scholarly journals POSSIBILITIES OF MACHINE LEARNING METHODS FOR FORECASTING THE DEVELOPMENT OF URBAN TERRITORIES

2020 ◽  
Vol 6 (2) ◽  
pp. 77-81
Author(s):  
Andrey A. Sivkov ◽  
Alexey A. Kolesnikov
Keyword(s):  
Machine Learning ◽  
Urban Design ◽  
Spatial Modeling ◽  
Urban Areas ◽  
Spatial Process ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Territorial Planning ◽  
Urban Territories ◽  
The City

Urban design has always been a spatial process. Since the city is a combination of spaces and connections, both above and under ground, it is especially important to bring territorial planning to the level of spatial modeling. In the paper, the possibilities of machine learning methods for predicting the development of urban areas were investigated, a forecasting model was compiled, and its accuracy was evaluated.

scholarly journals Machine-learning methods in the classification of water bodies

2016 ◽  
Vol 4 (2) ◽  
pp. 34-42 ◽  
Author(s):  
Marek Sołtysiak ◽  
Marcin Blachnik ◽  
Dominika Dąbrowska
Keyword(s):  
Machine Learning ◽  
Water Body ◽  
Urban Areas ◽  
Water Bodies ◽  
Learning Methods ◽  
Amphibian Species ◽  
Data Set ◽  
Machine Learning Methods ◽  
Nearest Neighbours

AbstractAmphibian species have been considered as useful ecological indicators. They are used as indicators of environmental contamination, ecosystem health and habitat quality., Amphibian species are sensitive to changes in the aquatic environment and therefore, may form the basis for the classification of water bodies. Water bodies in which there are a large number of amphibian species are especially valuable even if they are located in urban areas. The automation of the classification process allows for a faster evaluation of the presence of amphibian species in the water bodies. Three machine-learning methods (artificial neural networks, decision trees and the k-nearest neighbours algorithm) have been used to classify water bodies in Chorzów – one of 19 cities in the Upper Silesia Agglomeration. In this case, classification is a supervised data mining method consisting of several stages such as building the model, the testing phase and the prediction. Seven natural and anthropogenic features of water bodies (e.g. the type of water body, aquatic plants, the purpose of the water body (destination), position of the water body in relation to any possible buildings, condition of the water body, the degree of littering, the shore type and fishing activities) have been taken into account in the classification. The data set used in this study involved information about 71 different water bodies and 9 amphibian species living in them. The results showed that the best average classification accuracy was obtained with the multilayer perceptron neural network.

scholarly journals A concept of the air quality monitoring system in the city of Lublin with machine learning methods to detect data outliers

2019 ◽  
Vol 252 ◽  
pp. 03009 ◽  
Author(s):  
Tomasz Cieplak ◽  
Tomasz Rymarczyk ◽  
Robert Tomaszewski
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Monitoring System ◽  
Low Cost ◽  
Quality Monitoring ◽  
Spatiotemporal Variability ◽  
Air Quality Monitoring ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
The City

This paper presents a concept of the air quality monitoring system design and describes a selection of data quality analysis methods. A high level of industrialisation affects the risk of natural disasters related to environmental pollution such ase.g.air pollution by gases and clouds of dust (carbon monoxide, sulphur oxides, nitrogen oxides). That is why researches related to the monitoring this type of phenomena are extremely important. Low-cost air quality sensors are more commonly used to monitor air parameters in urban areas. These types of sensors are used to obtain an image of the spatiotemporal variability in the concentration of air pollutants. Aside from their low price , which is important from a point of view of the economic accessibility of society, low-cost sensors are prone to produce erroneous results compared to professional air quality monitors. The described study focuses on the analysis of outliers as particularly interesting for further analysis, as well as modelling with machine learning methods for air quality assessment in the city of Lublin.

scholarly journals Machine Learning Methods for Classification of the Green Infrastructure in City Areas

2019 ◽  
Vol 8 (10) ◽  
pp. 463 ◽  
Author(s):  
Nikola Kranjčić ◽  
Damir Medak ◽  
Robert Župan ◽  
Milan Rezo
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Satellite Imagery ◽  
Urban Areas ◽  
Urban Infrastructure ◽  
Support Vector ◽  
Bayes Classifier ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Artificial Neural

