Economic, Social, and Ecological Impact Evaluation of Traffic Network in Beijing–Tianjin–Hebei Urban Agglomeration Based on the Entropy Weight TOPSIS Method

Zhang, Liang; Zhang, Xubing; Yuan, Shenggu; Wang, Kai

doi:10.3390/su13041862

Cited by 25 publications

(13 citation statements)

References 27 publications

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“…Several clustering methods were compared. Mini Batch K-Means (Peng et al, 2018), spectral clustering (Von Luxburg, 2007), ward (Ward, 1963;Murtagh and Legendre, 2014), average linkage (Yim and Ramdeen, 2015), balanced iterative reducing and clustering utilizing hierarchies (Birch) (Anchang et al, 2016), and Jenks natural breaks optimization approach (Jenks) (North, 2009;Zhang et al, 2021) were used in the clustering process. The occurrences of the parameter mood change, which reflects the changes in mood state, were clustered.…”

Section: Clustering Processmentioning

confidence: 99%

RETRACTED: Mental Health Identification of Children and Young Adults in a Pandemic Using Machine Learning Classifiers

Liu

Huang

2022

Front. Psychol.

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COVID-19 has altered our lifestyle, communication, employment, and also our emotions. The pandemic and its devastating implications have had a significant impact on higher education, as well as other sectors. Numerous researchers have utilized typical statistical methods to determine the effect of COVID-19 on the psychological wellbeing of young people. Moreover, the primary aspects that have changed in the psychological condition of children and young adults during COVID lockdown is analyzed. These changes are analyzed using machine learning and AI techniques which should be established for the alterations. This research work mainly concentrates on children's and young people's mental health in the first lockdown. There are six processes involved in this work. Initially, it collects the data using questionnaires, and then, the collected data are pre-processed by data cleaning, categorical encoding, and data normalization method. Next, the clustering process is used for grouping the data based on their mood state, and then, the feature selection process is done by chi-square, L1-Norm, and ReliefF. Then, the machine learning classifiers are used for predicting the mood state, and automatic calibration is used for selecting the best model. Finally, it predicts the mood state of the children and young adults. The findings revealed that for a better understanding of the effects of the COVID-19 pandemic on children's and youths' mental states, a combination of heterogeneous data from practically all feature groups is required.

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Section: Clustering Processmentioning

confidence: 99%

RETRACTED: Mental Health Identification of Children and Young Adults in a Pandemic Using Machine Learning Classifiers

Liu

Huang

2022

Front. Psychol.

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“…From the perspective of research scale, it is involved in different spatial scales such as provinces, urban agglomeration, and municipalities. Ecological security evaluation methods mainly include the comprehensive index method, the analytic hierarchy process, the matter-element model method, the TOPSIS method, the ecological model evaluation method, and other research methods [7][8][9][10]. The construction of the ecological safety evaluation index systems is mostly based on the PSR (Pressure, State, Response) model, DPSIR (Driving, Pressure, State, Impact, Response), DPSEEA (Driving, Pressure, State, Exposure, Effect, Action), and other multi-factor comprehensive evaluation models [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Research on Landscape Pattern Construction and Ecological Restoration of Jiuquan City Based on Ecological Security Evaluation

et al. 2021

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Jiuquan City is a typical ecologically fragile area in the arid areas of Northwest China, and unreasonable human activities directly affect the regional ecological security. Scientifically, it is necessary to construct an ecological landscape pattern on the basis of ecological security evaluation. This paper selected evaluation factors based on the perspective of “environmental base and human interference”, used spatial principal component analysis (SPCA) to comprehensively evaluate the regional landscape ecological security, and used the minimum cumulative resistance (MCR) model to construct the regional ecological security pattern. The results show that the overall ecological security level of the study area is low, and the area with a moderate safety level and below is 122,100 km2, accounting for 72.57% of the total area of the study area. The total area of the identified ecological source area is 6683 km2, the spatial distribution is extremely uneven, and it is extremely concentrated in the southern region; 32 ecological corridors with a total length of 3817.8 km are identified, of which corridor NO. 1–4 run through the Qilian Mountains, 11 oasis areas, and 14 nature reserves. The length of ecological corridors is 1376.1 km, accounting for 36.04% of the total. Forty-two ecological nodes are identified, and the central corridor area is more distributed; four ecological restoration zones are divided, including an ecological conservation zone, ecological improvement zone, ecological control zone, and ecological restoration zone, with areas of 34,380.3 km2, 61,884.4 km2, 21,134.4 km2, and 50,648.3 km2, respectively. Through the delineation of the urban ecological network pattern composed of source areas, corridors, and nodes, as well as the delineation of ecological restoration zones, the ecological security level of the study area will be effectively improved. Furthermore, a new method of ecological restoration zoning will be used, hoping to provide a useful reference for improving the quality of the ecological environment in arid areas and optimizing the spatial pattern of the land.

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“…For the clustering process, six popular methods were employed, such as Mini Batch K-Means [ 32 ], Spectral Clustering [ 33 ], Ward [ 34 , 35 ], Average Linkage [ 36 , 37 ], Balanced Iterative Reducing and Clustering using Hierarchies (Birch) [ 38 , 39 ], and Jenks natural breaks optimization method (Jenks) [ 40 , 41 , 42 ]. Clustering was performed on the values of the variable mood_change, that represents the change in mood state ( Figure 2 ).…”

Section: Methodsmentioning

confidence: 99%

An Explainable Machine Learning Approach for COVID-19’s Impact on Mood States of Children and Adolescents during the First Lockdown in Greece

Ntakolia

Priftis

Charakopoulou-Travlou

et al. 2022

Healthcare

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The global spread of COVID-19 led the World Health Organization to declare a pandemic on 11 March 2020. To decelerate this spread, countries have taken strict measures that have affected the lifestyles and economies. Various studies have focused on the identification of COVID-19’s impact on the mental health of children and adolescents via traditional statistical approaches. However, a machine learning methodology must be developed to explain the main factors that contribute to the changes in the mood state of children and adolescents during the first lockdown. Therefore, in this study an explainable machine learning pipeline is presented focusing on children and adolescents in Greece, where a strict lockdown was imposed. The target group consists of children and adolescents, recruited from children and adolescent mental health services, who present mental health problems diagnosed before the pandemic. The proposed methodology imposes: (i) data collection via questionnaires; (ii) a clustering process to identify the groups of subjects with amelioration, deterioration and stability to their mood state; (iii) a feature selection process to identify the most informative features that contribute to mood state prediction; (iv) a decision-making process based on an experimental evaluation among classifiers; (v) calibration of the best-performing model; and (vi) a post hoc interpretation of the features’ impact on the best-performing model. The results showed that a blend of heterogeneous features from almost all feature categories is necessary to increase our understanding regarding the effect of the COVID-19 pandemic on the mood state of children and adolescents.

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Economic, Social, and Ecological Impact Evaluation of Traffic Network in Beijing–Tianjin–Hebei Urban Agglomeration Based on the Entropy Weight TOPSIS Method

Cited by 25 publications

References 27 publications

RETRACTED: Mental Health Identification of Children and Young Adults in a Pandemic Using Machine Learning Classifiers

RETRACTED: Mental Health Identification of Children and Young Adults in a Pandemic Using Machine Learning Classifiers

Research on Landscape Pattern Construction and Ecological Restoration of Jiuquan City Based on Ecological Security Evaluation

An Explainable Machine Learning Approach for COVID-19’s Impact on Mood States of Children and Adolescents during the First Lockdown in Greece

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