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Integration of extreme gradient boosting feature selection approach with machine learning models: application of weather relative humidity prediction

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Abstract

Relative humidity (RH) is one of the important processes in the hydrology cycle which is highly stochastic. Accurate RH prediction can be highly beneficial for several water resources engineering practices. In this study, extreme gradient boosting (XGBoost) approach “as a selective input parameter” was coupled with support vector regression, random forest (RF), and multivariate adaptive regression spline (MARS) models for simulating the RH process. Meteorological data at two stations (Kut and Mosul), located in Iraq region, were selected as a case study. Numeric and graphic indicators were used for model’s evaluation. In general, all models revealed good prediction performance. In addition, research finding approved the importance of all the meteorological data for the RH simulation. Further, the integration of the XGBoost approach managed to abstract the essential parameters for the RH simulation at both stations and attained good predictability with less input parameters. At Kut station, RF model attained the best prediction results with minimum root mean square error (RMSE = 4.92) and mean absolute error (MAE = 3.89) using maximum air temperature and evaporation parameters. Whereas MARS model reported the best prediction results at Mosul station using all the utilized climate parameters with minimum (RMSE = 3.80 and MAE = 2.86). Overall, the research results evidenced the capability of the proposed coupled machine learning models for modeling the RH at different coordinates within a semi-arid environment.

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References

  1. Nguyen JL, Schwartz J, Dockery DW (2014) The relationship between indoor and outdoor temperature, apparent temperature, relative humidity, and absolute humidity. Indoor Air. https://doi.org/10.1111/ina.12052

    Article  Google Scholar 

  2. Nam SW, Shin HH, Seo DU (2014) Comparative analysis of weather data for heating and cooling load calculation in greenhouse environmental design. Prot Hortic Plant Fact. https://doi.org/10.12791/ksbec.2014.23.3.174

    Article  Google Scholar 

  3. Omid M, Shafaei A (2005) Temperature and relative humidity changes inside greenhouse. Int Agrophys 19(1):153–158

    Google Scholar 

  4. Serrano-Arellano J, Belman-Flores JM, Hernández-Pérez I et al (2020) Numerical study of the distribution of temperatures and relative humidity in a ventilated room located in warm weather. C Comput Model Eng Sci. https://doi.org/10.32604/cmes.2020.08677

    Article  Google Scholar 

  5. Laurence H, Fabry F, Dutilleul P et al (2002) Estimation of the spatial pattern of surface relative humidity using ground based radar measurements and its application to disease risk assessment. Agric For Meteorol. https://doi.org/10.1016/S0168-1923(02)00019-9

    Article  Google Scholar 

  6. Pierrehumbert RT, Brogniez H, Roca R (2007) On the relative humidity of the atmosphere. Glob Circ Atmos. https://doi.org/10.1172/JCI44005.es

    Article  Google Scholar 

  7. Romps DM (2014) An analytical model for tropical relative humidity. J Clim 27:7432–7449. https://doi.org/10.1175/JCLI-D-14-00255.1

    Article  Google Scholar 

  8. Tabari H, Hosseinzadeh Talaee P (2011) Analysis of trends in temperature data in arid and semi-arid regions of Iran. Glob Planet Change. https://doi.org/10.1016/j.gloplacha.2011.07.008

    Article  Google Scholar 

  9. Trenberth KE, Fasullo J, Smith L (2005) Trends and variability in column-integrated atmospheric water vapor. Clim Dyn. https://doi.org/10.1007/s00382-005-0017-4

    Article  Google Scholar 

  10. Prasad R, Ali M, Kwan P, Khan H (2019) Designing a multi-stage multivariate empirical mode decomposition coupled with ant colony optimization and random forest model to forecast monthly solar radiation. Appl Energy 236:778–792. https://doi.org/10.1016/j.apenergy.2018.12.034

    Article  Google Scholar 

  11. Zhou TJ, Yu RC (2005) Atmospheric water vapor transport associated with typical anomalous summer rainfall patterns in China. J Geophys Res D Atmos. https://doi.org/10.1029/2004JD005413

    Article  Google Scholar 

  12. Krebs FC, Gevorgyan SA, Alstrup J (2009) A roll-to-roll process to flexible polymer solar cells: model studies, manufacture and operational stability studies. J Mater Chem. https://doi.org/10.1039/b823001c

