Predicting the Water Level Fluctuation in an Alpine Lake Using Physically Based, Artificial Neural Network, and Time Series Forecasting Models

Chih-Chieh Young,Wan-Lin Hsieh,Wen-Cheng Liu

doi:10.1155/2015/708204

Abstract

Accurate prediction of water level fluctuation is important in lake management due to its significant impacts in various aspects. This study utilizes four model approaches to predict water levels in the Yuan-Yang Lake (YYL) in Taiwan: a three-dimensional hydrodynamic model, an artificial neural network (ANN) model (back propagation neural network, BPNN), a time series forecasting (autoregressive moving average with exogenous inputs, ARMAX) model, and a combined hydrodynamic and ANN model. Particularly, the black-box ANN model and physically based hydrodynamic model are coupled to more accurately predict water level fluctuation. Hourly water level data (a total of 7296 observations) was collected for model calibration (training) and validation. Three statistical indicators (mean absolute error, root mean square error, and coefficient of correlation) were adopted to evaluate model performances. Overall, the results demonstrate that the hydrodynamic model can satisfactorily predict hourly water level changes during the calibration stage but not for the validation stage. The ANN and ARMAX models better predict the water level than the hydrodynamic model does. Meanwhile, the results from an ANN model are superior to those by the ARMAX model in both training and validation phases. The novel proposed concept using a three-dimensional hydrodynamic model in conjunction with an ANN model has clearly shown the improved prediction accuracy for the water level fluctuation.

Highlights

Accurate predictions of water level fluctuation that results from hydrometeorological variations and anthropogenic disturbances [1] are needed for sustainable development and management of lake water usage [2,3,4,5,6,7,8]
The measured data from August 1 to December 31, 2009, was used for calibration, while the data from January 1 to May 31, 2010, was adopted for validation. The performance of these models was comprehensively evaluated by various statistical indices (i.e., mean absolute error (MAE), root mean square error (RMSE), R, and skill score (SS))
The three-dimensional hydrodynamic model satisfactorily presents the temporal variability of water level fluctuation in the calibration phase but somewhat fails to mimic the water level in the validation phase

Summary

Introduction

Accurate predictions of water level fluctuation that results from hydrometeorological variations and anthropogenic disturbances [1] are needed for sustainable development and management of lake water usage [2,3,4,5,6,7,8]. Lake water level changes seasonally (e.g., high in the wet summer and low in the dry winter) with sharp rising/falling limbs during typhoon events, but not in a simple periodic mode (except for the seiche oscillation that occurs mainly in large lakes). Effective prediction tools play an important role in the studies of lakes. They can be used to simulate the lake water level variations based upon the available measured data and predict the possible responses under different scenarios, supporting management decisions of valuable water resources.

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Results

Conclusion