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Modelling the economic effect of inbound birth tourism: a random forest algorithm approach

Quality and Quantity 2024年第5期58卷 4223-4240页

作者： Solarin, Sakiru Adebola Gorus, Muhammed Sehid Ozgur, Onder Faculty of Business Multimedia University Melaka Malaysia Department of Economics Faculty of Political Sciences Ankara Yildirim Beyazit University Ankara Turkey

Due to the tourism sector being one of the main drivers of economic growth in several countries, many studies have been conducted on the economic impact of the tourism sector. However, the economic effect of the different tourism activities, especially birth tourism has not been sufficiently explored in the existing literature. Using different proxies that capture the development of birth tourism, we examine the economic effect of birth tourism on economic growth in New Zealand for the period, 1980–2019. Novel machine-learning techniques including random forest algorithm and partial dependence plots have been used to estimate the relationship between the variables in a framework, which also include capital stock and financial development. The empirical findings of this study show that there is a non-linear relationship between per capita income and birth tourism-related variables in New Zealand. These results offer fresh insights to policymakers on the importance of birth tourism. © The Author(s), under exclusive licence to Springer Nature B.V. 2024.

关键词： Birth tourism Economic growth Machine learning New Zealand random forest algorithm Tourism-led hypothesis

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Photovoltaic Power Generation Forecasting Based on random forest algorithm 8

Photovoltaic Power Generation Forecasting Based on Random Fo...

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8th International Conference on Green Energy and Applications (ICGEA)

作者： Yu, TuoLiang Liang, Huang Jian, Liu Wuhan Univ Technol Sch Automat Wuhan Peoples R China

ISBN: (纸本)9798350349009;9798350349016

Since the 14th Five-Year Plan, the strategy of "carbon neutrality and carbon peak" has become one of the important strategies to realize China's economic and environmental development, and the clean energy structure will be the main way to achieve the double carbon goal. Solar energy has become one of the important energy sources because of its large resource amount and wide distribution range, and photovoltaic power generation technology is an important means to control solar power generation. The accuracy of photovoltaic power generation prediction plays an important role in the allocation and use of energy, so as to maximize the use of energy. However, unlike the continuous adjustable and controllable traditional power generation, solar power generation cannot be accurately controlled, and it has a great randomness, which brings great challenges to photovoltaic power generation. In order to make full use of solar energy resources and ensure the real-time balance of power generation, transmission and consumption of the power system when photovoltaic power generation is connected to the grid, it is crucial to accurately predict the power of photovoltaic power generation. In order to solve the problems of unsatisfactory prediction accuracy and poor robustness when training set missing data, this paper adopts K-means algorithm for similar day screening and combines it with random forest algorithm(RF) to construct a photovoltaic power generation prediction model. Through similar day screening, more accurate and applicable prediction models can be obtained for forecasting days;at the same time, the robustness of the model is improved by using random forest model. Through verification with data from a power plant in Wuhan, Hubei Province, the accuracy and stability of K-means-RF algorithm in photovoltaic power generation prediction are proved.

关键词： double carbon solar energy photovoltaic power generation random forest algorithm K-means algorithm

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An evaluation of methods to handle missing data in the context of latent variable interaction analysis: multiple imputation, maximum likelihood, and random forest algorithm

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JAPANESE JOURNAL OF STATISTICS AND DATA SCIENCE 2022年第2期5卷 629-659页

作者： Shin, Tacksoo Long, Jeffrey D. Davison, Mark L. Myongji Univ Dept Youth Educ & Leadership Seoul South Korea Univ Iowa Dept Psychiat Iowa City IA 52242 USA Univ Minnesota Twin Cities Dept Educ Psychol Minneapolis MN USA

The objective of this study was to examine the performance of the two most popular missing data methods (i.e., multiple imputation and maximum likelihood), as well as newly developed machine learning framework based on random forest algorithm for missing data under various reserach conditions. The design of the simulation study included random and non-random missingness (i.e., MCAR, MAR, and MNAR), small samples, and different levels of missing rates. All statistical inferences were investigated using latent variable interaction modeling. Consistent with the missing data literature, the combined effects of small sample sizes, higher missing rates, and non-ignorable missingness along with complicated modeling structure adversely affected the accuracy of statistical inferences. Although there is a possibility for overparameterization, it is a good way to select MI when convergence is concerned. If the primary goal of research is to investigate the relationship between variables as in many studies, ML would be attractive. MF presented similar performance compared to MI and ML across all research conditions and outperformed when estimating the variability of parameter estimates. Other practical issues pertaining to the missing data methods were also discussed.

关键词： Missing data Multiple imputation Maximum likelihood estimation random forest algorithm Latent interaction model

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Wind Power Forecasting Using Parallel random forest algorithm 8th

Wind Power Forecasting Using Parallel Random Forest Algorith...

