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As China's pillar industry, the property market has suffered a considerable impact in recent years, with a decline in turnover and many developers at risk of bankruptcy. As one of the most concerned factors for stakeholders, housing prices need to be predicted more objectively and accurately to minimize decision-making errors by developers and consumers. Many prediction models in recent years have been unfriendly to consumers due to technical difficulties, high data demand, and varying factors affecting house prices in different regions. A uniform model across the country cannot capture local differences accurately, so this study compares and analyses the fitting effects of multiple machine learning models using February 2024 new building data in Changsha as an example, aiming to provide consumers with a simple and practical reference for prediction methods. The modeling exploration applies several regression techniques based on machine learning algorithms, such as Stepwise regression, Robust regression, Lasso regression, Ridge regression, Ordinary Least Squares (OLS) regression, Extreme Gradient Boosted regression (XGBoost), and Random Forest (RF) regression. These algorithms are used to construct forecasting models, and the best-performing model is selected by conducting a comparative analysis of the forecasting errors obtained between these models. The research found that machine learning is a practical approach to property price prediction, with least squares regression and Lasso regression providing relatively more convincing results.
Effect of silvicultural treatments on forest diversity and structure in temperate forests under management in Durango, Mexico
Vol 4, Issue 1, 2023
Issue release: 31 December 2023
VIEWS - 3626 (Abstract)
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Abstract
The present study evaluated the effect of silvicultural treatments on the diversity and structure of species in temperate forest ecosystems in the Municipality of Pueblo Nuevo in the State of Durango, Mexico; it was carried out to know if forest use modifies the diversity, mixture of species, spatial distribution, and dimensional differentiation of individuals in these ecosystems. The evaluation was carried out by comparing 10 plots with management history, which were measured before the application of the treatment and five years later. The diversity indices of Shannon, Simpson and Margalef were compared, as well as indices of structure of mixture of species, spatial distribution, and dimensional differentiation. According to the silvicultural treatments applied, the values of the indices do not present significant differences in their evaluations (p> 0.05), which indicates that forest use does not modify the diversity and structure of species of the tree stratum of this plant community.
Keywords
References
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Prof. Mehmet Cetin
Kastamonu University,
Turkey
