PROGRESS IN GEOGRAPHY ›› 2014, Vol. 33 ›› Issue (7): 949-957.doi: 10.11820/dlkxjz.2014.07.010

• Orginal Article • Previous Articles     Next Articles

Short-term forecasting of waterlogging at urban storm-waterlogging monitoring sites based on STARMA model

Shanshan ZHENG1,2(), Qing WAN1(), Mingyuan JIA1,2   

  1. 1. State Key Laboratory of Resource and Environmental Information System, Institute of Geographic Science and Natural Resources Research, CAS, Beijing 100101, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Online:2014-07-25 Published:2014-07-25


Storm struck cities frequently and often suddenly in recent years, leading to urban inundation and threatening life and property in these cities. With the establishment of urban storm-waterlogging monitoring network, real-time and time series rainfall and waterlogging data at the temporal resolution of a few minutes can be easily acquired. Real-time monitoring of inundation thus can be achieved and this provides new ways for research of inundation in cities. At present, however, there is a general lack of in-depth data mining and analysis of the observed data, which leads to the fact that urban inundation monitoring systems are used only for monitoring purposes and waterlogging control is not an integrated part of the system. Based on the monitoring data of urban inundation monitoring system, according to the temporal autocorrelation of waterlogging, spatial correlation of rainfall and the correlation of rainfall and waterlogging, a spatial and temporal auto regressive and moving average model (STARMA) has been built for short-term forecasting of waterlogging in this study. Auto regressive and moving average model (ARMA) is one of the correlation analysis methods of time series data. ARMA combined with spatial analysis methods generates the STARMA model. STARMA model is an effective means for modeling the spatiotemporal processes of geographic phenomena, especially when the mechanism of the spatiotemporal processes is unclear or multiple spatial and temporal variables are involved. STARMA model has been applied in traffic prediction, environment variable prediction and the social and economic fields. In this study, the model is applied in rainfall and waterlogging process simulation and short-term forecasting for the first time. In order to simulate rainfall and waterlogging processes, the traditional STARMA univariate model is modified to create a bivariate model of rainfall and waterlogging. Based on urban inundation monitoring data on 12 July 2012 in Beijing, using Fengbei Bridge, Huaxiang Bridge, Majialou Bridge and Liuli bridge as examples, the STARMA models were built respectively to predict water depth with a 5 minute step at 5, 10, and 15 minutes. The modeling process included creating rainfall stations' spatial weight matrix, model identification, parameter estimation and model verification. The STARMA model form was determined by autocorrelation function and partial autocorrelation, in addition to A-information criterion or Bayesian information criterion. Spatial weight values were calculated by inverse distance weighting (IDW). Parameter estimates were derived by the least square method. The simulation results show that the STARMA model predictions fit well with observed data and accuracy of short-term forecasting is high. The root mean square error (RMSE) is about 0.03, the relative square error (RSE) is about 0.01 and the average error rate is about 5%. When the prediction time increased from 5 to 15 minutes, prediction accuracy slightly decreased. This method improves prediction accuracy and reliability as compared to traditional hydro model simulation and prediction. The research uses urban inundation monitoring network data to predict short-term waterlogging. On the one hand it takes full advantage of the monitoring data, and on the other hand it improves the ability of disaster early warning and emergency command, thus provide decision support for related government departments.

Key words: storm waterlogging, short-term forecasting, space-time sequence, STARMA model, temporal autocorrelation, spatial correlation

CLC Number: 

  • P208