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8th International Conference on Soft Computing for Problem Solving (SocProS)

作者： Natarajan, V. Anantha Kumari, N. Sandhya Sree Vidyanikethan Engn Coll Dept Comp Sci & Engn Tirupati Andhra Pradesh India

ISBN: (纸本)9789811500350;9789811500343

Wind power keeps on developing and it is broadly observed as the sustainable power source best capable to compete with fossil fuel electricity generation. In the near past wind power forecasting has improved the situation for the estimation of power production in wind farms. In general wind power forecasts are generated in two different ways one namely using Numerical Weather Prediction (NWP) and the other one using physical forecasting methods. Physical forecasting is deeply dependent on meteorological facts and the data from the NWP. The approach of physical method is vulnerable by the way that wind speeds are estimated a few feet over the ground can fluctuate. Statistical scheme encompasses models likewise ANN, SVM, and etc. less reliant on accuracy of Numerical Weather Predictions (NWP), yet relies more extremely on historical information of wind speed at respective areas. To compute large accurate wind energy forecast a good amount of real-time observations of historical observations from the wind farms becomes essential. Wind power forecasts using Support Vector Machines (SVM) and Artificial Neural Networks (ANN) suffers from slow training speed, and poor generalization ability. This paper aims at conducting experiments to assess the performance and test the suitability of the random forest algorithm for wind power forecasting. The prediction results are seeming to be close with the actual wind power generated at the wind farms and it is more accurate when compared to the results of the ANN.

关键词： Wind power forecasting Numerical weather predictions random forest algorithm Support vector machines Artificial neural networks

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THE PREDICTION OF FATIGUE LIFE BASING random forest algorithm

THE PREDICTION OF FATIGUE LIFE BASING RANDOM FOREST ALGORITH...

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ASME International Mechanical Engineering Congress and Exposition (IMECE)

作者： Yin, Chenfei Yang, Yu China Aircraft Strength Res Inst Xian Peoples R China

ISBN: (纸本)9780791885680

The fatigue performance of test pieces is sensitive to various influence factors. If one factor changes, the fatigue life will differ greatly. For the changes of each factors, the fatigue test must be carried on, which will increase the test cost. In this paper, in order to solve this problem, basing the machine learning method, we establish the random forest regression model to conduct a material fatigue fracture life prediction research for the 7050 T7451 aluminum alloy. For the 7050-T7451 aluminum alloy standard smooth test pieces considering six detailed factors, the fatigue test is carried out at two stress levels to obtain the fatigue fracture life. Firstly, the fatigue test data are pretreated in this paper. And the fatigue test conditions of each group are different from each other, so there are six attributes of the test conditions, including load, processing technology, roughness, material direction, thickness of the parent metal and raw material position. The test data from sets 2 to 10 are selected and randomly divided into training set and verification set with a ratio of 4:1, and the first set data was reserved as the test set. Secondly, the random forest regression model is established. And then the random forest model is trained. The model is evaluated according to the R-2 determination coefficient, and the R-2 determination coefficient is 0.49 after adjusting the hyperparameters of the random forest model on the verification set. It is found that the true values of the tests are all within the fatigue dispersion band four times of the predicted values. Considering the fatigue dispersibility, it is a reasonable learning model. Finally, the model is verified by the first set of test data, and the accuracy of the predicted value of the first test set is 87.7% relative to the test mean value, which the predicted result is good. Processing technology, roughness, material direction, thickness of the parent metal and raw material position can form 162 expe

关键词： random forest algorithm Fatigue life Data mining 7050 T7451 aluminum alloy

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Landslide Hazard Assessment in Minjiang River Basin Based on GIS and random forest algorithm 24

Landslide Hazard Assessment in Minjiang River Basin Based on...

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International Conference on algorithms, Software Engineering, and Network Security (ASENS)

作者： Cheng, Yashan Chen, Qiuhe Yu, Yingzhuo Xia, Yuling Officers Coll PAP Dept Informat & Commun Chengdu 610000 Sichuan Peoples R China Nanjing Inst Environm Sci Minist Ecol & Environm Nanjing 210000 Jiangsu Peoples R China

ISBN: (纸本)9798400709784

Disaster risk assessment is an effective tool for landslide disaster management. Machine learning models can effectively avoid the subjectivity of determining the weights of factors in disaster risk assessment, which is a hotspot of current research. We evaluate the risk of landslide disaster in Minjiang River Basin by random forest algorithm based on characters include rainfall, slope, vegetation cover, distance to river and distance to fault. The results show that landslides in the Minjiang River Basin are closely related to the distance to the river and the distance to the faults. About 11.5% of the total area of the Minjiang River Basin is susceptible to small landslides.

