Prediction and modelling of rainfall–runoff during typhoon events using a physically-based and artificial neural network hybrid model

Abstract

The accurate prediction of hourly runoff discharge in a watershed during heavy rainfall events is of critical importance for flood control and management. This study predicts n-h-ahead runoff discharge in the Sandimen basin in southern Taiwan using a novel hybrid approach which combines a physically-based model (HEC-HMS) with an artificial neural network (ANN) model. Hourly runoff discharge data (1200 datasets) from seven heavy rainfall events were collected for the model calibration (training) and validation. Six statistical indicators (i.e. mean absolute error, root mean square error, coefficient of correlation, error of time to peak discharge, error of peak discharge and coefficient of efficiency) were employed to evaluate the performance. In comparison with the HEC-HMS model, the single ANN model, and the time series forecasting (ARMAX) model, the developed hybrid HEC-HMS–ANN model demonstrates improved accuracy in recursive n-h-ahead runoff discharge prediction, especially for peak flow discharge and time.     

Generalized minimum variance control for buildings under seismic ground motion

The generalized minimum variance (GMV) algorithm for the control of civil engineering structures is developed and presented in this paper. This algorithm needs the knowledge of the seismic excitation model to derive the autoregressive moving average exogen model of the structure. Then the GMV control is applied. The control is designed such that the variance of the generalized cost function is minimized. To demonstrate the effectiveness of this control technique, simulation tests using a single‐degree‐of‐freedom structure are performed. The results show that the relative displacement of the structure and the control effort are significantly reduced. Copyright © 2001 John Wiley & Sons, Ltd.     

Inferring the location of catchment characteristic soil moisture monitoring sites. Covariance structures in the temporal domain

Information shortage is a fundamental constraint in catchment hydrology that severely affects the possibilities for secure inference of the generic hydrologic landscape, as well as for secure validation of physically deduced distributed models. The introduction of databases with high enough spatiotemporal resolution to properly reflect generic hydrological catchment characteristics may therefore be considered as an inferential breakthrough. The work presented here is part of a project where observations from such an Australian catchment (the Tarrawarra) are utilised to estimate the discrepancy for individual soil moisture monitoring sites in reflecting generic catchment characteristics. With low enough discrepancy, observation sites may be considered as catchment characteristic soil moisture monitoring (CASMM) sites, thus capturing unbiased catchment characteristics and being well suited to represent the catchment in a monitoring effort. In this particular study, covariance structures in the temporal domain are inferred in order to enable subsequent enquiries regarding CASMM discrepancies. This is accomplished with ARMAX filters applied to the conditional auto- and cross-covariance structures that connect observations of soil moisture to the temporal variation of meteorology. The results suggest that weekly observations of Tarrawarra soil moisture are quite consistent realisations of first order auto-regressive processes, which means that the present state of soil moisture is generally acquired through the past week. With auto-correlative effects filtered out, cross-correlative meteorological effects on Tarrawarra soil moisture are identified and generally represented by the present week's accumulation of rainfall, the present week's accumulation of global radiation, and the previous week's maximum wind speed. After successive filtering of conditional cross-correlative effects, residual time-series observations may be considered as temporally independent, and therefore are well suited for subsequent inferences regarding covariance structures in the spatial domain. Since the exclusion of auto-correlative effects is necessary for unambiguous model interpretation, the estimated cross-correlative parameters should reflect the true nature of underlying physical processes.     

A comparative study for water temperature modelling in a small basin,the Fourchue River,Quebec, Canada

Analysis and forecasting of water temperature are important for water ecological management. The objective of this study is to compare models for water temperature during the summer season for an impounded river. In a case study, we consider hydro-climatic and water temperature data for the Fourchue River (St-Alexandre-de-Kamouraska, Quebec, Canada) between 2011 and 2014. Three different models are applied, which are broadly characterized as deterministic (CEQUEAU), stochastic (Auto-regressive Moving Average with eXogenous variables or ARMAX) and nonlinear (Nonlinear Autoregressive with eXogenous variables or NARX). The efficiency of each model is analysed and compared. The results show that the ARMAX is the best performing water temperature model for the Fourchue River and the CEQUEAU model also simulates water temperature adequately without the overfitting issues that seem to plague the autoregressive models.

EDITOR M.C. Acreman

ASSOCIATE EDITOR R. Hirsch     

结构振动数据数学模型的线性与非线性刻划

对振动测试数据评价参数的数学反演使用时间序列的线性和非线性模型进行了建模,计算结果表明非线性的ＳＥＴＡＲ模型描述测试数据所反映的物理过程比线性ＡＲＭＡＸ模型更趋于合理、全面和客观。非线性ＳＥＴＡＲ模型能较深刻描述被测体系的振动机理和物理行为,为下一步振动数据评判奠定了基础