基于多源数据和集成学习的城市住宅地价分布模拟——以武汉市为例

doi:10.18306/dlkxjz.2021.10.005

Abstract

Abstract:

Characterizing the spatial distribution of urban residential land prices (RLPs) is essential for timely improving urban planning and management, as well as for effectively realizing urban smart growth. However, mapping urban RLPs at a fine scale remains challenging, due to the complex nonlinear relationship between RLPs and their potential determinants. This study developed a grid-level urban RLP mapping method based on big geo-data and ensemble learning technology to meet the needs of rapid and accurate monitoring of urban RLP dynamics. Using ensemble learning technology, combined with predictor variables extracted from points of interest (POIs) and NPP-VIIRS nighttime light images, the fine-scale RLPs in Wuhan City in 2018 were mapped through the following steps. First, the kernel density of POIs and the intensity of nighttime lights were extracted and aggregated at the 500 m×500 m grid level as the predictor variables of RLPs. Second, several RLP prediction models were established using four individual machine learning algorithms (MLAs) and bagging and stacking ensemble methods. Finally, the prediction accuracy or errors of different models were evaluated and compared, and the best performing model was selected to estimate the RLPs of the grids with no observations in Wuhan City. The results show that: 1) Among all the individual MLAs, the support vector regression (SVR) algorithm has the best prediction performance, followed by the k-nearest neighbor algorithm (k-NN), Gaussian process regression (GPR), and back propagation neural network (BP-NN) algorithms. 2) In terms of improving the prediction accuracy of individual MLAs, the performance of the stacking method is better than that of the bagging method. The stacking #1 model that integrates the SVR and k-NN algorithms has the smallest prediction error, with %MAE of 8.29%, and R² of 0.814. 3) The RLP map generated by the proposed methodological framework can effectively reveal the circular characteristics and local singularity of the RLP distribution. This study provides new ideas, methods, and technical means for rapidly and accurately mapping urban RLPs, which is conducive to the improvement of urban RLP monitoring systems in the era of big data.

Key words: urban residential land price, land price distribution, machine learning, POI, nighttime light, Wuhan City

ZHANG Peng, HU Shougeng, YANG Shengfu, CHENG Peikun. Modeling urban residential land price distribution using multi-source data and ensemble learning: A case of Wuhan City[J].PROGRESS IN GEOGRAPHY, 2021, 40(10): 1664-1677.

