scholarly journals Prediction Power of Logistic Regression (LR) and Multi-Layer Perceptron (MLP) Models in Exploring Driving Forces of Urban Expansion to Be Sustainable in Estonia

2021 ◽  
Vol 14 (1) ◽  
pp. 160
Author(s):  
Najmeh Mozaffaree Pour ◽  
Tõnu Oja
Keyword(s):  
Land Cover ◽  
Driving Forces ◽  
Urban Expansion ◽  
Network Models ◽  
Influential Factors ◽  
Residential Areas ◽  
Neural Network Models ◽  
Independent Variables ◽  
The Core ◽  
Mlp Model

Estonia mainly experienced urban expansion after regaining independence in 1991. Employing the CORINE Land Cover dataset to analyze the dynamic changes in land use/land cover (LULC) in Estonia over 28 years revealed that urban land increased by 33.96% in Harju County and by 19.50% in Tartu County. Therefore, after three decades of LULC changes, the large number of shifts from agricultural and forest land to urban ones in an unplanned manner have become of great concern. To this end, understanding how LULC change contributes to urban expansion will provide helpful information for policy-making in LULC and help make better decisions for future transitions in urban expansion orientation and plan for more sustainable cities. Many different factors govern urban expansion; however, physical and proximity factors play a significant role in explaining the spatial complexity of this phenomenon in Estonia. In this research, it was claimed that urban expansion was affected by the 12 proximity driving forces. In this regard, we applied LR and MLP neural network models to investigate the prediction power of these models and find the influential factors driving urban expansion in two Estonian counties. Using LR determined that the independent variables “distance from main roads (X7)”, “distance from the core of main cities of Tallinn and Tartu land (X2)”, and “distance from water land (X11)” had a higher negative correlation with urban expansion in both counties. Indeed, this investigation requires thinking towards constructing a balance between urban expansion and its driving forces in the long term in the way of sustainability. Using the MLP model determined that the “distance from existing residential areas (X10)” in Harju County and the “distance from the core of Tartu (X2)” in Tartu County were the most influential driving forces. The LR model showed the prediction power of these variables to be 37% for Harju County and 45% for Tartu County. In comparison, the MLP model predicted nearly 80% of variability by independent variables for Harju County and approximately 50% for Tartu County, expressing the greater power of independent variables. Therefore, applying these two models helped us better understand the causative nature of urban expansion in Harju County and Tartu County in Estonia, which requires more spatial planning regulation to ensure sustainability.

What Phishing E-mails Reveal: An Exploratory Analysis of Phishing Attempts Using Text Analysis

2019 ◽  
Vol 33 (3) ◽  
pp. 285-307 ◽  
Author(s):  
Daniel E. O'Leary
Keyword(s):  
Neural Network ◽  
Text Analysis ◽  
Network Models ◽  
Exploratory Analysis ◽  
Organizational Resources ◽  
Neural Network Models ◽  
Independent Variables

ABSTRACT Accountants and auditing firms are frequent phishing targets because of the proximity to organizational resources. Since phishing typically is done using emails, text analysis is used to explore differences between phishing e gmails and other emails. By analyzing and comparing a database of phishing messages to a database of the Enron emails, we find that the phishing data is statistically significantly different across a large number of univariate text variable categories. Further, we generate a model of phishing as “power,” based on independent variables of friend (who they pretend to be), achievement (of their goal), (to take your) money, and (typically done at) work. These variables are used as a basis to estimate power in both the phishing and non-phishing messages, where we find differences on the signs of the independent variables. Finally, using text analysis, we examine the ability of neural network models to differentiate between phishing emails and Enron emails.

scholarly journals Forecasting Beijing Transportation Hub Areas’s Pedestrian Flow Using Modular Neural Network

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Shuwei Wang ◽  
Ronggui Zhou ◽  
Lin Zhao
Keyword(s):  
Neural Network ◽  
Public Transportation ◽  
Distance Measure ◽  
Network Models ◽  
Influential Factors ◽  
Forecasting Model ◽  
Pedestrian Flow ◽  
Neural Network Models ◽  
Modular Neural Network ◽  
The Neural Network

