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Springs. China J Hydrol 625:130079. Google Scholar Gan M, Pan S, Chen Y, Cheng C, Pan H, Zhu X (2021) Application of the machine learning LightGBM model to the prediction of the water levels of the lower Columbia River. J Marine Sci Eng 9(5):496. Google Scholar Gao X, Luo H, Wang Q, Zhao F, Ye L, Zhang Y (2019) A human activity recognition algorithm based on stacking denoising autoencoder and lightGBM. Sensors 19:947. Google Scholar Geurts P, Ernst D, Wehenkel L (2006) Extremely randomized trees. Mach Learn 63(1):3–42. Google Scholar Guo WD, Chen WB, Yeh SH, Chang CH, Chen H (2021) Prediction of river stage using multistep-ahead machine learning techniques for a tidal river of Taiwan. Water 13(7):920. Google Scholar Guo WD, Chen WB, Chang CH (2023a) Error-correction-based data-driven models for multiple-hour-ahead river stage predictions: a case study of the upstream region of the Cho-Shui River. Taiwan J Hydrol: Reg Stud 47:101378. Google Scholar Guo Y, Peng Y, Hao R, Tang X (2023b) Capturing spatial–temporal correlations with attention based graph convolutional network for network traffic prediction. J Netw Comput Appl 220:103746. Google Scholar Hameed MM, Alomar MK, Khaleel F, Al-Ansari N (2021) An extra tree regression model for discharge coefficient prediction: novel, practical applications in the hydraulic sector and future research directions. Math Probl Eng 2021:7001710. Google Scholar Hanifi S, Cammarono A, Zare-Behtash H (2024) Advanced hyperparameter optimization of deep learning models for wind power prediction. Renewable Energy 221:119700. Google Scholar Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural
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P (2024) Investigating the performance of the informer model for streamflow forecasting. Water 16(20):2882. Google Scholar Tiu ESK, Huang YF, Ng JL, AlDahoul N, Ahmed AN, Elshafie A (2022) An evaluation of various data preprocessing techniques with machine learning models for water level prediction. Nat Hazards 110(1):121–153. Google Scholar Vafaeipour M, Rahbari O, Rosen MA, Fazelpour F, Ansarirad P (2014) Application of sliding window technique for prediction of wind velocity time series. Int J Energy Environ Eng 5(105):1–7. Google Scholar van Zyl C, Ye X, Naidoo R (2024) Harnessing eXplainable artificial intelligence for feature selection in time series energy forecasting: a comparative analysis of Grad-CAM and SHAP. Appl Energy 353:122079. Google Scholar Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Proc Syst 30 Z, Batki B, Rátki L, Szalánczi S, Fehérváry I, Kozák P, Kiss T (2023) Water level prediction using long short-term memory neural network model for a lowland river: a case study on the Tisza River. Central Europe Environ Sci Europe 35(1):92. Google Scholar Wei X, Wang G, Schmalz B, Hagan DFT, Duan Z (2023) Evaluation of transformer model and self-attention mechanism in the Yangtze River basin runoff prediction. J Hydrol: Reg Stud 47:101438. Google Scholar Wu J, Chen XY, Zhang H, Xiong LD, Lei H, Deng SH (2019) Hyperparameter optimization for machine learning models based on Bayesian optimization. J Electron Sci Technol 17(1):26–40 Google Scholar Wu Q, Zheng H, GuoJ. River Media Jukebox 8.0 review: J. River Media - CNET
Stop and GoThe last strategy is check-raising the river when a nit does a “stop and go.” This is when they check the turn for pot control, then bet the river with a hand like top pair.For example, a nit raises on the button with A♣Q♠, and you call with J♥T♥ in the big blind. The flop is Q♦T♠4♣, and you check-call the nit’s CBet. The turn is 8♥, and both check. The river brings K♥, and the nit bets.Check-raise here because the nit likely has a top pair. By check-raising, you take advantage of their desire for pot control with good but not great hands. Raising on a scary river card can force them to fold the best hand.Before executing this strategy, use your poker HUD to check the nit’s WTSD% (Went to Showdown %). Most nits have a low WTSD%, but if it’s higher, they may be more inclined to call, so adjust accordingly.. Download J. River Media Center [NL] ダウンロードJ. River Media Center [JA] T l charger J. River Media Center [FR] J. River Media Center indir [TR] تنزيل J. River Media Center [AR] Ladda ner J. River Media Center [SV] 下载J. River J. River Media Center, free download. J. River Media Center : J. River Media Center: A Comprehensive Media Management Software J.J. River Media Jukebox 8.0 review: J. River Media Jukebox 8.0
