Mapping the Physiologically Equivalent Temperature in urban areas using artificial neural network

2016 ◽  
Vol 150 ◽  
pp. 1-9 ◽  
Author(s):  
Christine Ketterer ◽  
Andreas Matzarakis
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Urban Areas ◽  
Physiologically Equivalent Temperature ◽  
Equivalent Temperature ◽  
Artificial Neural

Development of artificial neural network models for thermal comfort evaluation in outdoor urban spaces

2020 ◽  
Author(s):  
Eun Sub Kim ◽  
Dong Kun Lee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Thermal Comfort ◽  
Urban Areas ◽  
Network Models ◽  
Temperate Climate ◽  
Neural Network Models ◽  
Comfort Evaluation ◽  
Artificial Neural ◽  
Artificial Neural Network Models

<p>This study has formulated artificial neural network models to predict thermal comfort evaluation in outdoor urban areas in Seoul for summer. The artificial neural network models were considerably improved by including preceptions of microclimate, perception of environmental features(e.g urban spatial characteristics and visual stimuli, etc) and personal traits as additional predictor variables. Thermal comfort in outdoor environments has been repeatedly shown to be influenced also by human perceptions and preferences. Despite numerous attempts at refining these thermal comfort, there still have been large discrepancies between the results predicted by the theoretical models and the actual thermal comfort evaluation votes. indeed Thermal comfort model using microclimatic factors including air temperature, air velocity, solar radiation and relative humidity as predictor variables could explain only 7–42% of thermal comfort evaluation votes.</p><p>Accordingly, this study aims to formulate models to predict thermal comfort evaluation in outdoor urban areas for summer in Korea, which is located in temperate climate zone. ANN models were formulated to portray intricate interrelationships among a multitude of personal traits, urban residents’ environmental perception, microclimatic and spatial perception and physiological factors. The prediction performances of the formulated ANN models were compared with those of the commonly used thermal comfort models(PMV, PET). Also, this study aims to identify important factors that influence thermal comfort evaluation in outdoor urban areas. In addition, it is intended to compare whether the important factors and the magnitude of their contributions are different in urban spatial environment. The findings should provide valuable insights for informing urban planning designers on formulating effective strategies to improve the thermal environments in outdoor urban areas in the temperate climate zone.</p>

scholarly journals Vehicular Traffic Noise Modelling of Urban Area- A Contouring & Artificial Neural Network-Based Approach

2021 ◽  
Author(s):  
Abhijit Debnath ◽  
Prasoon Kumar Singh ◽  
Sushmita Banerjee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Traffic Flow ◽  
Noise Level ◽  
Urban Areas ◽  
Traffic Noise ◽  
Traffic Volume ◽  
Noise Levels ◽  
Noise Characteristics ◽  
Artificial Neural

Abstract Road traffic vehicular noise is one of the main sources of environmental pollution in urban areas of India. Also, steadily increasing urbanization, industrialization, infrastructures around city condition causing health risks among the urban populations. In this study we have explored noise descriptors (L10, L90, Ldn, LNI, TNI, NC), contour plotting and finds the suitability of artificial neural networks (ANN) for the prediction of traffic noise all around the Dhanbad township in 15 monitoring stations. In order to develop the prediction model, measuring noise levels of five different hours, speed of vehicles and traffic volume in every monitoring point have been studied and analyzed. Traffic volume, percent of heavy vehicles, Speed, traffic flow, road gradient, pavement, road side carriageway distance factors taken as input parameter, whereas LAeq as output parameter for formation of neural network architecture. As traffic flow is heterogenous which mainly contains 59% 2-wheelers and different vehicle specifications with varying speeds also effects driving and honking behavior which constantly changing noise characteristics. From radial noise diagrams shown that average noise levels of all the stations beyond permissible limit and highest noise levels were found at the speed of 50-55 km/h in both peak and non-peak hours. Noise descriptors clearly indicates high annoyance level in the study area. Artificial neural network with 7-7-5 formation has been developed and found as optimum due to its sum of square and overall relative error 0.858 & .029 in training and 0.458 & 0.862 in testing phase respectively. Comparative analysis between observed and predicted noise level shows very less deviation up to ±0.6 dB(A) and the R2 linear values are more than 0.9 in all five noise hours indicating the accuracy of model. Also, it can be concluded that ANN approach is much superior in prediction of traffic noise level to any other statistical method.