Along with the increasing proportion of urban public transportation trip, pedestrian flow in transportation hub areas increased. For effectively improving the emergency handling ability of related management apartments and preventing the incident of pedestrian congestion, this paper studied the method of pedestrian flow forecast in Beijing transportation hub areas. Firstly, 34 typical sidewalks in Beijing transportation hub areas were surveyed to obtain 2200 valid data. Secondly, correlation analysis was used to analyze the relationship between pedestrian flow and its influential factors. 11 significant influential factors were extracted. Thirdly, forecasting model was established with modular neural network. The surveyed pedestrian flow sample was fuzzy clustered according to the regional land use where the transportation hub existed. Then, membership function based on the distance measure was constructed. Through fuzzy discrimination, online selection for the subnetwork of the information can be achieved. Consequently, the self-adaptation of the neural network on information processing was improved. Finally, this paper tested the pedestrian flow sample of a transportation hub in Beijing. It was concluded that the accuracy of pedestrian flow forecasting model using modular neural network was higher than other neural network models. There was also improvement in the adaptability to environment.

scholarly journals Spatial Interconnections of Land Surface Temperatures with Land Cover/Use: A Case Study of Tokyo

2021 ◽  
Vol 13 (4) ◽  
pp. 610
Author(s):  
Fei Liu ◽  
Hao Hou ◽  
Yuji Murayama
Keyword(s):  
Land Cover ◽  
Spatial Data ◽  
Land Surface ◽  
Metropolitan Areas ◽  
Driving Forces ◽  
Thermal Environment ◽  
Urban Expansion ◽  
Ordinary Least Squares ◽  
Joint Analysis ◽  
Movement Trajectories

As one of the most populated metropolitan areas in the world, the Tokyo Metropolitan Area (TMA) has experienced severe climatic modifications and pressure due to densified human activities and urban expansion. The surface urban heat island (SUHI) phenomenon particularly constitutes a significant threat to human comfort and geo-environmental health in TMA. This study aimed to profile the spatial interconnections between land surface temperature (LST) and land cover/use in TMA from 2001 to 2015 using multi-source spatial data. To this end, the thermal gradients between the urban and non-urban fabric areas in TMA were examined by joint analysis of land cover/use and LST. The spatiotemporal aggregation patterns, variations, and movement trajectories of SUHI intensity in TMA were identified and delineated. The spatial relationship between SUHI and the potential driving forces in TMA was clarified using geographically weighted regression (GWR) analysis. The results show that the thermal environment of TMA exhibited a polynucleated spatial structure with multiple thermal island cores. Overall, the magnitude and extent of SUHI in TMA increased and expanded from 2001 to 2015. During that time, SUHIs clustered in the compact residential quarters and redevelopment/renovation areas rather than downtown. The GWR models showed better performance than ordinary least squares (OLS) models, with Adj R2 > 0.9, indicating that the magnitude of SUHI significantly depended on its neighboring geographical setting, including land cover composition and configuration, population size, and terrain. We suggest that UHI mitigation in Tokyo should be focused on alleviating the magnitude of persistent thermal cores and controlling unstable SUHI occurrence based on partitioned or location-specific landscape design. This study’s findings have immense implications for SUHI mitigation in metropolitan areas situated in bay regions.

scholarly journals Exploring the Sensitivity of Recurrent Neural Network Models for Forecasting Land Cover Change

Land ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 282
Author(s):  
Alysha van Duynhoven ◽  
Suzana Dragićević
Keyword(s):  
Land Cover ◽  
Short Term Memory ◽  
Network Models ◽  
Optimization Techniques ◽  
Neural Network Models ◽  
Method Performance ◽  
Timeseries Analysis ◽  
Method Optimization ◽  
Long Short Term Memory ◽  
Real World Datasets

