Ehteram M, Kalantari Z, Ferreira CS, Chau KW, Emami SMK (2022) Prediction of future groundwater levels under representative concentration pathway scenarios using an inclusive multiple model coupled with artificial neural networks. J Water Clim Change 13(10):3620–3643

    Google Scholar 

  • Hamed MM, Nashwan MS, Shahid S (2022) A novel selection method of CMIP6 GCMs for robust climate projection. Int J Climatol 42(8):4258–4272

    Google Scholar 

  • Hasan N, Bao Y (2021) Comparing different feature selection algorithms for cardiovascular disease prediction. Health Technol 11:49–62

    Google Scholar 

  • Jose DM, Vincent AM, Dwarakish GS (2022) Improving multiple model ensemble predictions of daily precipitation and temperature through machine learning techniques. Sci Rep 12(1):1–25

    Google Scholar 

  • Kim YH, Min SK, Zhang X, Sillmann J, Sandstad M (2020) Evaluation of the CMIP6 multi-model ensemble for climate extreme indices. Weather Clim Extrem 29:100269

    Google Scholar 

  • Knutti R, Baumberger C, Hadorn GH (2019) Uncertainty quantification using multiple models—prospects and challenges. In: Beisbart C, Saam N (eds) Computer simulation validation. Simulation foundations, methods and applications. Springer. https://doi.org/10.1007/978-3-319-70766-2_34

    Chapter  Google Scholar 

  • Lacetera N (2019) Impact of climate change on animal health and welfare. Anim Front 9(1):26–31

    Google Scholar 

  • Li J, Lu L, Zhang YH, Xu Y, Liu M, Feng K et al (2020) Identification of leukemia stem cell expression signatures through Monte Carlo feature selection strategy and support vector machine. Cancer Gene Ther 27(1):56–69

    Google Scholar 

  • Li L, Zhu C, Zhang R, Liu B (2021a) Roles of the Tibetan Plateau vortices in the record Meiyu rainfall in 2020. Atmos Sci Lett 22(3):e1017

    Google Scholar 

  • Li Y, Stroe DI, Cheng Y, Sheng H, Sui X, Teodorescu R (2021b) On the feature selection for battery state of health estimation based on charging–discharging profiles. J Energy Storage 33:102122

    Google Scholar 

  • Liu F, Xu C, Long Y, Yin G, Wang H (2022) Assessment of CMIP6 model performance for air temperature in the arid region of northwest China and subregions. Atmosphere 13(3):454

    Google Scholar 

  • Liu H, Wang H, Li N, Shao J, Zhou X, van Groenigen KJ, Thakur MP (2022a) Phenological mismatches between above-and belowground plant responses to climate warming. Nat Clim Change 12(1):97–102

    Google Scholar 

  • Lovenduski NS, McKinley GA, Fay AR, Lindsay K, Long MC (2016) Partitioning uncertainty in ocean carbon uptake projections: Internal variability, emission scenario, and model structure. Glob Biogeochem Cycles 30(9):1276–1287

    Google Scholar 

  • Mare F, Bahta YT, Van Niekerk W (2018) The impact of drought on commercial livestock farmers in South Africa. Dev Pract 28(7):884–898

    Google Scholar 

  • Mokhov II (2022) Climate change: causes, risks, consequences, and problems of adaptation and regulation. Her Russ Acad Sci 92(1):1–11

    Google Scholar 

  • Morim J, Trenham C, Hemer M, Wang XL, Mori N, Casas-Prat M et al (2020) A global ensemble of ocean wave climate projections from CMIP5-driven models. Sci Data 7(1):105

    Google Scholar 

  • Mudryk L, Santolaria-Otín M, Krinner G, Ménégoz M, Derksen C, Brutel-Vuilmet C et al (2020) Historical Northern Hemisphere snow cover trends and projected changes in the CMIP6 multi-model ensemble. Cryosphere 14(7):2495–2514

    Google Scholar 

  • Nematzadeh H, Enayatifar R, Mahmud M, Akbari E (2019) Frequency based feature selection method using whale algorithm. Genomics 111(6):1946–1955

    Google Scholar 

  • Niaz R, Almanjahie IM, Ali Z, Faisal M, Hussain I (2020) A novel framework for selecting informative meteorological stations using Monte Carlo Feature Selection (MCFS) algorithm. Adv Meteorol 2020:1–13

    Google Scholar 

  • Niu C, Jian S, Liu S, Liu C, Hu C (2023) Comparative study of reference evapotranspiration estimation models based on machine learning algorithm: a case study of Zhengzhou City. Hydrol Res 54(8):945–964