    Article  Google Scholar 

  13. Tai Q, You P, Sang H et al (2016) Efficient and stable perovskite solar cells prepared in ambient air irrespective of the humidity. Nat Commun. https://doi.org/10.1038/ncomms11105

    Article  Google Scholar 

  14. Jian Q, fei, Ma G qing, Qiu X liang, (2014) Influences of gas relative humidity on the temperature of membrane in PEMFC with interdigitated flow field. Renew Energy. https://doi.org/10.1016/j.renene.2013.06.046

    Article  Google Scholar 

  15. Goh LJ, Othman MY, Mat S et al (2011) Review of heat pump systems for drying application. Renew Sustain Energy Rev 15(9):4788–4796

    Article  Google Scholar 

  16. Wang D, Zhan Y, Yu T et al (2020) Improving meteorological input for surface energy balance system utilizing mesoscaleweather research and forecasting model for estimating daily actual evapotranspiration. Water (Switzerland). https://doi.org/10.3390/w12010009

    Article  Google Scholar 

  17. Kaur A, Sharma JK, Agrawal S (2011) Artificial neural networks in forecasting maximum and minimum relative humidity. Int J Comput Sci Netw Secur 11:197–199

    Google Scholar 

  18. Pour SH, Wahab AKA, Shahid S (2020) Physical-empirical models for prediction of seasonal rainfall extremes of Peninsular Malaysia. Atmos Res 233:104720

    Article  Google Scholar 

  19. Alley RB, Emanuel KA, Zhang F (2019) Advances in weather prediction. Science 363:342–344

    Article  Google Scholar 

  20. Bauer P, Thorpe A, Brunet G (2015) The quiet revolution of numerical weather prediction. Nature 525(7567):47–55

    Article  Google Scholar 

  21. Yaseen ZM, Sulaiman SO, Deo RC, Chau K-W (2018) An enhanced extreme learning machine model for river flow forecasting: state-of-the-art, practical applications in water resource engineering area and future research direction. J Hydrol 569:387–408. https://doi.org/10.1016/j.jhydrol.2018.11.069

    Article  Google Scholar 

  22. Jing W, Yaseen ZM, Shahid S et al (2019) Implementation of evolutionary computing models for reference evapotranspiration modeling: short review, assessment and possible future research directions. Eng Appl Comput Fluid Mech 13:811–823. https://doi.org/10.1080/19942060.2019.1645045

    Article  Google Scholar 

  23. Malik A, Kumar A, Salih SQ et al (2020) Drought index prediction using advanced fuzzy logic model: regional case study over Kumaon in India. PLoS ONE. https://doi.org/10.1371/journal.pone.0233280

    Article  Google Scholar 

  24. Sanikhani H, Deo RC, Yaseen ZM et al (2018) Non-tuned data intelligent model for soil temperature estimation: a new approach. Geoderma 330:52–64. https://doi.org/10.1016/j.geoderma.2018.05.030

    Article  Google Scholar 

  25. Sanikhani H, Deo RC, Samui P et al (2018) Survey of different data-intelligent modeling strategies for forecasting air temperature using geographic information as model predictors. Comput Electron Agric 152:242–260

    Article  Google Scholar 

  26. Zhu S, Ptak M, Yaseen ZM et al (2020) Forecasting surface water temperature in lakes: a comparison of approaches. J Hydrol 585:124809

    Article  Google Scholar 

  27. Jiang F, Wang K, Dong L et al (2019) Deep-learning-based joint resource scheduling algorithms for hybrid MEC networks. IEEE Internet Things J 7:6252–6265

    Article  Google Scholar 

  28. Danandeh Mehr A, Nourani V, Kahya E et al (2018) Genetic programming in water resources engineering: a state-of-the-art review. J Hydrol 566:643–667

    Article  Google Scholar 

  29. Jiang F, Wang K, Dong L et al (2020) AI driven heterogeneous MEC system with UAV assistance for dynamic environment: challenges and solutions. IEEE Netw 35:400–408

    Article  MathSciNet  Google Scholar 

  30. Yaseen ZM, Faris H, Al-Ansari N (2020) Hybridized extreme learning machine model with salp swarm algorithm: a novel predictive model for hydrological application. Complexity. https://doi.org/10.1155/2020/8206245