Recurrent Neural Networks (RNNs), including Long Short-Term Memory (LSTM) architectures, have obtained successful outcomes in timeseries analysis tasks. While RNNs demonstrated favourable performance for Land Cover (LC) change analyses, few studies have explored or quantified the geospatial data characteristics required to utilize this method. Likewise, many studies utilize overall measures of accuracy rather than metrics accounting for the slow or sparse changes of LC that are typically observed. Therefore, the main objective of this study is to evaluate the performance of LSTM models for forecasting LC changes by conducting a sensitivity analysis involving hypothetical and real-world datasets. The intent of this assessment is to explore the implications of varying temporal resolutions and LC classes. Additionally, changing these input data characteristics impacts the number of timesteps and LC change rates provided to the respective models. Kappa variants are selected to explore the capacity of LSTM models for forecasting transitions or persistence of LC. Results demonstrate the adverse effects of coarser temporal resolutions and high LC class cardinality on method performance, despite method optimization techniques applied. This study suggests various characteristics of geospatial datasets that should be present before considering LSTM methods for LC change forecasting.

scholarly journals Identifying Driving Forces of Built-Up Land Expansion Based on the Geographical Detector: A Case Study of Pearl River Delta Urban Agglomeration

2020 ◽  
Vol 17 (5) ◽  
pp. 1759 ◽  
Author(s):  
Yongwei Liu ◽  
Xiaoshu Cao ◽  
Tao Li
Keyword(s):  
Population Density ◽  
Pearl River Delta ◽  
Driving Forces ◽  
Urban Expansion ◽  
River Delta ◽  
Urban Agglomeration ◽  
Pearl River ◽  
Landscape Index ◽  
The Core ◽  
Geographical Detector

Understanding the driving forces behind built-up land expansion is crucial in urban planning and management. Using the Pearl River Delta urban agglomeration as research area, four landscape metrics were used to analyze landscape characteristics of urban expansion from 1990 to 2015. Spatial autocorrelation analysis was used to study the characteristics of built-up land expansion, while geographical detector was employed to identify the driving forces of urban land growth and their interactions. The results show the extent of built-up land has been increasing, the structure has become more complex, the level of fragmentation has been increasing, and the aggregation degree is in decline. The built-up landscape index shows spatial heterogeneity occurring in the core and peripheral towns of cities, as well as in the core and peripheral areas of the entire region. Also, changes in the built-up landscape index indicate increased spatial aggregation occurring in the past 25 years. Results from the geographical detector show natural, socio-economic, and transportation-related factors have substantial influence on built-up land expansion. Elevation, slope, population density, change in population density, and road network density were shown to have high influencing power. The influencing powers of slope and change in population density were also found to be different from other factors, highlighting their important role in urban development. Also, there were two types of interactions found, enhance nonlinear and enhance bivariate interactions, indicating the compounding influence of interactions between significant determinants. This study provides a new perspective and methodological approach in evaluating the driving forces behind built-up land expansion and their interactions.

scholarly journals Land Use Land Cover Change Analysis in Fagge Local Government, Kano State from 1991-2019

2020 ◽  
Author(s):  
Al-amin Abbas Ahmad
Keyword(s):  
Land Use ◽  
Local Government ◽  
Land Cover ◽  
Driving Forces ◽  
Urban Expansion ◽  
Land Use Land Cover ◽  
Economic Activities ◽  
Landsat Images ◽  
Change Analysis ◽  
Barren Land

