Real estate risk measurement and early warning based on PSO-SVM

Zhou, Wenwen; Chen, Mengyao; Yang, Zaoli; Song, Xiaobo

doi:10.1016/j.seps.2020.101001

Cited by 32 publications

(27 citation statements)

References 15 publications

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“…Hopkin (2018) posits that effective risk management is incumbent on the developer's ability to communicate the risk to other project team members for their perusal, analysis and interpretation. This logic is also confirmed by other studies (Fabozzi et al, 2009;Giacoletti, 2021;Oluwayemisi et al, 2018;Zhou et al, 2021), who stresses that the integration of risk knowledge sets the framework for the prioritization and control of idiosyncratic and systematic risks capable of influencing the project. By implication, such a framework sets the tone for the control of risk towards realizing risk management maturity levels for the organization.…”

Section: Discussionsupporting

confidence: 72%

Organizational Learning and Risk Management Maturity: Systematic and Meta-Analyses Approach

Nwaibe

Ogbuefi

Egbenta

2022

BEL

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This paper summarizes the arguments and counterarguments within the scientific discussion on the issue of investigating the role of developers in the economic development of the country. The relevance of this scientific problem decision is that the risk management maturity on the part of developers is often underestimated when researching developing real estate markets. Systematization of the literary sources and approaches for solving the problem of risk management proved the presence of a significant number of studies that confirm the hypothesis that some of the developers have not reached the normalized risk management maturity scale. The main purpose of the paper is to explore the role of organizational learning in enhancing risk management maturity, as a means of setting research agenda for its empirical evaluation in emerging economies. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach, the article systematizes the scientific literature on the importance of organizational learning in managing real estate investment maturity risks in emerging economies like Nigeria. The object of the research was indexed studies on Web of Scholar, Google Scholar, Emerald and Pubmed databases. Qualitative analysis methods have become a methodical toolkit for determining the parameters of organizational learning that contribute to the achievement of risk management maturity. The paper confirms and theoretically proves that risk knowledge acquisition, integration and transformation had potentials of respectively leading to sustainable risk identification, assessment and mitigation in property development projects. The results of the study form the basis for the understanding by scholars, practitioners and policy makers of their contribution to scientific research on the issues of a risk management mature property development sector in emerging economies. The strengths of this study are that it aims to identify the importance of organizational learning in risk management mature property investment in emerging markets.

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Section: Discussionsupporting

confidence: 72%

Organizational Learning and Risk Management Maturity: Systematic and Meta-Analyses Approach

Nwaibe

Ogbuefi

Egbenta

2022

BEL

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“…It is derived from the study of predatory behavior of birds. The basic idea of the PSO algorithm is to find the optimal solution through collaboration and information sharing among individuals in a population ( Zhou et al, 2021b ). Each particle in this algorithm represents a potential solution to the problem.…”

Section: Methodsmentioning

confidence: 99%

Rapid Identification of Soybean Varieties by Terahertz Frequency-Domain Spectroscopy and Grey Wolf Optimizer-Support Vector Machine

et al. 2022

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Different soybean varieties vary greatly in their nutritional value and composition. Screening for superior varieties is also essential for the development of the soybean seed industry. The objective of the paper was to analyze the feasibility of terahertz (THz) frequency-domain spectroscopy and chemometrics for soybean variety identification. Meanwhile, a grey wolf optimizer-support vector machine (GWO-SVM) soybean variety identification model was proposed. Firstly, the THz frequency-domain spectra of experimental samples (6 varieties, 270 in total) were collected. Principal component analysis (PCA) was used to analyze the THz spectra. After that, 203 samples from the calibration set were used to establish a soybean variety identification model. Finally, 67 samples from the test set were used for prediction validation. The experimental results demonstrated that THz frequency-domain spectroscopy combined with GWO-SVM could quickly and accurately identify soybean varieties. Compared with discriminant partial least squares (DPLS) and particles swarm optimization support vector machine, GWO-SVM combined with the second derivative could establish a better soybean variety identification model. The overall correct identification rate of its prediction set was 97.01%.

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“…The SVM algorithm is a supervised machine learning algorithm proposed by Vapnik and Corts in 1995 (Cortes and Vapnik, 1995). It improves the generalization ability of the learning machine by minimizing the empirical risk and structured risk so that when the number of samples is small, a good statistical law can be obtained (Zhou et al, 2020). The core idea of SVM technology is to use kernel function K (Xi, XJ) to map the data to a high-dimensional or even infinite dimensional feature space so that the data can be easily processed by a linear learning machine in the obtained feature space.…”

Section: Svmmentioning

confidence: 99%

Real-time prediction and ponding process early warning method at urban flood points based on different deep learning methods

Zhou

Jiang

et al. 2022

Preprint

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Accurate prediction of urban floods is regarded as one of the critical means to prevent urban floods and reduce the losses caused by floods. However, due to the uncertainty and complexity of urban floods and waterlogging, it is very difficult to use simulation models to quickly and accurately predict and warn of urban floods. Therefore, it is necessary to develop new methods to support the rapid and accurate prediction of urban floods and waterlogging. In this study, a refined prediction and early warning method for urban flood and waterlogging processes based on deep learning methods is proposed from three aspects: research feasibility, method applicability, and method system. The spatial autocorrelation of rain and ponding points is analyzed by Moran's I. For each ponding point, the relationship model between the rainfall process and ponding process is constructed based on different deep learning methods, and the results are analyzed and verified by a statistical evaluation method. The results show that the gradient boosting decision tree algorithm has the highest accuracy and efficiency (with a root mean square error of 0.001) for ponding process prediction and is regarded as the most suitable method for ponding process prediction. Finally, the real-time prediction and early warning of urban floods and waterlogging processes driven by rainfall forecast data are realized, and the results are verified by the measured data. In addition, the results of the sensitivity analysis show that the rainfall peak and location coefficient have the greatest impact on ponding.

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Real estate risk measurement and early warning based on PSO-SVM

Cited by 32 publications

References 15 publications

Organizational Learning and Risk Management Maturity: Systematic and Meta-Analyses Approach

Organizational Learning and Risk Management Maturity: Systematic and Meta-Analyses Approach

Rapid Identification of Soybean Varieties by Terahertz Frequency-Domain Spectroscopy and Grey Wolf Optimizer-Support Vector Machine

Real-time prediction and ponding process early warning method at urban flood points based on different deep learning methods

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