Next Article in Journal
An Experimental Water Consumption Regression Model for Typical Administrative Buildings in the Czech Republic
Next Article in Special Issue
Sustainable Urban Water Management under a Changing Climate: The Role of Spatial Planning
Previous Article in Journal
Zinc(II) Adsorption by Low-Carbon Shungite: The Effect of pH
Previous Article in Special Issue
Managed Aquifer Recharge (MAR) in Sustainable Urban Water Management
Open AccessArticle

Soil Moisture Investigation Utilizing Machine Learning Approach Based Experimental Data and Landsat5-TM Images: A Case Study in the Mega City Beijing

by 1, 1, 2,3,*, 3,4,*, 2 and 2
1
School of Mechanical Electronic and Information Engineering, China University of Mining and Technology, Beijing 100083, China
2
School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China
3
National & Local Joint Engineering Lab for Big Data Analysis and Computing Technology, Beijing 100190, China
4
School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
*
Authors to whom correspondence should be addressed.
Water 2018, 10(4), 423; https://doi.org/10.3390/w10040423
Received: 25 January 2018 / Revised: 16 March 2018 / Accepted: 21 March 2018 / Published: 4 April 2018
The characteristics of soil moisture content (SMC) distribution in an area are necessarily analyzed for the design and construction of sponge cities. Combining remote sensing data with experimental data, this paper establishes a machine learning model to reveal the characteristics of SMC. Taking Beijing as an example, the SMC distribution was obtained and the characteristics were analyzed after training and validating. When comparing different machine learning methods, it can be concluded that the support vector classifier (SVC) method trained with remote sensing and grayscale data can achieve the highest accuracy (76.69%). The calculation results show that the districts with the highest and lowest SMC value are Xicheng District (19.94%) and Daxing District (11.04%), respectively, in Beijing. The mean SMC value of Beijing is 15.65%. The SMC distribution characteristic in Beijing shows that the soil in the west and north are relatively wet, while the soil in the east and south are relatively dry. Therefore, it is suggested that the timely monitoring of the SMC of vegetation covered areas at the north and west should be carried out. Water conservation facilities also need to be established with the development of city constructions in the south and east areas. View Full-Text
Keywords: soil moisture content (SMC); remote sensing; machine learning; support vector classifier (SVC); experimental data soil moisture content (SMC); remote sensing; machine learning; support vector classifier (SVC); experimental data
Show Figures

Figure 1

MDPI and ACS Style

Qu, Y.; Qian, X.; Song, H.; Xing, Y.; Li, Z.; Tan, J. Soil Moisture Investigation Utilizing Machine Learning Approach Based Experimental Data and Landsat5-TM Images: A Case Study in the Mega City Beijing. Water 2018, 10, 423.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map

1
Back to TopTop