Novel method for industrial sewage outfall detection: Water pollution monitoring based on web crawler and remote sensing interpretation techniques

2021 ◽  
pp. 127640
Author(s):  
Jing Zhang ◽  
Tianyuan Zou ◽  
Yuequn Lai
Keyword(s):  
Water Pollution ◽  
Remote Sensing ◽  
Pollution Monitoring ◽  
Web Crawler ◽  
Sewage Outfall ◽  
Novel Method ◽  
Remote Sensing Interpretation ◽  
Industrial Sewage

scholarly journals Application of Remote Sensing Techniques for Water Pollution Monitoring

1979 ◽  
Vol 2 (4) ◽  
pp. 193-200
Author(s):  
Yoshinori ISHII ◽  
Tsunemasa IMAIZUMI ◽  
Yoshinori MIYAZAKI
Keyword(s):  
Water Pollution ◽  
Remote Sensing ◽  
Pollution Monitoring ◽  
Remote Sensing Techniques

Water Pollution Monitoring through Remote Sensing

2020 ◽  
Vol 16 ◽  
Author(s):  
Krishnamurthy Vallidevi ◽  
Kannappan P. Gopinath ◽  
Krishnan K. Nagarajan ◽  
D. Gnana Prakash ◽  
G. Sudhamsu ◽  
...  
Keyword(s):  
Water Pollution ◽  
Remote Sensing ◽  
Water Quality ◽  
Water Quality Monitoring ◽  
Pollution Monitoring ◽  
Geographical Information ◽  
Continuous Assessment ◽  
Remotely Sensed Data ◽  
Detection Techniques ◽  
Almost All

Background: Water is undoubtedly a very precious resource that helps life thrive on this planet, and its pollution is a problem which is potent of erasing almost all forms of life on earth. Hence, considering the magnitude of this problem, studies and experiments have been focused (from the day water pollution was recognised as an eminent issue) on monitoring and finding possible solutions for water pollution. While the latter deals with treating and purifying water using a variety of concepts, monitoring deals with the continuous assessment of water quality of a waterbody. Methodology: There are several methods for monitoring purposes, and remote sensing is a popular choice, thanks to its wide applicability and flexibility in implementation. Remote sensing basically deals with collecting data about a place (which is to be monitored) and sending the data to another ‘remote’ location for analysis. This article aims to provide a description of some methods employed in recent times for the purpose of remote sensing and a short section which deals with the analysis of the remotely sensed data using machine learning / deep learning models, hence making the reader aware of the concept of remote sensing and its scope for monitoring water pollution (or any form of pollution) in the future. Conclusions: The detailed comparative analysis of these methods showed that sensor based water quality monitoring with Geographical Information System (GIS) will be more efficient in detection of water pollutants. The further research in this field may introduce many advancements to enable efficient water pollution detection techniques.

Corrigendum to: Water Pollution Monitoring through Remote Sensing

2021 ◽  
Vol 17 (9) ◽  
pp. 1384-1384
Author(s):  
Krishnamurthy Vallidevi ◽  
Kannappan P. Gopinath ◽  
Krishnan K. Nagarajan ◽  
D. Gnana Prakash ◽  
Gurijala Sudhamsu ◽  
...  
Keyword(s):  
Water Pollution ◽  
Remote Sensing ◽  
Pollution Monitoring ◽  
Online Version

We apologize for the error that occurred in the online version of the article. Incorrect name of 5th author was published in the article entitled “Water Pollution Monitoring through Remote Sensing” in “Curr. Anal. Chem., 2021, 17(6), 802-814 [1]. The original article can be found online at https://doi.org/10.2174/1573411016666200206095055 Original: Gadug Sudhamsu Corrected: Gurijala Sudhamsu

Structural geometry of Ikogosi warm spring, southwestern Nigeria: evidence from aeromagnetic and remote sensing interpretation

2021 ◽  
Vol 7 (2) ◽  
Author(s):  
Naheem Banji Salawu ◽  
Julius Ogunmola Fatoba ◽  
Leke Sunday Adebiyi ◽  
Akinola Bolaji Eluwole ◽  
Nurudeen Kolawole Olasunkanmi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Structural Geometry ◽  
Southwestern Nigeria ◽  
Warm Spring ◽  
Remote Sensing Interpretation

scholarly journals Remote Sensing Investigation of Geological Hazards in Xingguo County, Jiangxi Province

2019 ◽  
Vol 131 ◽  
pp. 01056
Author(s):  
Min Yu ◽  
Jiangqin Chao
Keyword(s):  
Remote Sensing ◽  
Debris Flow ◽  
Jiangxi Province ◽  
Landsat 8 ◽  
Geological Environment ◽  
Geological Disasters ◽  
Work Area ◽  
Ground Collapse ◽  
Geological Disaster ◽  
Remote Sensing Interpretation

Xingguo County is located in the middle and low hilly mountainous areas. The area of the landslide, collapse and debris flow geological disasters is large. The sudden geological disasters such as landslides and mudslides caused by heavy rainfall are increasing year by year. This study mainly used high-altitude aerial imagery (0.5m) and Landsat 8 OLI satellite imagery covering Xingguo County as the data source, carried out remote sensing interpretation of geological environment background conditions and geological disasters in the whole area, and carried out on-site verification. At the same time, the correlation between the stratigraphic structure, topography and other factors in the study area and the spatial distribution characteristics of geological disaster points are discussed. The results show that: (1) based on remote sensing image interpretation of 377 geological disaster points; 83 landslide points, 229 hidden danger points, 17 collapse points, 26 hidden danger points, 1 hidden danger point, ground collapse point 1 At 20 places in the geological environment. (2) From the results of remote sensing interpretation, the types of geological disasters in the work area are mainly landslides and landslide hazards (including collapse type), and there are fewer collapses, collapses and debris flow hazards, and most landslide hazard points are unstable. (3) From the distribution of geological disasters, it is mainly within the scope of artificial influence. The construction of excavation slopes on the roads leads to instability of the slopes and induces disasters under the influence of rainfall. In addition, there are a large number of artificial mining mines in the work area. These places are also prone to geological disasters due to unreasonable mining and subsequent prevention and control work. (4) Areas with strong human activities, areas near the fault structure and water system roads are the main influencing factors for geological disasters in the work area.

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