scholarly journals OPTIMIZATION RAINFALL-RUNOFF MODELING FOR CIUJUNG RIVER USING BACK PROPAGATION METHOD

SINERGI ◽  
2018 ◽  
Vol 22 (3) ◽  
pp. 193
Author(s):  
Ika Sari Damayanthi Sebayang ◽  
Agus Suroso ◽  
Alnis Gustin Laoli
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Urban Areas ◽  
Back Propagation ◽  
Water Volume ◽  
Rainfall Runoff ◽  
Back Propagation Algorithm ◽  
Runoff Modeling ◽  
Artificial Neural ◽  
The Relationship

The rainfall-runoff model is required to ascertain the relationship between rainfall and runoff. Hydrologists are often confronted with problems of prediction and estimation of runoff using the rainfall date. In actual fact the relationship of rainfall-runoff is known to be highly non-linear and complex. The spatial and temporal precipitation patterns and the variability of watershed characteristics create a more complex hydrologic phenomenon. Runoff is part of the rain water that enters and flows and enters the river body. Rainfall-runoff modeling in this study using Artificial Neural Network, back propagation method and sigmoid binary activation function. This model is used to simulate single or long-term continuous events, water volume, making it very appropriate for urban areas. Back propagation is an inherited learning algorithm and is commonly used by perceptron with multiple layers to change the weights associated with neurons in the hidden layer. Back propagation algorithm uses output error to change the values of its weight in the backward direction. The location of the review is the Ciujung River Basin (DAS), the data used are rainfall and debit data of Ciujung River from 2011-2017. Based on training and simulation results, obtained R2 value: 2012 = 0,85102; 2013 = 0,78661; 2014 = 0,81188; 2015 = 0,77902; 2016 = 0,7279. on model 2 = 0,8724. On model 3 R2:  January = 0,96937; February = 0,92984; March = 0,90666; April = 0,92566; May = 0,9128; June = 0,87975; July= 0,85292; August = 0,95943; September = 0,88229; October = 0,90537; November = 0,93522; December = 0,9111. with MSE (Mean Squared Error) of 0,0018479. The closer value of MSE to 0 and the value of R2 close to 1 then the better designed artificial neural network. If the data used for training more, the artificial neural network will produce a larger R2 value.

scholarly journals WATER QUALITY CLASSIFICATION BY ARTIFICIAL NEURAL NETWORK - A CASE STUDY OF DONG NAI RIVER, VIETNAM

2018 ◽  
Vol 55 (4C) ◽  
pp. 297
Author(s):  
Nguyen Hien Than
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Urban Areas ◽  
Fuzzy Comprehensive Evaluation ◽  
Neural Network Models ◽  
Quality Classification ◽  
Water Quality Classification ◽  
Artificial Neural

The Dong Nai River is the main source of supplied water for Ho Chi Minh City, Dong Nai, Binh Duong province and other areas. However, the water quality state of the Dong Nai River has been heavily pressured by discharged sources from urban areas, industrial zones, agricultural, domestic activities, etc. In this paper, the authors employed the artificial neural network model (ANNs) to classify water quality of Dong Nai River that apply a new tool to assess water quality in Vietnam. The monitoring data were used for eight years from 2007 to 2014 with 23 monitoring stations. Two neural network models including a multi-layer perceptron (MLPNN) and a generalized regression network (GRNN) were employed to classify water quality of the Dong Nai River. The results of the study showed that GRNN and MLPNN classified excellently water quality. Optimal structure of the MLPNN was H8I4O1 with model error about 0.1268 while the GRNN was error about 0.00001615. Comparing the result of water quality classification between the ANNs and the fuzzy comprehensive evaluation indicated that they were in close agreement with the respective values (the accurate rate of GRNN 100% and 98,5 % of MLPNN).

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