Title: Prediction method of construction land expansion speed of ecological city based on BP neural network

Authors: Anlin Li; Lede Niu; Yan Zhou

Addresses: Faculty of Geography, Yunnan Normal University, Kunming 650500, China ' Faculty of Geography, Yunnan Normal University, Kunming 650500, China ' Faculty of Geography, Yunnan Normal University, Kunming 650500, China

Abstract: In order to solve the problems of low accuracy and poor convergence of traditional urban construction land expansion speed prediction, a new method based on BP neural network for ecological city construction land expansion speed prediction is proposed. On the basis of driving forces state responses (DSR) research framework model, this paper analyses the main components of the driving mechanism model of urban construction land expansion, finds out the driving factors, and verifies the unit root of its time series, as well as causality verification and screening, to establish the driving mechanism model of urban construction land expansion. After preprocessing the data in the model, the BP neural network is constructed to predict the expansion speed of urban construction land. The experimental results show that the proposed method has better convergence and higher prediction accuracy, which provides a reference for related research.

Keywords: DSR; BP neural network; ecological city; construction land; expansion speed prediction.

DOI: 10.1504/IJETM.2022.120722

International Journal of Environmental Technology and Management, 2022 Vol.25 No.1/2, pp.108 - 121

Received: 07 Dec 2020
Accepted: 14 Sep 2021
Published online: 04 Feb 2022 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article