Forecasting Emergency Calls with a Poisson Neural Network-Based Assemble Model

Hongyun Huang, Mingyue Jiang, Zuohua Ding, Mengchu Zhou

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Forecasting emergency calls are of great importance in practice. By forecasting the occurrence of unfortunate events, we can learn from these events and further prevent their occurrence in the future. However, because of the uncertainty of event occurrences, it is hard to guarantee their prediction accuracy. In this paper, a combined model, which consists of two parts, is proposed. The first part is a Poisson neural network model (PNN). It is responsible for basic forecasting, and its initial weights and thresholds are trained by applying a genetic algorithm. The second part consists of multiple linear regression (MLR), autoregressive integrated moving average (ARIMA), and multivariate gray (GM), which are responsible for estimating residual errors. The basic prediction result adjusted by the residual error is used as the final forecasting result. The proposed model fully takes the advantages of PNN, MLR, ARIMA, and GM, and thus improves forecasting performance. Our method has been applied to the emergency calls of Ningbo, China. The experimental results show that the proposed model has advantages over some existing forecasting models, such as a neural network model, Poisson regression, and stochastic configuration networks in terms of mean absolute percentage error.

Original languageEnglish (US)
Article number8632890
Pages (from-to)18061-18069
Number of pages9
JournalIEEE Access
Volume7
DOIs
StatePublished - 2019

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

Keywords

  • Accident forecasting
  • Poisson distribution
  • Poisson neural network
  • combined model
  • residual model

Fingerprint

Dive into the research topics of 'Forecasting Emergency Calls with a Poisson Neural Network-Based Assemble Model'. Together they form a unique fingerprint.

Cite this