干旱区生态环境知识资源中心(Arid): Integration of extreme gradient boosting feature selection approach with machine learning models: application of weather relative humidity prediction

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DOI	10.1007/s00521-021-06362-3
	Integration of extreme gradient boosting feature selection approach with machine learning models: application of weather relative humidity prediction
	Tao, Hai; Awadh, Salih Muhammad; Salih, Sinan Q.; Shafik, Shafik S.; Yaseen, Zaher Mundher
通讯作者	Yaseen, ZM (corresponding author), Al Ayen Univ, Sci Res Ctr, New Era & Dev Civil Engn Res Grp, Thi Qar 64001, Iraq. ; Yaseen, ZM (corresponding author), Asia Univ, Coll Creat Design, Taichung, Taiwan.
来源期刊	NEURAL COMPUTING & APPLICATIONS
ISSN	0941-0643
EISSN	1433-3058
出版年	2021-08
英文摘要	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.
英文关键词	Relative humidity XGBoost feature selection Weather stochasticity Machine learning
类型	Article ; Early Access
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000684798600003
WOS关键词	ADAPTIVE REGRESSION SPLINES ; SOLAR-RADIATION PREDICTION ; SUPPORT VECTOR MACHINE ; AIR-TEMPERATURE ; CLASSIFICATION ; IMPLEMENTATION ; SYSTEMS ; INDOOR ; MARS
WOS类目	Computer Science, Artificial Intelligence
WOS研究方向	Computer Science
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/367565
作者单位	[Tao, Hai] Baoji Univ Arts & Sci, Comp Sci Dept, Baoji, Shaanxi, Peoples R China; [Awadh, Salih Muhammad] Univ Baghdad, Coll Sci, Dept Geol, Baghdad, Iraq; [Salih, Sinan Q.] Dijlah Univ Coll, Comp Sci Dept, Baghdad, Iraq; [Shafik, Shafik S.] Al Ayen Univ, Sci Res Ctr, Expt Nucl Radiat Res Grp, Thi Qar 64001, Iraq; [Yaseen, Zaher Mundher] Al Ayen Univ, Sci Res Ctr, New Era & Dev Civil Engn Res Grp, Thi Qar 64001, Iraq; [Yaseen, Zaher Mundher] Asia Univ, Coll Creat Design, Taichung, Taiwan
推荐引用方式 GB/T 7714	Tao, Hai,Awadh, Salih Muhammad,Salih, Sinan Q.,et al. Integration of extreme gradient boosting feature selection approach with machine learning models: application of weather relative humidity prediction[J],2021.
APA	Tao, Hai,Awadh, Salih Muhammad,Salih, Sinan Q.,Shafik, Shafik S.,&Yaseen, Zaher Mundher.(2021).Integration of extreme gradient boosting feature selection approach with machine learning models: application of weather relative humidity prediction.NEURAL COMPUTING & APPLICATIONS.
MLA	Tao, Hai,et al."Integration of extreme gradient boosting feature selection approach with machine learning models: application of weather relative humidity prediction".NEURAL COMPUTING & APPLICATIONS (2021).