An Effective Prediction Method for Supporting Decision Making in Real Estate Area Selection DOI Open Access

Haoying Jin,

Yang Song,

Mingzhi Zhao

и другие.

Journal of Computer and Communications, Год журнала: 2024, Номер 12(07), С. 105 - 119

Опубликована: Янв. 1, 2024

Real estate has been a dominant industry in many countries. One problem for real companies is determining the most valuable area before starting new project. Previous studies on this issue mainly focused market needs and economic prospects, ignoring impact of natural disasters. We observe that disasters are important selection because they will introduce considerable losses to enterprises. Following observation, we first develop self-defined indicator named Average Loss Ratio predict caused by an area. Then, adopt existing ARIMA model After that, propose integrate TOPSIS Grey Prediction Model rank recommendation levels candidate areas, thereby assisting their decision-making process. conduct experiments datasets validate our proposal, results suggest effectiveness proposed method.

Язык: Английский

Forecasting microhardness, surface roughness and metal removal rate in electrical discharge machining using GM(1,N|sin) power model DOI
G. S. Reddy, K. Venkata Rao, Y. Prasanna Kumar

и другие.

The International Journal of Advanced Manufacturing Technology, Год журнала: 2024, Номер 135(9-10), С. 4695 - 4713

Опубликована: Ноя. 13, 2024

Язык: Английский

Процитировано

0
Application of Thermal Buffer Spaces in the Renovation of Rural Dwellings for Nearly Zero Energy Consumption in Severe Cold Regions of China DOI
Gang Yao, Xing Guo,

Zhijun Qian

и другие.

Journal of Building Engineering, Год журнала: 2024, Номер unknown, С. 111543 - 111543

Опубликована: Дек. 1, 2024

Язык: Английский

Процитировано

0
An Effective Prediction Method for Supporting Decision Making in Real Estate Area Selection DOI Open Access

Haoying Jin,

Yang Song,

Mingzhi Zhao

и другие.

Journal of Computer and Communications, Год журнала: 2024, Номер 12(07), С. 105 - 119

Опубликована: Янв. 1, 2024

Real estate has been a dominant industry in many countries. One problem for real companies is determining the most valuable area before starting new project. Previous studies on this issue mainly focused market needs and economic prospects, ignoring impact of natural disasters. We observe that disasters are important selection because they will introduce considerable losses to enterprises. Following observation, we first develop self-defined indicator named Average Loss Ratio predict caused by an area. Then, adopt existing ARIMA model After that, propose integrate TOPSIS Grey Prediction Model rank recommendation levels candidate areas, thereby assisting their decision-making process. conduct experiments datasets validate our proposal, results suggest effectiveness proposed method.

Язык: Английский

Процитировано

0