ORIGINAL RESEARCH
River Runoff Influence Factors Recognition Using Stepwise Regression Analysis: The Case of a Northern Chinese Coal Mining Area
Xi-jun Wu 1,2
,
 
Ying Dong 1,2
,
 
,
 
 
 
 
More details
Hide details
1
School of Civil Engineering, Yulin University, Yulin 719000, Shaanxi, China
 
2
Shaanxi Key Laboratory of Ecological Restoration in Shanbei Mining Area, Yulin University, Yulin 719000, Shaanxi, China
 
 
Submission date: 2018-12-18
 
 
Final revision date: 2019-01-24
 
 
Acceptance date: 2019-01-24
 
 
Online publication date: 2019-09-09
 
 
Publication date: 2019-12-09
 
 
Corresponding author
Xi-jun Wu   

Yulin University, China
 
 
Pol. J. Environ. Stud. 2020;29(1):893-900
 
KEYWORDS
TOPICS
ABSTRACT
Northern Shaanxi Province of China has been affected by severe water shortages, especially in coal mining areas. A systematic method of identifying the influencing factors and their significance on river runoff is essential for paving the way towards environmental protection. Based on stepwise regression analysis, this paper analyzed the runoff influence factors of the Kuye River for 1961-1979, 1980-1998, 1999-2016 and 1961-2016, and calculated the runoff reduction caused by various factors in different periods. We found that the main influence factors on Kuye runoff are disparate in different periods. In 1961-2016, the main factors were rainfall, temperature, soil and water conservation measures, and coal mining. Compared with the base period (1961-1979), the reduction in Kuye runoff stood at 21,569×104 m3 per year in 1980-1998. In 1999-2016, runoff reduction of 52,992×104 m3 was attributable to water conservation measures (57%), temperature (21%), and coal mining (25%) – but was also partially offset by the rainfall increase. These findings could serve as important references for the ecological restoration of river ecosystems in other coal-mining areas.
CONFLICT OF INTEREST
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
 
CITATIONS (13):
1.
An intelligent soft computing technique for prediction of vehicular traffic noise
Ibrahim Khalil Umar, Hüseyin Gökçekuş, Vahid Nourani
Arabian Journal of Geosciences
 
2.
A global meta-analysis of coal mining studies provides insights into the hydrologic cycle at watershed scale
Jiahui Yang, Huaixin Wei, Zelin Quan, Rui Xu, Zhaohui Wang, Hailong He
Journal of Hydrology
 
3.
Clinical Diagnosis and Treatment Value of CT Three-Dimensional Imaging of Gynecological Pelvic Blood Vessels
Li Qiao, Changxiao Li, Qinde Yu, Li Ma
Journal of Medical Imaging and Health Informatics
 
4.
Socio-ecological correlates of exercise procrastination and exercise addiction: a preliminary exploratory study using a single-university sample
Yuxia Wang, Hengzhi Deng, Xing Zhang, Hansen Li
Frontiers in Psychiatry
 
5.
Novel Ensemble Machine Learning Paradigms for the Prediction of Antioxidant Activity of Bryophyllum pinnatum (Lam.) Oken
Mahmoud Dogara Abdulrahman, A. G. Usman, Dilber Uzun Ozsahin, Abdullahi Umar Ibrahim, S. I. Abba
Proceedings of the National Academy of Sciences, India Section B: Biological Sciences
 
6.
Recognition of Factors of Postoperative Complications of Knee Osteoarthritis Patients and Comprehensive Nursing Intervention
Ying Dong, Pei Zhang, Lidan Fan, Osamah Ibrahim Khalaf
Computational and Mathematical Methods in Medicine
 
7.
Sustainable Polyurethane-Based Polymer Concrete: Mechanical and Non-destructive Properties with Machine Learning Technique
S. I. Haruna, Han Zhu, Yasser E. Ibrahim, Jian Yang, AIB Farouk, Jianwen Shao, Musa Adamu, Omar Shabbir Ahmed
International Journal of Concrete Structures and Materials
 
8.
Evolutionary computational intelligence algorithm coupled with self-tuning predictive model for water quality index determination
S.I. Abba, Sinan Jasim Hadi, Saad Sh. Sammen, Sinan Q. Salih, R.A. Abdulkadir, Quoc Bao Pham, Zaher Mundher Yaseen
Journal of Hydrology
 
9.
Artificial intelligence-based approaches for modeling the effects of spirulina growth mediums on total phenolic compounds
Wubshet Asnake Metekia, Abdullahi Garba Usman, Beyza Hatice Ulusoy, Sani Isah Abba, Kefyalew Chirkena Bali
Saudi Journal of Biological Sciences
 
10.
The Alterations in Ecological Flow Indicators Caused by Coal Mining Operations
Jinkai Luan, Ning Ma, Ran Zhang
Land Degradation & Development
 
11.
Machine learning and regression in the management of runoff in bauxite mines under rehabilitation
Aline Gonçalves Spletozer, Elpidio Inacio Fernandes Filho, Angeline Martini, Julieta Bramorski, Kelly Cristina Tonello, Herly Carlos Teixeira Dias
Environmental Science and Pollution Research
 
12.
Surface Runoff in Open Cast Mining Areas: Methods, Influential Factors, Quantifications, and Trends
Aline Gonçalves Spletozer, Elpidio Inacio Fernandes Filho, Angeline Martini, Julieta Bramorski, Kelly Cristina Tonello, Herly Carlos Teixeira Dias
Mine Water and the Environment
 
13.
Estimation of tea quality grade using statistical identification of key variables
Menghu Li, Tianhong Pan, Qi Chen
Food Control
 
eISSN:2083-5906
ISSN:1230-1485
Journals System - logo
Scroll to top