Figures/Tables 11

Fig.1

Tab.1

Fig.2

Fig.3

Fig.4

Tab.2

Fig.5

Tab.3

Tab.4

Tab.5

Fig.6

References 49

[1]	况伟大, 李涛. 土地出让方式、地价与房价[J]. 金融研究, 2012(8):56-69.
	[ Kuang Weida, Li Tao. Land sale pattern, land price and real estate price. Journal of Financial Research, 2012(8):56-69. ]
[2]	Ding C R, Zhao X S. Land market, land development and urban spatial structure in Beijing[J]. Land Use Policy, 2014, 40:83-90. doi: 10.1016/j.landusepol.2013.10.019
[3]	Zou Y H, Mason R, Zhong R J. Modeling the polycentric evolution of post-Olympic Beijing: An empirical analysis of land prices and development intensity[J]. Urban Geography, 2015, 36(5):735-756. doi: 10.1080/02723638.2015.1027121
[4]	邓羽, 刘盛和, 姚峰峰, 等. 基于协同克里格的基准地价评估及空间结构分析[J]. 地理科学进展, 2009, 28(3):403-408.
	[ Deng Yu, Liu Shenghe, Yao Fengfeng, et al. Standard land price appraisal and space structure analysis based on co-kriging. Progress in Geography, 2009, 28(3):403-408. ]
[5]	邹如, 伍育鹏, 章文波. 浅析中国基准地价体系的现状与发展[J]. 中国土地科学, 2012, 26(3):49-54.
	[ Zou Ru, Wu Yupeng, Zhang Wenbo. Preliminary analysis on the status and trend of land benchmark pricing system in China. China Land Sciences, 2012, 26(3):49-54. ]
[6]	王华, 罗平, 张杰. 引入深度学习的城市基准地价评估模型研究[J]. 中国土地科学, 2018, 32(9):59-65.
	[ Wang Hua, Luo Ping, Zhang Jie. A new appraisal model for urban land benchmark price based on deep learning. China Land Science, 2018, 32(9):59-65. ]
[7]	包善驹, 陆林. 合肥城市规划引导空间演进对地价时空演变的影响[J]. 地理学报, 2015, 70(6):906-918. doi: 10.11821/dlxb201506005
	[ Bao Shanju, Lu Lin. Impact of planning-guided spatial evolvement on temporal-spatial evolution of land price: Taking Hefei as an example. Acta Geographica Sinica, 2015, 70(6):906-918. ]
[8]	杨叠涵, 陈江龙, 袁丰. 南京城市空间重构对土地出让时空演化影响研究[J]. 地理科学进展, 2015, 34(2):246-256. doi: 10.11820/dlkxjz.2015.02.013
	[ Yang Diehan, Chen Jianglong, Yuan Feng. Impact of urban spatial restructuring on spatial-temporal evolution of land leasing in Nanjing City. Progress in Geography, 2015, 34(2):246-256. ]
[9]	Yang S F, Hu S G, Wang S L, et al. Effects of rapid urban land expansion on the spatial direction of residential land prices: Evidence from Wuhan, China[J]. Habitat International, 2020, 101:102186. doi: 10.1016/j.habitatint.2020.102186. doi: 10.1016/j.habitatint.2020.102186
[10]	Davis M A, Oliner S D, Pinto E J, et al. Residential land values in the Washington, DC metro area: New insights from big data[J]. Regional Science and Urban Economics, 2017, 66:224-246. doi: 10.1016/j.regsciurbeco.2017.06.006
[11]	刘瑜, 詹朝晖, 朱递, 等. 集成多源地理大数据感知城市空间分异格局[J]. 武汉大学学报(信息科学版), 2018, 43(3):327-335.
	[ Liu Yu, Zhan Zhaohui, Zhu Di, et al. Incorporating multi-source big geo-data to sense spatial heterogeneity patterns in an urban space. Geomatics and Information Science of Wuhan University, 2018, 43(3):327-335. ]
[12]	张祚, 卢新海, 罗翔, 等. 城市住宅基准地价的三维可视化与空间形态分析: 以武汉市中心城区为例[J]. 清华大学学报(自然科学版), 2018, 58(10):941-952.
	[ Zhang Zuo, Lu Xinhai, Luo Xiang, et al. 3-D-visualization and spatial morphology analysis of urban benchmark land prices for residential use: A case study of Wuhan, China. Journal of Tsinghua University (Science and Technology), 2018, 58(10):941-952. ]
[13]	阚博颖, 濮励杰, 徐彩瑶, 等. 基于GWR模型的南京主城区住宅地价空间异质性驱动因素研究[J]. 经济地理, 2019, 39(3):100-107.
	[ Kan Boying, Pu Lijie, Xu Caiyao, et al. Driving factors on the spatial heterogeneity of residential land price in downtown Nanjing based on GWR model. Economic Geography, 2019, 39(3):100-107. ]
[14]	Hu S G, Cheng Q M, Wang L, et al. Modeling land price distribution using multifractal IDW interpolation and fractal filtering method[J]. Landscape and Urban Planning, 2013, 110:25-35. doi: 10.1016/j.landurbplan.2012.09.008
[15]	Zhang P, Hu S G, Li W D, et al. Modeling fine-scale residential land price distribution: An experimental study using open data and machine learning[J]. Applied Geography, 2021, 129:102442. doi: 10.1016/j.apgeog.2021.102442. doi: 10.1016/j.apgeog.2021.102442
[16]	Alonso W. Location and land use: Toward a general theory of land rent[M]. Cambridge, USA: Harvard University Press, 1964.
