Lake Water Level Prediction Model Based on Artificial Intelligence and Classical Techniques – An Empirical Study on Lake Volta Basin, Ghana

Year 2021, Volume: 3 Issue: 2, 134 - 150, 15.03.2021

Michael Stanley Peprah Edwin Kojo Larbı

Abstract

Several studies in the past and recent years have suggested numerous mathematical models for Lake Water Level (LWL) modelling to a good precision. This study considered an empirical evaluation of Artificial Intelligence and Classical Techniques such as Wavelet Transform (WT), Bayesian Regularization Backpropagation Artificial Neural Network (BRBPANN), Levenberg-Marquardt Backpropagation Artificial Neural Network (LMBPANN), Scaled Conjugate-Gradient Backpropagation Artificial Neural Network (SCGBPANN), Radial Basis Functions Artificial Neural Network (RBFANN), Generalized Regression Artificial Neural Network (GRANN), Multiple Linear Regression (MLR), and Autoregressive Integrated Moving Average (ARIMA) for LWL modelling. The motive is to apply and assess for the first time in our study area, the working efficiency of the aforementioned techniques. Satellite altimetry data provided by the United States Department of Agriculture was used in this study. The input and output variables used in this study were the decomposed LWL by the WT. Each model technique was assessed based on statistical measures such as Arithmetic Mean Error (AME), Arithmetic Mean Square Error (AMSE), arithmetic mean absolute percentage deviation (AMAPD), minimum error value (rmin), maximum error value (rmax), and arithmetic standard deviation (ASD). The statistical analysis of the results revealed that, all the hybridized models successfully estimate the LWL heights at a good precision for the study area. However, Discrete Wavelet Transform (DWT)-MLR model outperforms DWT-BRBPANN, DWT-LMBPANN, DWT-SCGBPANN, DWT-RBFANN, DWT-GRANN, and DWT-ARIMA techniques in estimating the LWL heights for the study area. In terms of AME, AMSE and ASD, DWT-MLR achieved 0.1988 m, 0.0024 m, and 0.0017 m respectively. The main conclusion drawn from this study is that, the method of using novel ensemble models is promising and can be adopted for LWL modelling in the study area. This study seeks to contribute to the existing knowledge on understanding the hydrodynamic processes in Lake Volta Basin and support water resource management.

Keywords

Artificial Intelligence, Lake Volta Basin, Stochastic Models, Time Series Analysis, Water Level Modelling

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