Asia Pacific Academy of Science Pte. Ltd. (APACSCI) specializes in international journal publishing. APACSCI adopts the open access publishing model and provides an important communication bridge for academic groups whose interest fields include engineering, technology, medicine, computer, mathematics, agriculture and forestry, and environment.
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.
The protection planning of small and micro habitats in urban fringe areas: Enlightenment from the management of high natural value farmland in the EU to the maintenance of bio-diversity in urban areas in China
Vol 5, Issue 1, 2024
Issue release: 31 December 2024
VIEWS - 4087 (Abstract)
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Abstract
The habitat unit in urban fringe is an important link to support regional biodiversity; due to the influence of urban system and agricultural system, most of them are semi natural, appeared with diverse types, small patches, scattered layout, and generally relying on low intensity farming activities. Export-oriented expansion of urban construction land and intensive agricultural production are prone to reduce the scale and quality of semi natural habitats in urban fringe areas; and the existing measures cannot effectively protect them. High natural value farmland management is an effective measure of the EU to protect semi-natural habitats related to agriculture and maintain regional biodiversity. Through combing the main points of high natural value farmland such as type definition, evaluation, identification, maintenance management, etc., this paper proposes the suggestions for optimization of small and micro semi-natural habitat protection in China: (1) strengthening the recognition of the value of small and microhabitats and related land use; (2) identifying small and micro-habitats that are conducive to the maintenance of biodiversity, and incorporating them in the overall protection of regional network; (3) combining the rigidity and flexibility of land use control, with the consideration of complex function demands; (4) Converging with the existing statutory planning to enhance the protection efficiency of small and micro habitats.
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Kastamonu University,
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