[17]	吴宇哲, 吴次芳. 基于Kriging技术的城市基准地价评估研究[J]. 经济地理, 2001, 21(5):584-588.
	[ Wu Yuzhe, Wu Cifang. A study on kriging-based urban base and standard land value assessment: Taking Hangzhou City as a case. Economic Geography, 2001, 21(5):584-588. ]
[18]	蒋芳, 朱道林. 基于GIS的地价空间分布规律研究: 以北京市住宅地价为例[J]. 经济地理, 2005, 25(2):199-202.
	[ Jiang Fang, Zhu Daolin. A GIS-based study on spatial distribution of land prices: The case of residential land prices in Beijing. Economic Geography, 2005, 25(2):199-202. ]
[19]	胡守庚, 成秋明. 基于多重分形的城市地价场构建方法研究[J]. 中国土地科学, 2012, 26(1):38-44, 66, 97.
	[ Hu Shougeng, Cheng Qiuming. Developing urban land price field using multi-fractal method. China Land Sciences, 2012, 26(1):38-44, 66, 97. ]
[20]	Derdouri A, Murayama Y. A comparative study of land price estimation and mapping using regression kriging and machine learning algorithms across Fukushima prefecture, Japan[J]. Journal of Geographical Sciences, 2020, 30(5):794-822. doi: 10.1007/s11442-020-1756-1
[21]	任辉, 吴群. 基于ESDA的城市住宅地价时空分异研究: 以南京市为例[J]. 经济地理, 2011, 31(5):760-765.
	[ Ren Hui, Wu Qun. Study on urban residential land price time-spatial differentiation based on ESDA analysis: A case study of Nanjing City. Economic Geography, 2011, 31(5):760-765. ]
[22]	崔娜娜, 冯长春, 宋煜. 北京市居住用地出让价格的空间格局及影响因素[J]. 地理学报, 2017, 72(6):1049-1062. doi: 10.11821/dlxb201706008
	[ Cui Nana, Feng Changchun, Song Yu. Spatial pattern of residential land parcels and determinants of residential land price in Beijing since 2004. Acta Geographica Sinica, 2017, 72(6):1049-1062. ]
[23]	郑新奇, 王家耀, 阎弘文, 等. 数字地价模型在城市地价时空分析中的应用[J]. 资源科学, 2004, 26(1):14-21.
	[ Zheng Xinqi, Wang Jiayao, Yan Hongwen, et al. Design and application of digital land price model for land price evolvement in urban area. Resources Science, 2004, 26(1):14-21. ]
[24]	张丽芳, 濮励杰, 张静, 等. 基于Hedonic模型的城市地价空间结构分析: 以湖南省娄底市为例[J]. 经济地理, 2009, 29(9):1475-1480.
	[ Zhang Lifang, Pu Lijie, Zhang Jing, et al. Analysis on the space frame of city land price based on the hedonic model: Taking Loudi City of Hunan Province as an example. Economic Geography, 2009, 29(9):1475-1480. ]
[25]	瞿诗进, 胡守庚, 李全峰, 等. 城市住宅地价影响因素的定量识别与时空异质性: 以武汉市为例[J]. 地理科学进展, 2018, 37(10):1371-1380. doi: 10.18306/dlkxjz.2018.10.007
	[ Qu Shijin, Hu Shougeng, Li Quanfeng, et al. Quantitative evaluation of the impacts of driving factors on urban residential land price and analysis of their spatio-temporal heterogeneity: A case study of Wuhan City. Progress in Geography, 2018, 37(10):1371-1380. ]
[26]	Glaesener M L, Caruso G. Neighborhood green and services diversity effects on land prices: Evidence from a multilevel hedonic analysis in Luxembourg[J]. Landscape and Urban Planning, 2015, 143:100-111. doi: 10.1016/j.landurbplan.2015.06.008
[27]	Glumac B, Herrera-Gomez M, Licheron J. A hedonic urban land price index[J]. Land Use Policy, 2019, 81:802-812. doi: 10.1016/j.landusepol.2018.11.032
[28]	吕萍, 甄辉. 基于GWR模型的北京市住宅用地价格影响因素及其空间规律研究[J]. 经济地理, 2010, 30(3):472-478.
	[ Lv Ping, Zhen Hui. Affecting factors research of Beijing residential land price based on GWR model. Economic Geography, 2010, 30(3):472-478. ]
[29]	Hu S G, Yang S F, Li W D, et al. Spatially non-stationary relationships between urban residential land price and impact factors in Wuhan City, China[J]. Applied Geography, 2016, 68:48-56. doi: 10.1016/j.apgeog.2016.01.006
[30]	韩娟, 金晓斌, 张志宏, 等. 中国住宅出让地价发育特征及其影响因素分析[J]. 地理科学, 2017, 37(4):573-584. doi: 10.13249/j.cnki.sgs.2017.04.011
	[ Han Juan, Jin Xiaobin, Zhang Zhihong, et al. Development characteristics and factors analysis of residential land price in China. Scientia Geographica Sinica, 2017, 37(4):573-584. ]
[31]	张鹏, 胡守庚, 瞿诗进. 城市中心对地价空间分布作用的非均质扩散规律[J]. 地理与地理信息科学, 2018, 34(2):79-86.
	[ Zhang Peng, Hu Shougeng, Qu Shijin. Research on the non-homogeneous diffusion law of urban center on spatial distribution of land price. Geography and Geo-Information Science, 2018, 34(2):79-86. ]