    Article  Google Scholar 

  31. Bou-Fakhreddine B, Mougharbel I, Faye A et al (2018) Daily river flow prediction based on two-phase constructive fuzzy systems modeling: a case of hydrological—meteorological measurements asymmetry. J Hydrol 558:255–265. https://doi.org/10.1016/j.jhydrol.2018.01.035

    Article  Google Scholar 

  32. Yaseen ZM, Ebtehaj I, Kim S et al (2019) Novel hybrid data-intelligence model for forecasting monthly rainfall with uncertainty analysis. Water (Switzerland). https://doi.org/10.3390/w11030502

    Article  Google Scholar 

  33. Ahmed K, Sachindra DA, Shahid S et al (2020) Multi-model ensemble predictions of precipitation and temperature using machine learning algorithms. Atmos Res. https://doi.org/10.1016/j.atmosres.2019.104806

    Article  Google Scholar 

  34. Ali M, Prasad R, Xiang Y, Yaseen ZM (2020) Complete ensemble empirical mode decomposition hybridized with random forest and kernel ridge regression model for monthly rainfall forecasts. J Hydrol. https://doi.org/10.1016/j.jhydrol.2020.124647

    Article  Google Scholar 

  35. Deo RC, Samui P, Kim D (2015) Estimation of monthly evaporative loss using relevance vector machine, extreme learning machine and multivariate adaptive regression spline models. Stoch Environ Res Risk Assess. https://doi.org/10.1007/s00477-015-1153-y

    Article  Google Scholar 

  36. Keshtegar B, Kisi O (2017) Modified response-surface method: new approach for modeling pan evaporation. J Hydrol Eng. https://doi.org/10.1061/(asce)he.1943-5584.0001541

    Article  MATH  Google Scholar 

  37. Khan N, Shahid S, Juneng L et al (2019) Prediction of heat waves in Pakistan using quantile regression forests. Atmos Res 221:1–11. https://doi.org/10.1016/j.atmosres.2019.01.024

    Article  Google Scholar 

  38. Ali M, Prasad R (2019) Significant wave height forecasting via an extreme learning machine model integrated with improved complete ensemble empirical mode decomposition. Renew Sustain Energy Rev. https://doi.org/10.1016/j.rser.2019.01.014

    Article  Google Scholar 

  39. Yaseen ZM, Shahid S (2020) Drought index prediction using data intelligent analytic models: a review intelligent data analytics for decision-support systems in hazard mitigation. Springer, Singapore, pp 1–27

  40. Khan N, Sachindra DA, Shahid S et al (2020) Prediction of droughts over Pakistan using machine learning algorithms. Adv Water Resour. https://doi.org/10.1016/j.advwatres.2020.103562

    Article  Google Scholar 

  41. Tao H, Salih SQ, Saggi MK et al (2020) A newly developed integrative bio-inspired artificial intelligence model for wind speed prediction. IEEE Access 8:83347–83358

    Article  Google Scholar 

  42. Bokde N, Feijóo A, Al-Ansari N et al (2020) The hybridization of ensemble empirical mode decomposition with forecasting models: Application of short-term wind speed and power modeling. Energies 13:1666

    Article  Google Scholar 

  43. Sharafati A, Khosravi K, Khosravinia P et al (2019) The potential of novel data mining models for global solar radiation prediction. Int J Environ Sci Technol. https://doi.org/10.1007/s13762-019-02344-0

    Article  Google Scholar 

  44. Kisi O, Heddam S, Yaseen ZM (2019) The implementation of univariable scheme-based air temperature for solar radiation prediction: new development of dynamic evolving neural-fuzzy inference system model. Appl Energy 241:184–195

    Article  Google Scholar 

  45. AlSadi S, Khatib T (2012) Modeling of relative humidity using artificial neural network. J Asian Sci Res 2:81–86

    Google Scholar 

  46. Khatibi R, Naghipour L, Ghorbani MA, Aalami MT (2013) Predictability of relative humidity by two artificial intelligence techniques using noisy data from two Californian gauging stations. Neural Comput Appl 23:2241–2252. https://doi.org/10.1007/s00521-012-1175-z

