Abstract
The aim of this study is to accurately forecast the changes in water level of a reservoir located in Malaysia with two different scenarios; Scenario 1 (SC1) includes rainfall and water level as input and Scenario 2 (SC2) includes rainfall, water level, and sent out. Different time horizons (one day ahead to seven days) will be investigated to check the accuracy of the proposed models. In this study, four supervised machine learning algorithms for both scenarios were proposed such as Boosted Decision Tree Regression (BDTR), Decision Forest Regression (DFR), Bayesian Linear Regression (BLR) and Neural Network Regression (NNR). Eighty percent of the total data were used for training the datasets while 20% for the dataset used for testing. The models’ performance is evaluated using five statistical indexes; the Correlation Coefficient (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Absolute Error (RAE), and Relative Squared Error (RSE). The findings showed that among the four proposed models, the BLR model outperformed other models with R2 0.998952 (1-day ahead) for SC1 and BDTR for SC2 with R2 0.99992 (1-day ahead). With regards to the uncertainty analysis, 95PPU and d-factors were adopted to measure the uncertainties of the best models (BLR and BDTR). The results showed the value of 95PPU for both models in both scenarios (SC1 and SC2) fall into the range between 80% to 100%. As for the d-factor, all values in SC1 and SC2 fall below one.
Highlights
IntroductionA reservoir is a physical structure (artificial and natural) used as water storage for water storage preservation, control, and regulation of water supply [1]
A reservoir is a physical structure used as water storage for water storage preservation, control, and regulation of water supply [1]
This study investigated different Machine Learning techniques such as Boosted Decision Tree Regression (BDTR), Decision Forest Regression (DFR), Neural Network Regression (NNR), and Bayesian Linear Regression (BLR) to identify the most accurate model for water level prediction based on daily measured historical data from 1985 to 2019
Summary
A reservoir is a physical structure (artificial and natural) used as water storage for water storage preservation, control, and regulation of water supply [1]. One major problem in current dam impact studies is the lack of a reliable model for simulating the implications of water level on the reservoir operation. Due to the complexity and lack of access to the data, overestimating parameters and high missing values in data cause poor performances and undermining of numerical models With regards to these issues, the use of ML approaches has been introduced, on the condition of improved results in modelling nonlinear processes and forecasting than traditional models, such as moving average methods [13,14,15]. SIcnenMaaricohi2n(eSLCe2a)rinninEgq(uMatLio),no(n2e).of the main tasks is to choose input parameters that will influence output parameters because it will require attention and good understanding of the underlying physical process based on causal variableWs aLnt+dnst=atiRstti+caWl aLntalysis of possible inputs and outputs [10(1].) Reservoir water level is basically affected by the hydrological phenomena such as rainfall, heat and temperature, evaporation, dischargeW, aLnt+dn =alsWo,Ltth+e Rdt e+ciSsiton of send out water to the riv(e2r) downstream. SC1 will use the data from 1985 to 2019 and SC2 from 2010 to 2019
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