[32]	Nakamura H. Relationship among land price, entrepreneurship, the environment, economics, and social factors in the value assessment of Japanese cities[J]. Journal of Cleaner Production, 2019, 217:144-152. doi: 10.1016/j.jclepro.2019.01.201
[33]	Ma J, Cheng J C P, Jiang F F, et al. Analyzing driving factors of land values in urban scale based on big data and non-linear machine learning techniques[J]. Land Use Policy, 2020, 94:104537. doi: 10.1016/j.landusepol.2020.104537. doi: 10.1016/j.landusepol.2020.104537
[34]	Qu S J, Hu S G, Li W D, et al. Temporal variation in the effects of impact factors on residential land prices[J]. Applied Geography, 2020, 114:102124. doi: 10.1016/j.apgeog.2019.102124. doi: 10.1016/j.apgeog.2019.102124
[35]	Hu L R, He S J, Han Z X, et al. Monitoring housing rental prices based on social media: An integrated approach of machine-learning algorithms and hedonic modeling to inform equitable housing policies[J]. Land Use Policy, 2019, 82:657-673. doi: 10.1016/j.landusepol.2018.12.030
[36]	刘宣, 黄江明, 赵冠华. 基于机器学习方法的城市社区尺度商铺租金空间布局分析[J]. 中国土地科学, 2021, 35(3):49-57.
	[ Liu Xuan, Huang Jiangming, Zhao Guanhua. A machine learning approach for community-scale commercial rents mapping. China Land Science, 2021, 35(3):49-57. ]
[37]	Park B, Bae J K. Using machine learning algorithms for housing price prediction: The case of Fairfax County, Virginia housing data[J]. Expert Systems with Applications, 2015, 42(6):2928-2934. doi: 10.1016/j.eswa.2014.11.040
[38]	Kang Y H, Zhang F, Peng W Z, et al. Understanding house price appreciation using multi-source big geo-data and machine learning[J]. Land Use Policy, 2020: 104919. doi: 10.1016/j.landusepol.2020.104919. doi: 10.1016/j.landusepol.2020.104919
[39]	Zhou Z H. Ensemble methods: Foundations and algorithms[M]. Boca Raton, USA: CRC Press, 2012.
[40]	Graczyk M, Lasota T, Trawiński B, et al. Comparison of bagging, boosting and stacking ensembles applied to real estate appraisal[C]// Nguyen N T, Le M T, Świątek J. Intelligent information and database systems. Berlin, Germany: Springer, 2010: 340-350.
[41]	Chen Y M, Liu X P, Li X, et al. Mapping the fine-scale spatial pattern of housing rent in the metropolitan area by using online rental listings and ensemble learning[J]. Applied Geography, 2016, 75:200-212. doi: 10.1016/j.apgeog.2016.08.011
[42]	Zhang Z, Li J M, Luo X, et al. Urban lake spatial openness and relationship with neighboring land prices: Exploratory geovisual analytics for essential policy insights[J]. Land Use Policy, 2020, 92:104479. doi: 10.1016/j.landusepol.2020.104479. doi: 10.1016/j.landusepol.2020.104479
[43]	Li C, Zou L, Wu Y, et al. Potentiality of using Luojia1-01 night-time light imagery to estimate urban community housing price: A case study in Wuhan, China[J]. Sensors, 2019, 19(14):3167. doi: 10.3390/s19143167. doi: 10.3390/s19143167
[44]	Zhao M, Cheng W M, Zhou C H, et al. GDP spatialization and economic differences in south China based on NPP-VIIRS nighttime light imagery[J]. Remote Sensing, 2017, 9(7):673. doi: 10.3390/rs9070673. doi: 10.3390/rs9070673
[45]	Tan M H, Li X B, Li S J, et al. Modeling population density based on nighttime light images and land use data in China[J]. Applied Geography, 2018, 90:239-247. doi: 10.1016/j.apgeog.2017.12.012
[46]	Li C, Zhu H L, Ye X Y, et al. Study on average housing prices in the inland capital cities of China by night-time light remote sensing and official statistics data[J]. Scientific Reports, 2020, 10:7732. doi: 10.1038/s41598-020-64506-2. doi: 10.1038/s41598-020-64506-2
[47]	Gu J R, Zhu M C, Jiang L. Housing price forecasting based on genetic algorithm and support vector machine[J]. Expert Systems with Applications, 2011, 38(4):3383-3386. doi: 10.1016/j.eswa.2010.08.123
[48]	Srivastava S. Weka: A tool for data preprocessing, classification, ensemble, clustering and association rule mining[J]. International Journal of Computer Applications, 2014, 88(10):26-29.
[49]	Demšar J. Statistical comparisons of classifiers over multiple data sets[J]. The Journal of Machine Learning Research, 2006, 7:1-30.

