Enhancing water level prediction through model residual correction based on Chaos theory and Kriging

Abstract

SUMMARY Hydrodynamic models based on the physical processes are indispensable tools for predicting water levels in ocean environment. Nonetheless, their accuracies are limited by various factors such as simplifying assumptions, complex ocean bathymetry, and so on. Residual correction, as one of the data assimilation techniques, can extract information from observation and assimilate it into a numerical model to correct the model output directly. Such correction is often performed in two steps: prediction of the model residuals at measured stations followed by spatial distribution at non‐measured locations. For long‐term residual forecast, the accuracy of prediction usually deteriorates with the forecast horizon. In addition to the residual correction at measurement locations, in this paper, we address the critical question as to how to effectively update outputs for computational points without measurements. We develop a hybrid data assimilation procedure, which combines a modified local model (MLM) and an approximated ordinary kriging (AOK). This technique improves the forecasts over a long horizon over the entire computational domain. Using the proposed residual correction technique, the hybrid procedure is examined on a case study of Singapore Regional Model for correcting the water level outputs at locations with and without measurements. In order to provide a comparison, the analysis is carried out throughout prediction horizon of model residuals, tidal residuals, and sea level anomaly, respectively. The comparisons show that the proposed method can successfully assimilate and forecast all variables. Results indicate that resulting prediction accuracy can be significantly improved for all locations of interest independently of the forecast horizon. Copyright © 2014 John Wiley & Sons, Ltd.

Improved sea level anomaly prediction through combination of data relationship analysis and genetic programming in Singapore Regional Waters

“…Increasing spatial and temporal data coverage, better quality and reliability of data modelling and data driven techniques are becoming more favourable and acceptable by the hydrodynamic community. The data mining tools and techniques are being applied in variety of hydroinformatics applications ranging from simple data mining for pattern discovery to data driven models and numerical model error correction (Babovic et al, 2001Sannasiraj et al, 2005;Sun et al, 2010;Rao and Babovic, 2010;Karri et al, 2013Karri et al, , 2014Wang and Babovic, 2014). The objectives of this paper is to explore the feasibility of applying average mutual information (AMI) theory by evaluating the amount of information contained in observed and prediction errors of non-tidal barotropic numerical modelling (i.e.…”

Chaotic Identification of Hourly and Daily Water Level Time Series Data in Different Areas of Elevation at Pahang River

“…As the world is changing into a modern world, vast knowledge and applications on chaos approach have been developed including the implementation of chaos in science and engineering as well as in hydrology area. Through literature studies, the chaotic analysis on water level systems is widely applied in many countries such as in China (Huang, Huang, Jiang, & Zhou, 2017), Singapore (Wang & Babovic, 2014) and Australia (Tongal & Berndtsson, 2014). Literally, different water areas have different geographical characteristics which may contribute to the accuracy of the prediction using chaotic approach.…”

Prediction of Water Level Time Series Data for Dam at Selangor using Chaotic Approach and Local Linear Approximation Method

“…Tetapi, peramalan menggunakan pendekatan kalut memberikan hasil peramalan yang lebih cemerlang berbanding kaedah ANN (Kumar et al, 2019). Pelbagai kajian telah dilaksanakan dengan mengaplikasikan pendekatan kalut terhadap data siri masa hidrologi di luar negara dan di Malaysia antaranya adalah aliran sungai (Ghorbani et al, 2018;Adenan & Noorani, 2016), aras air pantai (Wang & Babovic, 2014), aras air tasik (Shiri et al, 2016), aras air sungai (Mashuri et al, 2019) dan hujan (Chettih et al, 2015).…”