29 April 2021).Tsakiris, G.D. Flood risk assessment: Concepts, modelling, applications. Nat. Hazards Earth Syst. Sci. 2014, 14, 1361–1369. [Google Scholar] [CrossRef] [Green Version]Hakim, F.A.; Akhtar, A.; Sultan, B.; Shabir. A One Dimensional Steady Flow Analysis Using HEC-RAS: A Case of River Jhelum, Jammu, and Kashmir. Eur. Sci. J. 2016, 12, 340–350. [Google Scholar] [CrossRef]Kamaledin, E.B.; Basim, H.K.; Ghassan, K.K. Inundation Map Development by Using Hec-Ras Hydraulic Simulation Modeling From Rosaries to Khartoum Cities. Indian J. Res. 2014, 3, 58–62. [Google Scholar]CEIWR-HEC; HEC-HMS. Hydrological Modeling System: Application Guide; US Army Corps of Engineers Hydrologic Engineering Center: Davis, CA, USA, 2017; pp. 3.1–3.19. [Google Scholar]Lahsaini, M.; Tabyaoui, H. Mono Dimensional Hydraulic Modeling by HEC RAS, Application on L’oued Aggay (City of Sefrou). Eur. Sci. J. Ed. 2018, 14, 110–121. [Google Scholar] [CrossRef]Mohammed, A.B.; Pascal, M.; François, A. Uncertainty Analysis of a 1D River Hydraulic Model with Adaptive Calibration. J. Water 2020, 12, 1–24. [Google Scholar]Chow, V.T. Open Channel Hydraulics; McGraw-Hill: New York, NY, USA, 1959. [Google Scholar]Bennani, O.; Tramblay, Y.; El Mehdi, S.M.; Gascoin, S.; Leone, F. Flood Hazard Mapping Using Two Digital Elevation Models: Application in a Semi-Arid Environment of Morocco. Eur. Sci. J. 2019, 15, 338–359. [Google Scholar]Merwade, V. Creating SCS Curve Number Grid using HEC-GeoHMS; School of Civil Engineering, Purdue University: West Lafayette, IN, USA, 2012; pp. 101–114. [Google Scholar]US Army Corps of Engineers, Hydrologic Engineering Center. HEC-RAS, River Analysis System-Hydraulic User’s Manual; US Army Corps of Engineers, Hydrologic Engineering Center: St. Davis, CA, USA, 2016; pp. 1–538. [GoogleJ. River Media Jukebox - Download
Open J. Mod. Hydrol. 2020, 10, 45–64. [Google Scholar]Radmanesh, F.; Hemat, J.P.; Behnia, A.; Khond, A.; Mohamad, B.A. Calibration and assessment of HEC-1 model in Roodzard watershed. In Proceedings of the 17th International Conference of River Engineering, University of Shahid Chamran, Ahva, Iran, 26–28 February 2006; pp. 85–99. [Google Scholar]Abdelkarim, A. Hydrological and Hydraulic Modeling of Floods using Watershed Modeling System (WMS), 1st ed.; Al-Akiban Publishing House: Riyadh, Saudi Arabia, 2020; 484p. [Google Scholar]Quan, V.D.; Kittiwet, K. An Assessment of Potential Climate Change Impacts on Flood Risk in Central Vietnam. Eur. Sci. J. 2015, 1, 667–681. [Google Scholar]Ministry of Land, Infrastructure, Transport and Tourism (MLIT). Manual for Economic Evaluation of Flood Control Investment (Draft); River Bureau, Ministry of Land, Infrastructure, Transport and Tourism: Tokyo, Japan, 2005; pp. 1–65. Available online: (accessed on 23 June 2022).Hamed, Y. The hydrogeochemical characterization of groundwater in Gafsa-Sidi Boubaker region (Southwestern Tunisia). Arab. J. Geosci. 2013, 6, 697–710. [Google Scholar] [CrossRef] Figure 1. Geographical location of the study area. Figure 1. Geographical location of the study area. Figure 2. Flowchart for HEC-HMS methodology. Figure 2. Flowchart for HEC-HMS methodology. Figure 3. Geometric data of Khazir River. Figure 3. Geometric data of Khazir River. Figure 4. Flowchart for HEC-RAS methodology. Figure 4. Flowchart for HEC-RAS methodology. Figure 5. Curve values (intensity, duration, frequency) for the study station. Figure 5. Curve values (intensity, duration, frequency) for the study station. Figure 6. Curve values (intensity, duration, and frequency) for the study station. Figure 6. Curve values (intensity, duration,J. River Media Center - Download
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Springs. China J Hydrol 625:130079. Google Scholar Gan M, Pan S, Chen Y, Cheng C, Pan H, Zhu X (2021) Application of the machine learning LightGBM model to the prediction of the water levels of the lower Columbia River. J Marine Sci Eng 9(5):496. Google Scholar Gao X, Luo H, Wang Q, Zhao F, Ye L, Zhang Y (2019) A human activity recognition algorithm based on stacking denoising autoencoder and lightGBM. Sensors 19:947. Google Scholar Geurts P, Ernst D, Wehenkel L (2006) Extremely randomized trees. Mach Learn 63(1):3–42. Google Scholar Guo WD, Chen WB, Yeh SH, Chang CH, Chen H (2021) Prediction of river stage using