类别	变量	数据源	参考文献	类别	变量	数据源	参考文献
商服繁华度	商场核密度	POI	[15,35]	医疗条件	医院核密度	POI	[33,38]
	超市核密度	POI	[15,35]	交通便捷度	公交站核密度	POI	[15,33]
	生活设施核密度	POI	[33,35]	交通便捷度	地铁站核密度	POI	[15,41]
教育条件	大学核密度	POI	[29,38]	居住环境	公园广场核密度	POI	[34,38]
	中学核密度	POI	[29,41]	居住环境	距最近水体距离	土地利用现状数据	[29,42]
	小学核密度	POI	[15,41]	夜间灯光	夜间灯光像素值	NPP-VIIRS影像	[41,43]

评价指标	GPR	k-NN	BP-NN	SVR
MAE	1760.91	1360.68	1964.62	1304.41
%MAE/%	11.66	9.06	13.26	8.73
RMSE	2514.09	2066.58	2677.31	1961.69
%RMSE/%	17.47	14.27	18.59	13.60
P	0.885	0.909	0.872	0.913
R²	0.676	0.760	0.626	0.803

评价指标	B_GPR	B_k-NN	B_BP-NN	B_SVR
MAE	1862.48	1429.18	1844.68	1363.82
%MAE/%	12.19	9.40	12.65	9.11
RMSE	2685.98	2127.50	2498.64	2003.45
%RMSE/%	18.68	14.72	17.37	13.89
P	0.880	0.905	0.876	0.910
R²	0.631	0.754	0.678	0.796

评价指标	stacking #1	stacking #2	stacking #3
MAE	1231.06	1245.11	1249.50
%MAE/%	8.29	8.37	8.41
RMSE	1871.29	1889.08	1892.24
%RMSE/%	12.94	13.07	13.09
P	0.918	0.917	0.917
R²	0.814	0.811	0.810

运行次数	B_GPR		B_k-NN		B_BP-NN		B_SVR		stacking #2		stacking #3
运行次数	MAE差异	秩	MAE差异	秩	MAE差异	秩	MAE差异	秩	MAE差异	秩	MAE差异	秩
1	-610.55	1	-200.33	6	-600.41	1	-139.96	2	-10.22	8	-5.49	3
2	-629.66	5	-190.31	3	-635.75	7	-183.10	5	-7.02	5	-8.26	5
3	-625.41	2	-216.81	9	-611.44	3	-171.72	3	-10.82	9	-5.77	4
4	-630.42	6	-218.38	10	-647.00	9	-183.25	6	-8.86	7	-14.84	7
5	-631.42	7	-198.12	5	-613.62	4	-132.76	1	-14.05	10	-18.44	9
6	-654.50	10	-213.26	8	-606.55	2	-219.31	10	-4.47	2	-1.98	1
7	-625.84	4	-205.48	7	-639.70	8	-192.83	9	-8.12	6	-17.64	8
8	-625.73	3	-179.01	1	-623.23	5	-183.57	7	-4.83	3	-3.83	2
9	-636.82	8	-181.17	2	-635.56	6	-176.81	4	-1.46	1	-10.56	6
10	-642.45	9	-194.39	4	-649.68	10	-186.16	8	-5.32	4	-22.88	10
R⁺	0		0		0		0		0		0
R^-	55		55		55		55		55		55
Z值	–2.803		–2.803		–2.803		–2.803		–2.803		–2.803

