Prediction of Per Capita Ecological Carrying Capacity Based on ARIMA-LSTM in Tourism Ecological Footprint Big Data

Abstract

Reasonable and effective regional ecological evaluation and analysis methods can be an effective help for urban sustainable development, but there are still some errors in the current ecological prediction and analysis methods. To solve this problem, this paper proposes a prediction method of per capita ecological carrying capacity based on the autoregressive integrated moving average model (ARIMA) and long short-term memory (LSTM). First, the method improves the ecological footprint model based on energy analysis and constructs a comprehensive regional ecological data model; considering the complex characteristics of ecological data set, based on the ARIMA network model and LSTM model, a reliable and efficient big data prediction model of per capita ecological carrying capacity is established by analyzing the linear or nonlinear data sets in the data set. Finally, according to the actual ecological data set collected in Shenzhen, China, the results show that the economic and ecological trend of Shenzhen is generally good.