A Hybrid Population Distribution Prediction Approach Integrating LSTM and CA Models with Micro-Spatiotemporal Granularity: A Case Study of Chongming District, Shanghai

Abstract

Studying population prediction under micro-spatiotemporal granularity is of great significance for modern and refined urban traffic management and emergency response to disasters. Existing population studies are mostly based on census and statistical yearbook data due to the limitation of data collecting methods. However, with the advent of techniques in this information age, new emerging data sources with fine granularity and large sample sizes have provided rich materials and unique venues for population research. This article presents a new population prediction model with micro-spatiotemporal granularity based on the long short-term memory (LSTM) and cellular automata (CA) models. We aim at designing a hybrid data-driven model with good adaptability and scalability, which can be used in more refined population prediction. We not only try to integrate these two models, aiming to fully mine the spatiotemporal characteristics, but also propose a method that fuses multi-source geographic data. We tested its functionality using the data from Chongming District, Shanghai, China. The results demonstrated that, among all scenarios, the model trained by three consecutive days (ordinary dates), with the granularity of one hour, incorporated with road networks, achieves the best performance (0.905 as the mean absolute error) and generalization capability.

Aeroengine performance degradation prediction method considering operating conditions

“…In the field of forecasting, a large of researchers have put forward many methods ranging from simple linear regression to advance nonlinear forecasting [2]. Performance degradation prediction theory is becoming more and more mature, and the actual application effect of engineering is also improving.…”

“…and parameters used by(2). (1) Calculation process of ( ( ) | (1))f o t O t − It can be found from (4), which ( ( ) | ( 1))f o t O t −requires to be calculated.…”

Prediction of Population Distribution in 2030 Using the Integration of the Ca-Ann Land Cover Change Method With Numeric Extrapolation in Karawang-Bekasi, Indonesia

“…Second, the study conducted by (Yoezer Tenzin, 2019), who modelled the distribution of the population using the Third Clark model, research conducted by (Rahimi et al, 2021) modelled the spatiotemporal of the population with taxi origin and destination data that utilizes GPS data from taxis. In the four studies conducted by (Wang et al, 2021), who conducted population distribution prediction modelling using the integration of LSTM and CA models with micro-spatiotemporal granularity Based on previous studies, it can be seen that there is no modelling and prediction of future population distribution by integrating changes in CA-ANN land cover with numeric extrapolation. Thus, this study developed a prediction of population distribution in 2030 by integrating the CA-ANN land cover change model with the numeric extrapolation model in predicting population change.…”