scholarly journals USING MULTIVARIATE ADAPTIVE REGRESSION SPLINE AND ARTIFICIAL NEURAL NETWORK TO SIMULATE URBANIZATION IN MUMBAI, INDIA

2015 ◽  
Vol XL-1-W5 ◽  
pp. 31-36 ◽  
Author(s):  
M. Ahmadlou ◽  
M. R. Delavar ◽  
A. Tayyebi ◽  
H. Shafizadeh-Moghadam
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Urban Areas ◽  
Parametric Model ◽  
Parametric Models ◽  
Model Structure ◽  
Regression Spline ◽  
Adaptive Regression ◽  
Modelling Process ◽  
Non Parametric

Land use change (LUC) models used for modelling urban growth are different in structure and performance. Local models divide the data into separate subsets and fit distinct models on each of the subsets. Non-parametric models are data driven and usually do not have a fixed model structure or model structure is unknown before the modelling process. On the other hand, global models perform modelling using all the available data. In addition, parametric models have a fixed structure before the modelling process and they are model driven. Since few studies have compared local non-parametric models with global parametric models, this study compares a local non-parametric model called multivariate adaptive regression spline (MARS), and a global parametric model called artificial neural network (ANN) to simulate urbanization in Mumbai, India. Both models determine the relationship between a dependent variable and multiple independent variables. We used receiver operating characteristic (ROC) to compare the power of the both models for simulating urbanization. Landsat images of 1991 (TM) and 2010 (ETM<sup>+</sup>) were used for modelling the urbanization process. The drivers considered for urbanization in this area were distance to urban areas, urban density, distance to roads, distance to water, distance to forest, distance to railway, distance to central business district, number of agricultural cells in a 7 by 7 neighbourhoods, and slope in 1991. The results showed that the area under the ROC curve for MARS and ANN was 94.77% and 95.36%, respectively. Thus, ANN performed slightly better than MARS to simulate urban areas in Mumbai, India.

scholarly journals Modeling of Indonesia Composite Index using Artificial Neural Network and Multivariate Adaptive Regression Spline

2019 ◽  
Vol 5 (2) ◽  
pp. 112-122
Author(s):  
Mutia Yollanda ◽  
Dodi Devianto ◽  
Putri Permathasari
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stock Price ◽  
Price Index ◽  
Multivariate Adaptive Regression Spline ◽  
Regression Spline ◽  
Determination Coefficient ◽  
Adaptive Regression ◽  
Artificial Neural ◽  
Stock Price Index

The Indonesian Composite Stock Price Index is an indicator of changes in stock prices are a guide for investors to invest in reducing risk. Fluctuations in stock data tend to violate the assumptions of normality, homoscedasticity, autocorrelation, and multicollinearity. This problem can be overcome by modelling the Composite Stock Price Index uses an artificial neural network (ANN) and multivariate adaptive regression spline (MARS). In this study, the time-series data from the Composite Stock Price Index starting in April 2003 to March 2018 with its predictor variables are crude oil prices, interest rates, inflation, exchange rates, gold prices, Down Jones, and Nikkei 225. Based on the coefficient of determination, the determination coefficient of ANN is 0.98925, and the MARS determination coefficient is 0.99427. While based on the MAPE value, MAPE value of ANN was obtained, namely 6.16383 and MAPE value of MARS, which was 4.51372. This means that the ANN method and the good MARS method are used to forecast the value of the Indonesian Composite Stock Index in the future, but the MARS method shows the accuracy of the model is slightly better than ANN.

scholarly journals Performance Evaluation for Mean Sea Level Prediction using Multivariate Adaptive Regression Spline and Artificial Neural Network

2018 ◽  
Vol 18 (1) ◽  
pp. 1-8
Author(s):  
Cynthia Borkai Boye ◽  
Valentine Ikechukwu Amoah
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Sea Level ◽  
Multivariate Adaptive Regression Spline ◽  
Ann Model ◽  
Mean Sea Level ◽  
Regression Spline ◽  
Mars Model ◽  
Adaptive Regression ◽  
Artificial Neural

Mean sea level (MSL) has been used as a vertical datum for geodetic levelling and mapping in most countries all over the world. This is because the MSL approximates the geoid and serves as a realist reference surface that could be determined mostly through tide measurements over a period of time. However, sea levels have been rising over the years due to global warming and its associated climate change which continuous to melt ice sheets around the Polar Regions. This phenomenon is likely to affect the reliability of MSL, thus it is important to determine the local MSL at regular time periods. This study assessed the performance of Artificial Neural Network (ANN) and Multivariate Adaptive Regression Spline (MARS) models in predicting the MSL. Tide gauge records from the Takoradi Harbour of Ghana were used in the study. Monthly maximum, minimum and mean tidal values were derived from the secondary data and used for both model formulation and model testing. A comparative analysis of both models showed that the ANN model performed better than the MARS model. A Root Mean Square Error (RMSE) of 0.0359 m was obtained for the ANN model, whereas 0.0555 m was obtained for the MARS model. Mean Absolute Percentage Error (MAPE) of 3.1414% was obtained for the ANN model and whereas the MARS model yielded 5.6349%. A Mean Absolute Error (MAE) for the ANN model was 0.0284 m as against 0.0446 m for the MARS model. Correlation coefficient values of 0.9720 and 0.8874 were obtained for the ANN model and the MARS model respectively. An optimum ANN structure was found to be ANN 2-11-1. Based on the outcome of this study, it is recommended that ANN model should be adopted for forecasting local mean sea level for the study area. Keywords: Mean Sea Level, Artificial Neural Network, Multivariate Adaptive Regression Spline

A Non Parametric Model for Magneto Rheological Dampers

1999 ◽  
Author(s):  
Mehdi Ahmadian ◽  
Xubin Song
Keyword(s):  
Test Data ◽  
Mr Damper ◽  
Parametric Model ◽  
Parametric Models ◽  
Magnetic Saturation ◽  
Mr Dampers ◽  
Electro Magnetic ◽  
Magneto Rheological ◽  
Force Characteristics ◽  
Non Parametric

Abstract A non-parametric model for magneto-rheological (MR) dampers is presented. After discussing the merits of parametric and non-parametric models for MR dampers, the test data for a MR damper is used to develop a non-parametric model. The results of the model are compared with the test data to illustrate the accuracy of the model. The comparison shows that the non-parametric model is able to accurately predict the damper force characteristics, including the damper non-linearity and electro-magnetic saturation. It is further shown that the parametric model can be numerically solved more efficiently than the parametric models.

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

&lt;p&gt;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&amp;#8211;42% of thermal comfort evaluation votes.&lt;/p&gt;&lt;p&gt;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&amp;#8217; 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.&lt;/p&gt;

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.

Sign in / Sign up

Export Citation Format

Share Document