Lake Water Level Prediction Model Based on Autocorrelation Regressive Integrated Moving Average and Kalman Filtering Techniques – An Empirical Study on Lake Volta Basin, Ghana

Authors : Michael Stanley PEPRAH, Edwin Kojo LARBI

Pages : 1-11

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Publication Date : 2020-12-15

Article Type : Research Paper

Abstract :The continuous decline in lake water levels is not only a major concern but also a daunting challenge to policymakers, demanding a backup technological and policy interventions in context of broader political and socio-economic realities. This study used Lake Volta hydrological system to shed light on the extensive and flexible modelling and simulation capabilities of stochastic models to understand the bigger picture of water level insert ignore into journalissuearticles values(WL); dynamics. The study used Autocorrelation Regressive Integrated Moving Average insert ignore into journalissuearticles values(ARIMA); and Kalman Filtering insert ignore into journalissuearticles values(KF); techniques as the proposed optimal stochastic models for the study area. The first order ARIMA insert ignore into journalissuearticles values(0, 1, 1); was found suitable for predicting the future monthly Lake Volta WL in the presented study based on expert advice and recommendations from existing studies. The statistical performance indicators used were minimum residual error insert ignore into journalissuearticles values(rmin);, maximum residual error insert ignore into journalissuearticles values(rmax);, arithmetic mean error insert ignore into journalissuearticles values(AME);, arithmetic mean squared error insert ignore into journalissuearticles values(AMSE);, arithmetic mean absolute percentage deviation insert ignore into journalissuearticles values(AMAPD);, and arithmetic standard deviation insert ignore into journalissuearticles values(ASD);. Based on the results achieved in this study, ARIMA insert ignore into journalissuearticles values(0, 1, 1); achieved AME, AMSE, AMAPD and ASD of -0.1268 m, 0.0037 m, 0.5749 m, and 0.0033 m respectively. Ensemble of ARIMA and KF was further used to forecast the upcoming monthly WL trends up to December 2048. ARIMA insert ignore into journalissuearticles values(0, 1, 1); model is found suitable for forecasting Lake Volta WL which shows positive trend up to December 2048. The study further predicted that Lake Volta WL will increase from the current average level of 0.2272 m to an average of 9.1366 m for the next 28 years. The ensuing conclusions stressed the need for checks against over-release of WL for hydropower production and measures for sustainable land and water management in the entire basin. This study can potentially enhance our understanding of hydrodynamic processes in Lake Volta and support water resource management.
Keywords : Lake Volta, Kalman Filter, Stochastic Models, Time Series Analysis, Water Levels

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