    Google Scholar 

  • Oh SG, Sushama L, Teufel B (2020) Arctic precipitation and surface wind speed associated with cyclones in a changing climate. Clim Dyn 55(11):3067–3085

    Google Scholar 

  • Ombadi M, Nguyen P, Sorooshian S, Hsu KL (2021) Retrospective analysis and Bayesian model averaging of CMIP6 precipitation in the Nile River Basin. J Hydrometeorol 22(1):217–229

    Google Scholar 

  • Otterbring T, Samuelsson P, Arsenovic J, Elbæk CT, Folwarczny M (2022) Shortsighted sales or long-lasting loyalty? The impact of salesperson-customer proximity on consumer responses and the beauty of bodily boundaries. Eur J Mark 57(7):1854–1885

    Google Scholar 

  • Perović V, Kadović R, Đurđević V, Pavlović D, Pavlović M, Čakmak D et al (2021) Major drivers of land degradation risk in Western Serbia: current trends and future scenarios. Ecol Indic 123:107377

    Google Scholar 

  • Raftery AE, Gneiting T, Balabdaoui F, Polakowski M (2005) Using Bayesian model averaging to calibrate forecast ensembles. Mon Weather Rev 133(5):1155–1174

    Google Scholar 

  • Rajak J (2021) A preliminary review on impact of climate change and our environment with reference to global warming. Int J Environ Sci 10:11–14

    Google Scholar 

  • Raju KS, Kumar DN (2020) Review of approaches for selection and ensembling of GCMs. J Water Clim Change 11(3):577–599

    Google Scholar 

  • Rungskunroch P, Shen ZJ, Kaewunruen S (2022) Benchmarking socio-economic impacts of high-speed rail networks using K-nearest neighbour and Pearson’s correlation coefficient techniques through computational model-based analysis. Appl Sci 12(3):1520

    Google Scholar 

  • Russo MA, Carvalho D, Martins N, Monteiro A (2022) Forecasting the inevitable: a review on the impacts of climate change on renewable energy resources. Sustain Energy Technol Assess 52:102283

    Google Scholar 

  • Sanderson BM, Knutti R, Caldwell P (2015) Addressing interdependency in a multimodel ensemble by interpolation of model properties. J Clim 28(13):5150–5170

    Google Scholar 

  • Scafetta N (2022) CMIP6 GCM ensemble members versus global surface temperatures. Clim Dyn 1-30:3091–3120. https://doi.org/10.1007/s00382-022-06493-w

    Article  Google Scholar 

  • Seifi A, Ehteram M, Soroush F, Haghighi AT (2022) Multi-model ensemble prediction of pan evaporation based on the Copula Bayesian Model Averaging approach. Engineering Applications of Artificial Intelligence 114:105124

    Google Scholar 

  • Smith DM, Scaife AA, Hawkins E, Bilbao R, Boer GJ, Caian M et al (2018) Predicted chance that global warming will temporarily exceed 1.5 C. Geophys Res Lett 45(21):11–895

    Google Scholar 

  • Sperry JS, Venturas MD, Todd HN, Trugman AT, Anderegg WR, Wang Y, Tai X (2019) The impact of rising CO2 and acclimation on the response of US forests to global warming. Proc Natl Acad Sci 116(51):25734–25744

    Google Scholar 

  • Tadist K, Najah S, Nikolov NS, Mrabti F, Zahi A (2019) Feature selection methods and genomic big data: a systematic review. J Big Data 6(1):1–24

    Google Scholar 

  • Try S, Tanaka S, Tanaka K, Sayama T, Khujanazarov T, Oeurng C (2022) Comparison of CMIP5 and CMIP6 GCM performance for flood projections in the Mekong River Basin. J Hydrol Reg Stud 40:101035

    Google Scholar 

  • Varney RM, Chadburn SE, Burke EJ, Cox PM (2022) Evaluation of soil carbon simulation in CMIP6 Earth system models. Biogeosciences 19(19):4671–4704

    Google Scholar 

  • Wang W, Zhang P, Garzione CN, Liu C, Zhang Z, Pang J et al (2022) Pulsed rise and growth of the Tibetan Plateau to its northern margin since ca. 30 Ma. Proc Natl Acad Sci 119(8):e2120364119

    Google Scholar 

  • Wang Z, Afgan MS, Gu W, Song Y, Wang Y, Hou Z et al (2021) Recent advances in laser-induced breakdown spectroscopy quantification: from fundamental understanding to data processing. TrAC Trends Analyt Chem 143:116385

    Google Scholar 

  • Wei Q, Xu J, Liao L, Yu Y, Liu W, Zhou J, Ding Y (2021) Indicators for evaluating trends of air humidification in arid regions under circumstance of climate change: relative humidity (RH) vs. Actual water vapour pressure (ea). Ecol Ind 121:107043