    Article  Google Scholar 

  47. Mba L, Meukam P, Kemajou A (2016) Application of artificial neural network for predicting hourly indoor air temperature and relative humidity in modern building in humid region. Energy Build 121:32–42. https://doi.org/10.1016/j.enbuild.2016.03.046

    Article  Google Scholar 

  48. Philippopoulos K, Deligiorgi D, Kouroupetroglou G (2015) Artificial neural network modeling of relative humidity and air temperature spatial and temporal distributions over complex terrains. In Pattern Recognition Applications and Methods. Springer, Cham, pp 171–187

  49. Bayatvarkeshi M, Mohammadi K, Kisi O, Fasihi R (2018) A new wavelet conjunction approach for estimation of relative humidity: wavelet principal component analysis combined with ANN. Neural Comput Appl. https://doi.org/10.1007/s00521-018-3916-0

    Article  Google Scholar 

  50. Lange H, Sippel S (2020) Machine learning applications in hydrology. In: Levia DF, Carlyle-Moses DE, Iida S, Michalzik B, Nanko K, Tischer A (eds), Forest-water interactions. Ecological Studies, 240. Cham, Switzerland: Springer Nature, pp 233–257. https://doi.org/10.1007/978-3-030-26086-6_10

  51. Sit M, Demiray BZ, Xiang Z et al (2020) A comprehensive review of deep learning applications in hydrology and water resources. Water Sci Technol 82:2635–2670

    Article  Google Scholar 

  52. Adnan RM, Liang Z, Heddam S et al (2019) Least square support vector machine and multivariate adaptive regression splines for streamflow prediction in mountainous basin using hydro-meteorological data as inputs. J Hydrol. https://doi.org/10.1016/j.jhydrol.2019.124371

    Article  Google Scholar 

  53. Heddam S, Kisi O (2018) Modelling daily dissolved oxygen concentration using least square support vector machine, multivariate adaptive regression splines and m5 model tree. J Hydrol. https://doi.org/10.1016/j.jhydrol.2018.02.061

    Article  Google Scholar 

  54. Kisi O, Heddam S (2019) Evaporation modelling by heuristic regression approaches using only temperature data. Hydrol Sci J. https://doi.org/10.1080/02626667.2019.1599487

    Article  Google Scholar 

  55. Heddam S, Ptak M, Zhu S (2020) Modelling of daily lake surface water temperature from air temperature: extremely randomized trees (ERT) versus Air2Water, MARS, M5Tree RF and MLPNN. J Hydrol. https://doi.org/10.1016/j.jhydrol.2020.125130

    Article  Google Scholar 

  56. Ahmed K, Shahid S, Nawaz N, Khan N (2018) Modeling climate change impacts on precipitation in arid regions of Pakistan: a non-local model output statistics downscaling approach. Theor Appl Climatol. https://doi.org/10.1007/s00704-018-2672-5

    Article  Google Scholar 

  57. Ahmed K, Iqbal Z, Khan N et al (2019) Quantitative assessment of precipitation changes under CMIP5 RCP scenarios over the northern sub-Himalayan region of Pakistan. Environ Dev Sustain. https://doi.org/10.1007/s10668-019-00548-5

    Article  Google Scholar 

  58. Awadh SM, Abdulhussein FM, Al-Kilabi JA (2016) Hydrogeochemical processes and water-rock interaction of groundwater in Al-Dammam aquifer at Bahr Al-Najaf, Central Iraq. Iraqi Bull Geol Min 12:1–15

    Google Scholar 

  59. Abbasa N, Wasimia SA, Al-Ansari N (2016) Assessment of climate change impacts on water resources of Al-Adhaim, Iraq using SWAT model. Engineering 08:716–732. https://doi.org/10.4236/eng.2016.810065

    Article  Google Scholar 

  60. Sayl KN, Muhammad NS, Yaseen ZM, El-shafie A (2016) Estimation the physical variables of rainwater harvesting system using integrated GIS-based remote sensing approach. Water Resour Manag 30:3299–3313. https://doi.org/10.1007/s11269-016-1350-6

    Article  Google Scholar 

  61. Oleiwi S, Jalal S, Hamed S et al (2018) Precipitation pattern modeling using cross-station perception: regional investigation. Environ Earth Sci. https://doi.org/10.1007/s12665-018-7898-0