multistep-ahead machine learning techniques for a tidal river of Taiwan. Water 13(7):920. Google Scholar Guo WD, Chen WB, Chang CH (2023a) Error-correction-based data-driven models for multiple-hour-ahead river stage predictions: a case study of the upstream region of the Cho-Shui River. Taiwan J Hydrol: Reg Stud 47:101378. Google Scholar Guo Y, Peng Y, Hao R, Tang X (2023b) Capturing spatial–temporal correlations with attention based graph convolutional network for network traffic prediction. J Netw Comput Appl 220:103746. Google Scholar Hameed MM, Alomar MK, Khaleel F, Al-Ansari N (2021) An extra tree regression model for discharge coefficient prediction: novel, practical applications in the hydraulic sector and future research directions. Math Probl Eng 2021:7001710. Google Scholar Hanifi S, Cammarono A, Zare-Behtash H (2024) Advanced hyperparameter optimization of deep learning models for wind power prediction. Renewable Energy 221:119700. Google Scholar Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural
2025-04-11P (2024) Investigating the performance of the informer model for streamflow forecasting. Water 16(20):2882. Google Scholar Tiu ESK, Huang YF, Ng JL, AlDahoul N, Ahmed AN, Elshafie A (2022) An evaluation of various data preprocessing techniques with machine learning models for water level prediction. Nat Hazards 110(1):121–153. Google Scholar Vafaeipour M, Rahbari O, Rosen MA, Fazelpour F, Ansarirad P (2014) Application of sliding window technique for prediction of wind velocity time series. Int J Energy Environ Eng 5(105):1–7. Google Scholar van Zyl C, Ye X, Naidoo R (2024) Harnessing eXplainable artificial intelligence for feature selection in time series energy forecasting: a comparative analysis of Grad-CAM and SHAP. Appl Energy 353:122079. Google Scholar Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Proc Syst 30 Z, Batki B, Rátki L, Szalánczi S, Fehérváry I, Kozák P, Kiss T (2023) Water level prediction using long short-term memory neural network model for a lowland river: a case study on the Tisza River. Central Europe Environ Sci Europe 35(1):92. Google Scholar Wei X, Wang G, Schmalz B, Hagan DFT, Duan Z (2023) Evaluation of transformer model and self-attention mechanism in the Yangtze River basin runoff prediction. J Hydrol: Reg Stud 47:101438. Google Scholar Wu J, Chen XY, Zhang H, Xiong LD, Lei H, Deng SH (2019) Hyperparameter optimization for machine learning models based on Bayesian optimization. J Electron Sci Technol 17(1):26–40 Google Scholar Wu Q, Zheng H, Guo
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2025-04-02Open J. Mod. Hydrol. 2020, 10, 45–64. [Google Scholar]Radmanesh, F.; Hemat, J.P.; Behnia, A.; Khond, A.; Mohamad, B.A. Calibration and assessment of HEC-1 model in Roodzard watershed. In Proceedings of the 17th International Conference of River Engineering, University of Shahid Chamran, Ahva, Iran, 26–28 February 2006; pp. 85–99. [Google Scholar]Abdelkarim, A. Hydrological and Hydraulic Modeling of Floods using Watershed Modeling System (WMS), 1st ed.; Al-Akiban Publishing House: Riyadh, Saudi Arabia, 2020; 484p. [Google Scholar]Quan, V.D.; Kittiwet, K. An Assessment of Potential Climate Change Impacts on Flood Risk in Central Vietnam. Eur. Sci. J. 2015, 1, 667–681. [Google Scholar]Ministry of Land, Infrastructure, Transport and Tourism (MLIT). Manual for Economic Evaluation of Flood Control Investment (Draft); River Bureau, Ministry of Land, Infrastructure, Transport and Tourism: Tokyo, Japan, 2005; pp. 1–65. Available online: (accessed on 23 June 2022).Hamed, Y. The hydrogeochemical characterization of groundwater in Gafsa-Sidi Boubaker region (Southwestern Tunisia). Arab. J. Geosci. 2013, 6, 697–710. [Google Scholar] [CrossRef] Figure 1. Geographical location of the study area. Figure 1. Geographical location of the study area. Figure 2. Flowchart for HEC-HMS methodology. Figure 2. Flowchart for HEC-HMS methodology. Figure 3. Geometric data of Khazir River. Figure 3. Geometric data of Khazir River. Figure 4. Flowchart for HEC-RAS methodology. Figure 4. Flowchart for HEC-RAS methodology. Figure 5. Curve values (intensity, duration, frequency) for the study station. Figure 5. Curve values (intensity, duration, frequency) for the study station. Figure 6. Curve values (intensity, duration, and frequency) for the study station. Figure 6. Curve values (intensity, duration,
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