    Google Scholar 

  • Wine ML, Davison JH (2019) Untangling global change impacts on hydrological processes: resisting climatization. Hydrol Process 33(15):2148–2155

    Google Scholar 

  • Wootten AM, Massoud EC, Sengupta A, Waliser DE, Lee H (2020) The effect of statistical downscaling on the weighting of multi-model ensembles of precipitation. Climate 8(12):138

    Google Scholar 

  • Wu J, Shi Y, Xu Y (2020) Evaluation and projection of surface wind speed over China based on CMIP6 GCMs. J Geophys Res Atmos 125(22):e2020JD033611

    Google Scholar 

  • Xu D, Ivanov VY, Kim J, Fatichi S (2019) On the use of observations in assessment of multi-model climate ensemble. Stoch Environ Res Risk Assess 33(11-12):1923–1937

    Google Scholar 

  • Xu J, Zhang X, Zhang W, Hou N, Feng C, Yang S et al (2022a) Assessment of surface downward longwave radiation in CMIP6 with comparison to observations and CMIP5. Atmos Res 270:106056

    Google Scholar 

  • Xu Y, Zhang H, Yang F, Tong L, Yan D, Yang Y et al (2022b) State of charge estimation of supercapacitors based on multi-innovation unscented Kalman filter under a wide temperature range. Int J Energy Res 46(12):16716–16735

    Google Scholar 

  • Yan C, Liang J, Zhao M, Zhang X, Zhang T, Li H (2019) A novel hybrid feature selection strategy in quantitative analysis of laser-induced breakdown spectroscopy. Anal Chim Acta 1080:35–42

    Google Scholar 

  • Yasmin N, Jamuda M, Panda AK, Samal K, Nayak JK (2022) Emission of greenhouse gases (GHGs) during composting and vermicomposting: measurement, mitigation, and perspectives. Energy Nexus 7:100092

    Google Scholar 

  • Yue Y, Yan D, Yue Q, Ji G, Wang Z (2021) Future changes in precipitation and temperature over the Yangtze River Basin in China based on CMIP6 GCMs. Atmos Res 264:105828

    Google Scholar 

  • Zeng W, Jin S, Liu W, Qian C, Luo P, Ouyang W, Wang X (2022) Not all tokens are equal: human-centric visual analysis via token clustering transformer. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 11101–11111

    Google Scholar 

  • Zhang X, Hua L, Jiang D (2022) Assessment of CMIP6 model performance for temperature and precipitation in Xinjiang, China. Atmos Ocean Sci Lett 15(2):100128

    Google Scholar 

  • Zhang X, Xiong Z, Zhang X, Shi Y, Liu J, Shao Q, Yan X (2016) Using multi-model ensembles to improve the simulated effects of land use/cover change on temperature: a case study over northeast China. Clim Dyn 46:765–778

    Google Scholar 

  • Zhang Y, Yang X, Chen C (2021) Substantial decrease in concurrent meteorological droughts and consecutive cold events in Huai River Basin, China. Int J Climatol 41(13):6065–6083

    Google Scholar 

  • Zheng Z, Zhao L, Oleson KW (2021) Large model structural uncertainty in global projections of urban heat waves. Nat Commun 12(1):3736

    Google Scholar 

  • Zhou H, Wang X, Zhu R (2022) Feature selection based on mutual information with trend and uncertainty. Clim Dyn 1-21. https://doi.org/10.1007/s00382-022-06518-4

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Funding

The current research is a part of a funded research project awarded by the University of the Punjab Lahore, Pakistan (2022). Therefore, the authors are thankful to the project awarding institution.

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Authors and Affiliations

  1. College of Statistical Sciences, University of the Punjab, Lahore, Pakistan

    Abdul Baseer, Zulfiqar Ali, Maryam Ilyas & Mahrukh Yousaf

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  1. Abdul Baseer
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  2. Zulfiqar Ali
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Contributions

Abdul Baseer and Zulfiqar Ali conceived the presented idea. Abdul Baseer developed the theory and performed the computations. Maryam Ilyas verified the analytical methods and computations. Mahrukh Yousaf revised the manuscript and addressed all the technical questions raised by the reviewers. All authors discussed the results and contributed to the final manuscript.

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Correspondence to Zulfiqar Ali.

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Baseer, A., Ali, Z., Ilyas, M. et al. A new Monte Carlo Feature Selection (MCFS) algorithm-based weighting scheme for multi-model ensemble of precipitation. Theor Appl Climatol 155, 513–524 (2024). https://doi.org/10.1007/s00704-023-04648-1

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