    Article  Google Scholar 

  62. Cullen HM, DeMenocal PB (2000) North Atlantic influence on tigris-euphrates streamflow. Int J Climatol 20:853–863. https://doi.org/10.1002/1097-0088(20000630)20:8%3c853::AID-JOC497%3e3.0.CO;2-M

    Article  Google Scholar 

  63. Osman Y, Abdellatif M, Al-Ansari N et al (2017) Climate change and future precipitation in arid environment of middle east: case study of Iraq. J Environ Hydrol 25:1–18

    Google Scholar 

  64. Khosravi K, Daggupati P, Alami MT et al (2019) Meteorological data mining and hybrid data-intelligence models for reference evaporation simulation: a case study in Iraq. Comput Electron Agric 167:105041

    Article  Google Scholar 

  65. Friedman JH (1991) Multivariate adaptive regression splines. Ann Stat 19:1–67. https://doi.org/10.1214/aos/1176347963

    Article  MathSciNet  MATH  Google Scholar 

  66. Yousif AA, Sulaiman SO, Diop L et al (2019) Open channel sluice gate scouring parameters prediction: different scenarios of dimensional and non-dimensional input parameters. Water (Switzerland). https://doi.org/10.3390/w11020353

    Article  Google Scholar 

  67. Yaseen ZM, Deo RC, Hilal A et al (2018) Predicting compressive strength of lightweight foamed concrete using extreme learning machine model. Adv Eng Softw 115:112–125. https://doi.org/10.1016/j.advengsoft.2017.09.004

    Article  Google Scholar 

  68. Sekulic S, Kowalski BR (1992) Mars: a tutorial. J Chemom 6:199–216

    Article  Google Scholar 

  69. Rehamnia I, Benlaoukli B, Heddam S (2020) Modeling of seepage flow through concrete face rockfill and embankment dams using three heuristic artificial intelligence approaches: a comparative study. Environ Process. https://doi.org/10.1007/s40710-019-00414-6

    Article  Google Scholar 

  70. Sekhar Roy S, Roy R, Balas VE (2018) Estimating heating load in buildings using multivariate adaptive regression splines, extreme learning machine, a hybrid model of MARS and ELM. Renew Sustain Energy Rev 82:4256–4268. https://doi.org/10.1016/j.rser.2017.05.249

    Article  Google Scholar 

  71. Al-Sudani ZA, Salih SQ, Yaseen ZM (2019) Development of multivariate adaptive regression spline integrated with differential evolution model for streamflow simulation. J Hydrol 573:1–12

    Article  Google Scholar 

  72. Zhang B, Xu D, Liu Y et al (2016) Multi-scale evapotranspiration of summer maize and the controlling meteorological factors in north China. Agric For Meteorol. https://doi.org/10.1016/j.agrformet.2015.09.015

    Article  Google Scholar 

  73. Ho TK (1995) Random decision forests C3 - Proceedings of the International Conference on Document Analysis and Recognition, ICDAR. IEEE Computer Society, Washington, D.C., pp 278–82

  74. Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324

    Article  MATH  Google Scholar 

  75. Tang T, Liang Z, Hu Y et al (2020) Research on flood forecasting based on flood hydrograph generalization and random forest in Qiushui River basin, China. J Hydroinform 22:1588–1602

    Article  Google Scholar 

  76. Raghavendra S, Deka PC (2014) Support vector machine applications in the field of hydrology: a review. Appl Soft Comput J 19:372–386. https://doi.org/10.1016/j.asoc.2014.02.002

    Article  Google Scholar 

  77. Ramedani Z, Omid M, Keyhani A et al (2014) A comparative study between fuzzy linear regression and support vector regression for global solar radiation prediction in Iran. Sol Energy 109:135–143. https://doi.org/10.1016/j.solener.2014.08.023

    Article  Google Scholar 

  78. Brereton RG, Lloyd GR (2010) Support vector machines for classification and regression. Analyst 135:230–267. https://doi.org/10.1039/B918972F

    Article  Google Scholar 

  79. Campbell C, Ying Y (2011) Learning with support vector machines. Synth Lect Artif Intell Mach Learn. https://doi.org/10.2200/S00324ED1V01Y201102AIM010

    Article  MATH  Google Scholar 

  80. Vapnik V, Golowich SE, Smola A (1997) Support vector method for function approximation, regression estimation, and signal processing. Advances in neural information processing systems, pp 281–287