Abstract Land Use and Land Cover (LULC) are important components of the environmental system and changes in it mirror the impacts of human activities on the environment. These impacts needed to be determined in order to get a clear picture of the extent at which different land use practices change over time. This study focused on the Land use and land cover changes of Fagge local government Kano state between 1991 and 2019 and also identify the driving forces of such changes. The data for the study two 30m x 30m Landsat images (Landsat 4&8) of the two years i.e. 2019 and 1991. The two images undergo series of image analysis and classification using ArcGIS 10.7 and ENVI 5.1 and the result where presented in form of maps, charts and tables. The result also shows that the changes that occurred from 1991 to 2019 in Fagge local government to be positive and negative changes. There happen to be a positive in the size of built-up areas in Fagge from 1991 – 2019 with a change of +4.678km2. The vegetation cover experienced a negative change of -8.87km2 while the barren land also had an increase in size with a positive change of +4.199. The data collected from previous studies indicated that the main driving behind the various changes may include; urban expansion, population growth, commercial and economic activities, security, and Government law and policies. It was recommended that Sufficient land use/land cover information should be acquired, Sensitization programs on land use / land cover, Geospatial techniques should be adopted by Government and NGO’s and lastly Government policies should geared to ensuring that there is balance in the utilization of the available land in the country

scholarly journals Assessment of Land Cover Dynamics and Drivers of Urban Expansion Using Geospatial and Logistic Regression Approach in Wa Municipality, Ghana

Land ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1251
Author(s):  
Mawuli Asempah ◽  
Wahib Sahwan ◽  
Brigitta Schütt
Keyword(s):  
Land Use ◽  
Logistic Regression ◽  
Land Cover ◽  
Temporal Dynamics ◽  
Driving Forces ◽  
Urban Expansion ◽  
Thematic Mapper ◽  
Land Use Dynamics ◽  
Spatio Temporal ◽  
Wa Municipality

The current trends of land use dynamics have revealed a significant transformation of settlement spaces. In the Wa Municipality of Ghana, the changes in land use and land cover are inspired by a plethora of driving forces. In this study, we assessed the geo-physical drivers of settlement expansion under land use dynamics in the Wa Municipality of Ghana. The study employed geospatial and remote sensing tools to map and analyse the spatio-temporal dynamics of the landscape, using Landsat satellite imageries: thematic mapper (TM), enhanced thematic mapper (ETM) and operational land imager (OLI) from 1990 to 2020. The study employed a binomial logistic regression model to statistically assess the geo-physical drivers of settlement expansion. Random forest (RF)–supervised classification based on spatio-temporal analyses generated relatively higher classification accuracies, with overall accuracy ranging from 89.33% to 93.3%. Urban expansion for the last three decades was prominent, as the period from 1990 to 2001 gained 11.44 km2 landmass of settlement, while there was 11.30 km2 gained from 2001 to 2010, and 29.44 km2 gained from 2010 to 2020. Out of the independent variables assessed, the distance to existing settlements, distance to river, and distance to primary, tertiary and unclassified roads were responsible for urban expansion.

A study on performance of MHDA in training MLPs

2019 ◽  
Vol 36 (6) ◽  
pp. 1820-1834 ◽  
Author(s):  
Sree Ranjini K.S.
Keyword(s):  
Network Models ◽  
Performance Comparison ◽  
Data Sets ◽  
Training Algorithms ◽  
Neural Network Models ◽  
Content Type ◽  
Dragonfly Algorithm ◽  
Deterministic Algorithms ◽  
Mlp Model ◽  
Optimal Set

Purpose In recent years, the application of metaheuristics in training neural network models has gained significance due to the drawbacks of deterministic algorithms. This paper aims to propose the use of a recently developed “memory based hybrid dragonfly algorithm” (MHDA) for training multi-layer perceptron (MLP) model by finding the optimal set of weight and biases. Design/methodology/approach The efficiency of MHDA in training MLPs is evaluated by applying it to classification and approximation benchmark data sets. Performance comparison between MHDA and other training algorithms is carried out and the significance of results is proved by statistical methods. The computational complexity of MHDA trained MLP is estimated. Findings Simulation result shows that MHDA can effectively find the near optimum set of weight and biases at a higher convergence rate when compared to other training algorithms. Originality/value This paper presents MHDA as an alternative optimization algorithm for training MLP. MHDA can effectively optimize set of weight and biases and can be a potential trainer for MLPs.

Sign in / Sign up

Export Citation Format

Share Document