Multivariate air pollution classification in urban areas using mobile sensors and self-organizing maps

2018 ◽  
Vol 16 (10) ◽  
pp. 5475-5488
Author(s):  
R. H. de Oliveira ◽  
C. de C. Carneiro ◽  
F. G. V. de Almeida ◽  
B. M. de Oliveira ◽  
E. H. M. Nunes ◽  
...  
Keyword(s):  
Air Pollution ◽  
Urban Areas ◽  
Mobile Sensors ◽  
Self Organizing Maps ◽  
Self Organizing

scholarly journals Using k-Means and Self Organizing Maps in Clustering Air Pollution Distribution in Makassar City, Indonesia

2022 ◽  
Vol 4 (1) ◽  
pp. 167-176
Author(s):  
Suwardi Annas ◽  
Uca Uca ◽  
Irwan Irwan ◽  
Rahmat Hesha Safei ◽  
Zulkifli Rais
Keyword(s):  
Air Pollution ◽  
Urban Areas ◽  
Local Governments ◽  
Surface Ozone ◽  
Quality Parameters ◽  
Self Organizing Maps ◽  
The Government ◽  
Regional Clustering ◽  
The Impact ◽  
Self Organizing

Air pollution is an important environmental problem for specific areas, including Makassar City, Indonesia. The increase should be monitored and evaluated, especially in urban areas that are dense with vehicles and factories. This is a challenge for local governments in urban planning and policy-making to fulfill the information about the impact of air pollution. The clustering of starting points for the distribution areas can ease the government to determine policies and prevent the impact. The k-Means initial clustering method was used while the Self-Organizing Maps (SOM) visualized the clustering results. Furthermore, the Geographic Information System (GIS) visualized the results of regional clustering on a map of Makassar City. The air quality parameters used are Suspended Particles (TSP), Sulfur Dioxide (SO2), Nitrogen Dioxide (NO2), Carbon Monoxide (CO), Surface Ozone (O3), and Lead (Pb) which are measured during the day and at night. The results showed that the air contains more CO, and at night, the levels are reduced in some areas. Therefore, the density of traffic, industry and construction work contributes significantly to the spread of CO. Air conditions vary, such as high CO levels during the day and TSP at night. Also, there is a phenomenon at night that a group does not have SO2 and O3 simultaneously. The results also show that the integration of k-Means and SOM for regional clustering can be appropriately mapped through GIS visualization.

scholarly journals Selection of Optimized Retaining Wall Technique Using Self-Organizing Maps

2021 ◽  
Vol 13 (3) ◽  
pp. 1328
Author(s):  
Young-Su Kim ◽  
U-Yeol Park ◽  
Seoung-Wook Whang ◽  
Dong-Joon Ahn ◽  
Sangyong Kim
Keyword(s):  
Decision Making ◽  
Urban Areas ◽  
Construction Projects ◽  
Retaining Wall ◽  
Multidimensional Data ◽  
Self Organizing Maps ◽  
High Rise ◽  
Average Accuracy ◽  
Som Model ◽  
Self Organizing

Construction projects in urban areas tend to be associated with high-rise buildings and are of very large-scales; hence, the importance of a project’s underground construction work is significant. In this study, a rational model based on machine learning (ML) was developed. ML algorithms are programs that can learn from data and improve from experience without human intervention. In this study, self-organizing maps (SOMs) were utilized. An SOM is an alternative to existing ML methods and involves a subjective decision-making process because a developed model is used for data training to classify and effectively recognize patterns embedded in the input data space. In addition, unlike existing methods, the SOM can easily create a feature map by mapping multidimensional data to simple two-dimensional data. The objective of this study is to develop an SOM model as a decision-making approach for selecting a retaining wall technique. N-fold cross-validation was adopted to validate the accuracy of the SOM model and evaluate its reliability. The findings are useful for decision-making in selecting a retaining wall method, as demonstrated in this study. The maximum accuracy of the SOM was 81.5%, and the average accuracy was 79.8%.

Hybrid Unsupervised Modeling of Air Pollution Impact to Cardiovascular and Respiratory Diseases

2017 ◽  
Vol 9 (3) ◽  
pp. 13-35 ◽  
Author(s):  
Lazaros Iliadis ◽  
Vardis-Dimitris Anezakis ◽  
Konstantinos Demertzis ◽  
Georgios Mallinis
Keyword(s):  
Climate Change ◽  
Air Pollution ◽  
Urban Areas ◽  
Respiratory Diseases ◽  
Air Pollutant ◽  
Chi Square ◽  
Self Organizing Maps ◽  
Pollutant Concentrations ◽  
Chi Square Test ◽  
The Impact

During the last few decades, climate change has increased air pollutant concentrations with a direct and serious effect on population health in urban areas. This research introduces a hybrid computational intelligence approach, employing unsupervised machine learning (UML), in an effort to model the impact of extreme air pollutants on cardiovascular and respiratory diseases of citizens. The system is entitled Air Pollution Climate Change Cardiovascular and Respiratory (APCCCR) and it combines the fuzzy chi square test (FUCS) with the UML self organizing maps algorithm. A major innovation of the system is the determination of the direct impact of air pollution (or of the indirect impact of climate change) to the health of the people, in a comprehensive manner with the use of fuzzy linguistics. The system has been applied and tested thoroughly with spatiotemporal data for the Thessaloniki urban area for the period 2004-2013.

scholarly journals Analysis of Wetland Landcover Change in Great Lakes Urban Areas Using Self-Organizing Maps

2021 ◽  
Vol 13 (24) ◽  
pp. 4960
Author(s):  
Elissa Penfound ◽  
Eric Vaz
Keyword(s):  
Ecosystem Services ◽  
Great Lakes ◽  
Urban Areas ◽  
Flood Mitigation ◽  
Wetland Ecosystem ◽  
Wetland Area ◽  
Self Organizing Maps ◽  
Landcover Change ◽  
The City ◽  
Self Organizing

Wetland loss and subsequent reduction of wetland ecosystem services in the Great Lakes region has been driven, in part, by changing landcover and increasing urbanization. With landcover change data, digital elevation models (DEM), and self-organizing maps (SOM), this study explores changing landcover and the flood mitigation attributes of wetland areas over a 15-year period in Toronto and Chicago. The results of this analysis show that (1) in the city of Toronto SOM clusters, the landcover change correlations with wetland volume and wetland area range between −0.1 to −0.5, indicating that a more intense landcover change tends to be correlated with small shallow wetlands, (2) in the city of Chicago SOM clusters, the landcover change correlations with wetland area range between −0.1 to −0.7, the landcover change correlations with wetland volume per area range between −0.1 to 0.8, and the landcover change correlations with elevation range between −0.2 to −0.6, indicating that more intense landcover change tends to be correlated with spatially small wetlands that have a relatively high water-storage capacity per area and are located at lower elevations. In both cities, the smallest SOM clusters represent wetland areas where increased landcover change is correlated with wetland areas that have high flood mitigation potential. This study aims to offer a new perspective on changing urban landscapes and urban wetland ecosystem services in Toronto and Chicago.

A Psychographic Segmentation of Kuwaiti Travelers Using Self-Organizing Maps

2019 ◽  
Vol 24 (1) ◽  
pp. 87-92 ◽  
Author(s):  
Yvette Reisinger ◽  
Mohamed M. Mostafa ◽  
John P. Hayes
Keyword(s):  
Self Organizing Maps ◽  
Self Organizing
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