  81. Georganos S, Grippa T, Vanhuysse S et al (2018) Very high resolution object-based land use-land cover urban classification using extreme gradient boosting. IEEE Geosci Remote Sens Lett. https://doi.org/10.1109/LGRS.2018.2803259

    Article  Google Scholar 

  82. Brownlee J (2018) Feature Importance and Feature Selection With XGBoost in Python. Machine Learning Mastery, 10-Mar-2018. [Online]. Available: https://machinelearningmastery.com/feature-importance-and-feature-selection-withxgboost-in-python/

  83. Shi X, Wong YD, Li MZ-F et al (2019) A feature learning approach based on XGBoost for driving assessment and risk prediction. Accid Anal Prev 129:170–179

    Article  Google Scholar 

  84. Abdullah AYM, Masrur A, Gani Adnan MS et al (2019) Spatio-temporal patterns of land use/land cover change in the heterogeneous coastal region of Bangladesh between 1990 and 2017. Remote Sens. https://doi.org/10.3390/rs11070790

    Article  Google Scholar 

  85. Torres-Barrán A, Alonso Á, Dorronsoro JR (2017) Regression tree ensembles for wind energy and solar radiation prediction. Neurocomputing. https://doi.org/10.1016/j.neucom.2017.05.104

    Article  Google Scholar 

  86. Zheng H, Yuan J, Chen L (2017) Short-term load forecasting using EMD-LSTM neural networks with a xgboost algorithm for feature importance evaluation. Energies. https://doi.org/10.3390/en10081168

    Article  Google Scholar 

  87. Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning. Springer, New York

    Book  Google Scholar 

  88. Breiman L, Friedman JH, Ohlsen RA, Stone CJ (1984) Classification and regression trees. The Wadsworth Statistics Probability Series. Boston: Wadsworth Publishing, p 358

  89. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I—a discussion of principles. J Hydrol 10:282–290. https://doi.org/10.1016/0022-1694(70)90255-6

    Article  Google Scholar 

  90. Chai T, Draxler RR (2014) Root mean square error (RMSE) or mean absolute error (MAE)? Arguments against avoiding RMSE in the literature. Geosci Model Dev 7:1247–1250. https://doi.org/10.5194/gmd-7-1247-2014

    Article  Google Scholar 

  91. Legates DR, McCabe GJ (1999) Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation. Water Resour Res 35:233–241. https://doi.org/10.1029/1998WR900018

    Article  Google Scholar 

  92. Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res Atmos 106:7183–7192. https://doi.org/10.1029/2000JD900719

    Article  Google Scholar 

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Acknowledgements

The authors acknowledge the data source provider: State Commission of Dams and Reservoirs, Ministry of Water Resources, Baghdad, Iraq. In addition, the authors acknowledge the supports received by the doctoral scientific research initial funding project of Baoji University of Arts and Sciences (ZK2018062) and the Key Research, Development Program in Shaanxi Province (2019GY-131).

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Authors and Affiliations

  1. Computer Science Department, Baoji University of Arts and Sciences, Shaanxi, China

    Hai Tao

  2. Department of Geology, College of Science, University of Baghdad, Baghdad, Iraq

    Salih Muhammad Awadh

  3. Computer Science Department, Dijlah University College, Baghdad, Iraq

    Sinan Q. Salih

  4. Experimental Nuclear Radiation Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar, 64001, Iraq

    Shafik S. Shafik

  5. New Era and Development in Civil Engineering Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar, 64001, Iraq

    Zaher Mundher Yaseen

  6. College of Creative Design, Asia University, Taichung City, Taiwan

    Zaher Mundher Yaseen

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Contributions

HT contributed to concept, modeling, software, writing. SMA contributed to writing, data, validation, investigation. SQS contributed to validation, discussion, analysis, and writing. SSS contributed to data analysis, revision, editing, validation, investigation. ZMY contributed to data analysis, revision, editing, validation, investigation.

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Correspondence to Zaher Mundher Yaseen.

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Tao, H., Awadh, S.M., Salih, S.Q. et al. Integration of extreme gradient boosting feature selection approach with machine learning models: application of weather relative humidity prediction. Neural Comput & Applic 34, 515–533 (2022). https://doi.org/10.1007/s00521-021-06362-3

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