The innovation effect of administrative hierarchy on intercity connection: The machine learning of twin cities

Luo, Ji; Wang, Yahua; Li, Guangqin

doi:10.1016/j.jik.2022.100293

Cited by 72 publications

(12 citation statements)

References 50 publications

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“…We find that the absence of goals and plannings is the premise for the effectiveness of it. That is, we should weaken the role of planning tools in the process of industrial integration, which correlates with previous views that government’s role in industrial planning has undergone a transition from control to consultation [ 100 , 101 ]. Meanwhile, mandatory tools such as supervision and regulation can have an immediate and significant impact, considerably counteracting the gradual effects of planning and goals, thus to some extent realize the replacement of them.…”

Section: Discussionsupporting

confidence: 69%

How policy promotes the integration of culture and tourism? A fuzzy-set qualitative comparative analysis based on the Policy Instrument Theory

Tang,

Yi,

2024

PLoS ONE

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Crafting pertinent policies to facilitate the high-level integration of culture and tourism has now become a vital agenda within the current discourse in China. However, relatively little is known about the actual implementation of various policies to achieve a high-level integration, especially how combinations of policy instruments are deployed in the process of realization. Based on the Policy Instrument Theory, this study uses fuzzy-set qualitative comparative analysis on a sample of 31 provincial administrative regions in China to investigate the influence of typical policy instruments on the integration level of tourism and culture. The results show that each single policy tool is not necessary for high-level integration of culture and tourism. On the contrary, only through an organic combination of different policy tools can affect the integration level. This study also summarizes five policy instrument configurations, which can be grouped into four driving modes of culture-tourism integration: the environment-driven supply-demand coordination mode, supply-driven demand-environment coordination mode, supply-driven mode, and supply-driven environment coordination mode. This study considerably provides critical theoretical and practical insights into the integration of culture and tourism from the perspective of governmental policies.

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Section: Discussionsupporting

confidence: 69%

How policy promotes the integration of culture and tourism? A fuzzy-set qualitative comparative analysis based on the Policy Instrument Theory

Tang,

Yi,

2024

PLoS ONE

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“…In previous studies, other machine learning models, such as generative adversarial networks (GAN) [41], boosted regression tree (BRT), random forest (RF) [107], and support vector machine (SVM) [108], were also studied and evaluated and may be considered in future research. Models such as cellular automata (CA) and Markov models, for example, have been used in a variety of domains such as urban planning, land use forecasting, ecology, and climate change modelling [109][110][111]. For 2021, the estimation of the LST showed sufficiently high accuracy when compared with the actual LST, with a standard deviation of 3.3 • C and 3.5 • C (Table 6) and MAE equal to 3.6 • C and 3.4 • C (Table 7) in winter and summer, respectively, which is in line with the acceptable error of ±3 • C for LST calculation by the algorithms provided [97].…”

Section: Discussionmentioning

confidence: 99%

Modeling and Estimating the Land Surface Temperature (LST) Using Remote Sensing and Machine Learning (Case Study: Yazd, Iran)

Mansourmoghaddam,

Rousta,

Ghafarian Malamiri

et al. 2024

Remote Sensing

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The pressing issue of global warming is particularly evident in urban areas, where urban thermal islands amplify the warming effect. Understanding land surface temperature (LST) changes is crucial in mitigating and adapting to the effect of urban heat islands, and ultimately addressing the broader challenge of global warming. This study estimates LST in the city of Yazd, Iran, where field and high-resolution thermal image data are scarce. LST is assessed through surface parameters (indices) available from Landsat-8 satellite images for two contrasting seasons—winter and summer of 2019 and 2020, and then it is estimated for 2021. The LST is modeled using six machine learning algorithms implemented in R software (version 4.0.2). The accuracy of the models is measured using root mean square error (RMSE), mean absolute error (MAE), root mean square logarithmic error (RMSLE), and mean and standard deviation of the different performance indicators. The results show that the gradient boosting model (GBM) machine learning algorithm is the most accurate in estimating LST. The albedo and NDVI are the surface features with the greatest impact on LST for both the summer (with 80.3% and 11.27% of importance) and winter (with 72.74% and 17.21% of importance). The estimated LST for 2021 showed acceptable accuracy for both seasons. The GBM models for each of the seasons are useful for modeling and estimating the LST based on surface parameters using machine learning, and to support decision-making related to spatial variations in urban surface temperatures. The method developed can help to better understand the urban heat island effect and ultimately support mitigation strategies to improve human well-being and enhance resilience to climate change.

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“…ANN can figure out the relationship between the inputs and outputs without the need for complex mathematical or computational methods. Among the various types of ANN, the Multilayer Perceptron (MLP-ANN) stands out as the most commonly used 28 – 30 . The MLP includes three layers, namely input, hidden, and output layers 31 , 32 , as illustrated in Fig.…”

Section: Methodsmentioning

confidence: 99%

Application of power-law committee machine to combine five machine learning algorithms for enhanced oil recovery screening

Yousefzadeh,

Kazemi,

Al-Maamari

2024

Sci Rep

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One of the main challenges in screening of enhanced oil recovery (EOR) techniques is the class imbalance problem, where the number of different EOR techniques is not equal. This problem hinders the generalization of the data-driven methods used to predict suitable EOR techniques for candidate reservoirs. The main purpose of this paper is to propose a novel approach to overcome the above challenge by taking advantage of the Power-Law Committee Machine (PLCM) technique optimized by Particle Swam Optimization (PSO) to combine the output of five cutting-edge machine learning methods with different types of learning algorithms. The PLCM method has not been used in previous studies for EOR screening. The machine learning models include the Artificial Neural Network (ANN), CatBoost, Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The CatBoost is used for the first time in this work for screening of EOR methods. The role of the PSO is to find the optimal values for the coefficients and exponents of the power-law model. In this study, a bigger dataset than those in previous studies, including 2563 successful worldwide EOR experiences, was gathered. A bigger dataset improves the generalization of the data-driven methods and prevents overfitting. The hyperparameters of the individual machine-learning models were tuned using the fivefold cross-validation technique. The results showed that all the individual methods could predict the suitable EOR method for unseen cases with an average score of 0.868. Among the machine learning models, the KNN and SVM had the highest scores with a value of 0.894 and 0.892, respectively. Nonetheless, after combining the output of the models using the PLCM method, the score of the predictions improved to 0.963, which was a substantial increase. Finally, a feature importance analysis was conducted to find out the most influential parameters on the output. The novelty of this work is having shown the ability of the PLCM technique to construct an accurate model to overcome the class-imbalance issue in EOR screening by utilizing different types of data-driven models. According to feature importance analysis, oil gravity and formation porosity were recognized as the most influential parameters on EOR screening.

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The innovation effect of administrative hierarchy on intercity connection: The machine learning of twin cities

Cited by 72 publications

References 50 publications

How policy promotes the integration of culture and tourism? A fuzzy-set qualitative comparative analysis based on the Policy Instrument Theory

How policy promotes the integration of culture and tourism? A fuzzy-set qualitative comparative analysis based on the Policy Instrument Theory

Modeling and Estimating the Land Surface Temperature (LST) Using Remote Sensing and Machine Learning (Case Study: Yazd, Iran)

Application of power-law committee machine to combine five machine learning algorithms for enhanced oil recovery screening

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