PREDIKSI DEBIT ALIRAN MASUK KE TELAGA MENJER MENGGUNAKAN PERSAMAAN NERACA AIR DAN PEMODELAN HEC-HMS

The Menjer lake is the main source for Hydroelectric Power Plant of the PLTA Garung. Information about the water balance and the potential of existing water resources in the Menjer Catchment Area (DTA) is needed to obtain an efficient operating pattern, the sustainability of the Garung hydropower plant, and good management of the Menjer Lake. The purpose of this study was to estimate the inflow of three main rivers in the Menjer catchment area using HEC-HMS hydrological and water balance approach. Simulated results of the HEC-HMS model shows that the average of total the inflows of three main rivers to the Menjer lake in 2017, 2018 and 2019 during rainy season are 0.954 m3/s, 0.944 m3/s, and 1.017 m3/s, and during dry season are 0.820 m3/s, 0.783 m3/s, and 0.80 m3/s, respectively. While the prediction results of the discharge with the equation of the water balance shows that the average of total river inflows to the Menjer lake during rainy season is 2017 is 1.628 m3/s, in 2018 it is 1.579 m3/s, and in 2019 it is 3.296 m3/s and during dry season is 1.893 m3/s in 2017, 1.176 m3/s tahun 2018, and 1.893 m3/s in 2019. These results indicate that the results of discharge modeling with HEC-HMS are smaller than those predicted by the water balance equation. The study concluded that HEC-HMS could be used to predict daily inflows. However, further calibration and validation need to be carried out by recommending installing a river flow monitoring station at each river outlet.Keywords: water balance HEC-HMS, inflow prediction

Reservoir Inflow Prediction by Employing Response Surface-Based Models Conjunction with Wavelet and Bootstrap Techniques

Reservoir inflow prediction is a vital subject in the field of hydrology because it determines the flood event. The negative impact of the floods could be minimized greatly if the flood frequency is predicted accurately in advance. In the present study, a novel hybrid model, bootstrap quadratic response surface is developed to test daily streamflow prediction. The developed bootstrap quadratic response surface model is compared with multiple linear regression model, first-order response surface model, quadratic response surface model, wavelet first-order response surface model, wavelet quadratic response surface model, and bootstrap first-order response surface model. Time series data of monsoon season (1 July to 30 September) for the year 2010 of the Chenab river basin are analyzed. The studied models are tested by using performance indices: Nash–Sutcliffe coefficient of efficiency, mean absolute error, persistence index, and root mean square error. Results reveal that the proposed model, i.e., bootstrap quadratic response surface shows good performance and produces optimum results for daily reservoir inflow prediction than other models used in the study.

Monthly reservoir inflow prediction based on artificial neural network over the Saguling catchment area

In line with the objectives of the Impact-Based Forecast, hydrological predictions are used to support water resources management, mitigation of natural disasters, and climate variability impacts. The Artificial Neural Network algorithm was applied to build the prediction model for the Saguling Reservoir monthly inflow by utilizing observation data; the monthly rainfall (P) and inflow (Q) as the predictors in different lag time; t (recent), t-1 (previous month), and t-11 (11 months ago). The predictors are simulated in three variations of hidden layer numbers (2, 6, and 10). The best model under the normal period is model nine with RMSE 31.23 and R 0.88. This model is also the best model under La Nina's condition with RMSE 30.01 and R 0.83. For the El Nino period. We found that model five is the best model with the highest accuracy at the level of generalization in both the training process and predict extreme conditions at the validation stage. Overall, this model has a good performance and high potential usage from a practical point of view and costs but needs further simulation to make it more reliable and robust in any climate conditions.

Analysis of Tunnel Water Inrush Considering the Influence of Surrounding Rock Permeability Coefficient by Excavation Disturbance and Ground Stress

Affected by the coupling of excavation disturbance and ground stress, the heterogeneity of surrounding rock is very common. Presently, treating the permeability coefficient as a fixed value will reduce the prediction accuracy of the water inflow and the external water pressure of the structure, leading to distortion of the prediction results. Aiming at this problem, this paper calculates and analyzes tunnel water inflow when considering the heterogeneity of permeability coefficient of surrounding rock using a theoretical analysis method, and compares with field data, and verifies the rationality of the formula. The research shows that, when the influence of excavation disturbance and ground stress on the permeability coefficient of surrounding rock is ignored, the calculated value of the external water force of the tunnel structure is too small, and the durability and stability of the tunnel are reduced, which is detrimental to the safety of the structure. Considering the heterogeneity of surrounding rock, the calculation error of water inflow can be reduced from 27.3% to 13.2%, which improves the accuracy of water inflow prediction to a certain extent.