Rapid urbanization in cities can result in a decrease in green urban areas. Reductions in green urban infrastructure pose a threat to the sustainability of cities. Up-to-date maps are important for the effective planning of urban development and the maintenance of green urban infrastructure. There are many possible ways to map vegetation; however, the most effective way is to apply machine learning methods to satellite imagery. In this study, we analyze four machine learning methods (support vector machine, random forest, artificial neural network, and the naïve Bayes classifier) for mapping green urban areas using satellite imagery from the Sentinel-2 multispectral instrument. The methods are tested on two cities in Croatia (Varaždin and Osijek). Support vector machines outperform random forest, artificial neural networks, and the naïve Bayes classifier in terms of classification accuracy (a Kappa value of 0.87 for Varaždin and 0.89 for Osijek) and performance time.

scholarly journals Estimation of Danger Signs in Regional Complaint Data

2018 ◽  
Vol 2 (4-2) ◽  
pp. 342
Author(s):  
Yao Lin ◽  
Tsunenori Mine ◽  
Kohei Yamaguchi ◽  
Sachio Hirokawa
Keyword(s):  
Machine Learning ◽  
Intelligent Agent ◽  
Support Vector ◽  
City Management ◽  
Agreement Rate ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Conventional Machine ◽  
Multiple Data Sets ◽  
The City

Government 2.0 activities have become very attractive and popular. Using the platforms to support the activities, anyone can anytime report issues in a city on the Web and share the reports with other people. Since a variety of reports are posted, officials in the city management section have to give priorities to the reports. However, it is not easy task for the officials to judge the importance of the reports because importance judgments vary depending on the officials, and consequently the agreement rate becomes low. To remedy the low agreement rate problem of human judgment, it is necessary to create an intelligent agent which supports finding reports with high priorities. Hirokawa et al. employed the Support Vector Machine (SVM) with a word Feature Selection method (SVM+FS) to detect signs of danger from posted reports because the signs of danger is one of high priority issues to be dealt with. However they did not compare the SVM+FS method with other conventional machine learning methods and it is not clear if the SVM+FS method has better performance than the other methods. This paper explores methods for detecting the signs of danger through comprehensive experiments to develop an intelligent agent which supports officials in the city management sections. We explores conventional machine learning methods: SVM, Random Forest, Naïve Bayse using conventional word vectors, an LDA-based document vector, and word embedding by Word2Vec and compared the best method with SVM+FS. Experimental results illustrate the superiority of SVM+FS and invoke the importance of using multiple data sets when evaluating the methods of detecting signs of danger.

scholarly journals Comparative Assessment of Machine Learning Methods for Urban Vegetation Mapping Using Multitemporal Sentinel-1 Imagery

2020 ◽  
Vol 12 (12) ◽  
pp. 1952 ◽  
Author(s):  
Mateo Gašparović ◽  
Dino Dobrinić
Keyword(s):  
Machine Learning ◽  
Urban Areas ◽  
Vegetation Mapping ◽  
Gradient Boosting ◽  
Support Vector ◽  
Urban Vegetation ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Extreme Gradient Boosting ◽  
Sar Data

Mapping of green vegetation in urban areas using remote sensing techniques can be used as a tool for integrated spatial planning to deal with urban challenges. In this context, multitemporal (MT) synthetic aperture radar (SAR) data have not been equally investigated, as compared to optical satellite data. This research compared various machine learning methods using single-date and MT Sentinel-1 (S1) imagery. The research was focused on vegetation mapping in urban areas across Europe. Urban vegetation was classified using six classifiers—random forests (RF), support vector machine (SVM), extreme gradient boosting (XGB), multi-layer perceptron (MLP), AdaBoost.M1 (AB), and extreme learning machine (ELM). Whereas, SVM showed the best performance in the single-date image analysis, the MLP classifier yielded the highest overall accuracy in the MT classification scenario. Mean overall accuracy (OA) values for all machine learning methods increased from 57% to 77% with speckle filtering. Using MT SAR data, i.e., three and five S1 imagery, an additional increase in the OA of 8.59% and 13.66% occurred, respectively. Additionally, using three and five S1 imagery for classification, the F1 measure for forest and low vegetation land-cover class exceeded 90%. This research allowed us to confirm the possibility of MT C-band SAR imagery for urban vegetation mapping.

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