关键词： Minjiang River Basin landslide hazard evaluation GIS random forest algorithm

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Recognition of beta-alpha-beta Motifs in Proteins by Using random forest algorithm

Recognition of beta-alpha-beta Motifs in Proteins by Using R...

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6th International Conference on Biomedical Engineering and Informatics (BMEI)

作者： Sun, Lixia Hu, Xiuzhen Inner Mongolia Univ Technol Coll Sci Hohhot Peoples R China

ISBN: (纸本)9781479927616

A beta-alpha-beta motif was dataset constructed by using the Definition of Secondary Structure of Proteins (DSSP) and PROMOTIF software, that analyzes a protein coordinate file and provides details about the structural motifs in the protein. We performed a statistical analysis on beta-alpha-beta motifs and non-beta-alpha-beta motifs, and the study objects that loop-helix-loop length was from 10 to 26 amino acids were selected. Hydropathy component of position and amino acid composition were combined as characteristic parameter for expressing the sequence characteristics. A random forest algorithm for predicting beta-alpha-beta motifs was developed. The overall accuracy and Matthew's correlation coefficient of 5-fold cross-validation achieved 88.9% and 0.78.

关键词： Complex super-secondary structure random forest algorithm Hydropathy component of position Structure prediction

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Extrapolation and Visualization of NPA Using Feature Based random forest algorithm in Indian Banks

Extrapolation and Visualization of NPA Using Feature Based R...

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International Conference on Computing, Analytics and Networking (ICCAN)

作者： Arthi, J. Akoramurthy, B. Kongu Engn Coll Perundurai India IFET Coll Engn Villupuram India

ISBN: (纸本)9789811078712;9789811078705

The instigation of Non-Performing Assets (NPAs) in Indian banks was post 2009, when world was doing quantitative easing (QE) to save them off recession in 2008. The problem of NPA in India has witnessed the gross NPA rise to 79.7% and net NPA from 2.8% in September 2015 to 4.6% in March 2016. Banking has its NPA data increasing year by year which is a serious concern for the Indian banks. In this paper, an algorithm to determine and predict NPA is proposed. The proposed algorithm creates a pattern based on NPA data sets from various banks, and features are extracted to predict and eradicate financial debts. Also, a visual banking dashboard is developed exclusively for NPAs. The learning task in the proposed Feature based random forest algorithm is carried out by incorporating features extracted from the dataset considered and hence improves the prediction accuracy. The result of prediction is visualized using Tableau which, not only provides insights on the data but also aids in data-driven decision making.

关键词： NPA Data visualization random forest algorithm Prediction

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Predicting Video Website DAU by random forest algorithm

Predicting Video Website DAU by Random Forest Algorithm

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IEEE Conference on Telecommunications, Optics and Computer Science (TOCS)

作者： Dai, Jianhua Guo, Xiao Jin, Libo Commun Univ China Sch Econ & Management Beijing Peoples R China

ISBN: (纸本)9781728181172

With the rise of major video websites, the scale of video website users is expanding. Users are the core of all video websites, so it is of great significance to study video website DAU to evaluate and guide its operation. Based on the data of a video website DAU and platform layout from January 2017 to December 2018, this article analyzes the influencing factors of the video website DAU, selects 10 influencing factors from broadcasting arrangement, content level, content mode, and winter and summer vacations, constructs a random forest model, makes regression prediction on the daily average DAU of the video website and calculates the importance of 10 variables. The results show that the most important predictor of DAU is the self-produced drama, followed by the number of variety shows S-level, variety shows A-level and TV series. Video websites can use the method to predict DAU, and improve their operation from the above predictors.

关键词： video websites random forest algorithm DAU

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Analysis of Precision Agriculture based on random forest algorithm by using Sensor Networks 5

Analysis of Precision Agriculture based on Random Forest Alg...

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5th International Conference on Inventive Computation Technologies (ICICT)

作者： Pavithra, K. Jayalakshmi, M. Natl Engn Coll Comp Sci & Engn Kovilpatti Tamil Nadu India

ISBN: (纸本)9781728146850

India is the largest country where most of the human lives depends upon agriculture. Due to the shortage of water facilities, the yield of irrigation is affected. To reduce this shortage of water, different types of methods are invented to supply the correct level of water to the agricultural lands. Here precision irrigation system is used to maintain the perfect water supply from this method wastage of water can be reduced. By using this technique, we can achieve better harvesting results. The total amount of water for lands and plants can be calculated by using the PH value. To detect the PH level, a random forest algorithm is used. There is a direct relationship between the number of plants in the land and the results it can get. It improves the prediction accuracy.

关键词： Irrigation Precision Agriculture random forest algorithm Water Prediction

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