Modeling urban residential land price distribution using multi-source data and ensemble learning: A case of Wuhan City

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 11

References 49

Related Articles 15

Metrics

Comments

Recommended 0

[1]	WANG Anqi, PENG Jiandong, REN Peng, YANG Hong, DAI Qi. Impact of the built environment of rail transit stations on the travel behavior of persons with disabilities: Taking 189 rail transit stations in Wuhan City as an example [J]. PROGRESS IN GEOGRAPHY, 2021, 40(7): 1127-1140.
[2]	XIE Bo, WANG Xiao, WU Lei. Exploring the effect of urban greenspace on residents’ mental health using a quasi-natural experiment: Taking Wuhan East Lake Greenway as an example [J]. PROGRESS IN GEOGRAPHY, 2021, 40(7): 1141-1153.
[3]	ZHU Bangyao. From local food to national snacks: Spatial expansion of Lanzhou hand-pulled noodle and Shaxian snack restaurants [J]. PROGRESS IN GEOGRAPHY, 2021, 40(6): 991-999.
[4]	ZHAN Dongsheng, ZHANG Qianyun, ZHANG Wenzhong, YU Xiaofen, ZHANG Juanfeng. Spatial agglomeration and location choice factors of real estate enterprises in Hangzhou City [J]. PROGRESS IN GEOGRAPHY, 2021, 40(5): 736-745.
[5]	LU Xinhai, CAI Dawei, ZENG Chen. Influencing factors of housing price differentiation based on the spatial quantile model: A case study of Wuhan City [J]. PROGRESS IN GEOGRAPHY, 2021, 40(2): 283-292.
[6]	DONG Wenqian, DONG Liang, XIANG Lin, TAO Haijun, ZHAO Chuanhu, QU Hanbing. Spatial pattern of urban management cases based on Log Gaussian Cox Processes [J]. PROGRESS IN GEOGRAPHY, 2020, 39(8): 1356-1366.
[7]	WU Kejie, WU Jidong, YE Mengqi. A review on the application of social media data in natural disaster emergency management [J]. PROGRESS IN GEOGRAPHY, 2020, 39(8): 1412-1422.
[8]	LI Zhixuan, HE Zhongyu, ZHANG Yiming, JIN Shuangshuang, WANG Xuemei, ZHU Jie, LIU Shicen. Impact of greenspace exposure on residents’ mental health: A case study of Nanjing City [J]. PROGRESS IN GEOGRAPHY, 2020, 39(5): 779-791.
[9]	DUAN Qianwen, TAN Minghong. Temporal and spatial changes of urban forests in major cities in China and abroad [J]. PROGRESS IN GEOGRAPHY, 2020, 39(3): 410-419.
[10]	YUAN Wenhua, LI Jianchun, QIN Xiaonan, QI Xiaoxing. Landscape ecological characteristics and network connection of urban cultural industries based on the firm-cluster-system framework [J]. PROGRESS IN GEOGRAPHY, 2020, 39(3): 474-487.
[11]	XIE Cheng, HUANG Bo, LIU Xiaoqian, ZHOU Tao, WANG Yu. Population exposure to heatwaves in Shenzhen based on mobile phone location data [J]. PROGRESS IN GEOGRAPHY, 2020, 39(2): 231-242.
[12]	SHEN Jie, ZHANG Keyun. An empirical analysis of factors leading to typical urban problems in China [J]. PROGRESS IN GEOGRAPHY, 2020, 39(1): 1-12.
[13]	WU Yan, LI Hongbo. Spatial change and correlations of desakota regions in a metropolitan area using NPP/VIIRS nighttime light data: A case study of Wuhan City [J]. PROGRESS IN GEOGRAPHY, 2020, 39(1): 13-23.
[14]	LUO Jing,JIANG Liang,LUO Minghai,TIAN Lingling,CHEN Guolei,TIAN Ye,WU Yikun. Rural development level of villages in Wuhan City’s new urban districts and its hierarchical structure [J]. PROGRESS IN GEOGRAPHY, 2019, 38(9): 1370-1381.
[15]	LAI Xijun. A review of integrated water quality modeling for a watershed [J]. PROGRESS IN GEOGRAPHY, 2019, 38(8): 1123-1135.