Improving daily streamflow simulations for data-scarce watersheds using the coupled SWAT-LSTM approach

“Improving daily streamflow simulations for data-scarce watersheds using the coupled SWAT-LSTM approach” (2023) has been cited 128 times according to Google Scholar. CitationMap has resolved 83 citing papers from institutions across 5 countries.

Journal of Hydrology 622, 129734, 20232023View paper

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Where this paper is cited

China · 3Germany · 2South Korea · 1United States · 1Iran · 1

Top citing institutions

  • Technical University of Munich (2)
  • Ludwig-Maximilians-University (2)
  • Beijing Normal University (2)
  • Gwangju Institute of Science and Technology (1)
  • Korea University (1)
  • Konkuk University (1)
  • Nanjing Hydraulic Research Institute (1)
  • Guangdong Research Institute of Water Resources and Hydropower (1)
  • Xi'an Jiaotong University (1)
  • Xinjiang Agricultural University (1)
  • Mississippi State University (1)
  • Shahid Bahonar University of Kerman (1)

Papers citing this work (83 resolved)

  1. A review of hybrid deep learning applications for streamflow forecasting

  2. A state-of-the-art review of long short-term memory models with applications in hydrology and water resources

  3. A performance comparison study on climate prediction in Weifang City using different deep learning models

  4. Unraveling the interactions between flooding dynamics and agricultural productivity in a changing climate

  5. A coupled model to improve river water quality prediction towards addressing non-stationarity and data limitation

  6. Comparison and integration of physical and interpretable AI-driven models for rainfall-runoff simulation

  7. Assessing the response of non-point source nitrogen pollution to land use change based on SWAT model

  8. A comparative survey between cascade correlation neural network (CCNN) and feedforward neural network (FFNN) machine learning models for forecasting …

  9. Integrating machine learning with process-based glacio-hydrological model for improving the performance of runoff simulation in cold regions

  10. Assessing climate change and human impacts on runoff and hydrological droughts in the Yellow River Basin using a machine learning-enhanced hydrological …

  11. Coupling SWAT+ with LSTM for enhanced and interpretable streamflow estimation in arid and semi-arid watersheds, a case study of the Tagus Headwaters …

  12. An explainable ensemble deep learning model for long-term streamflow forecasting under multiple uncertainties

  13. Coupling the remote sensing data-enhanced SWAT model with the bidirectional long short-term memory model to improve daily streamflow simulations

  14. Interpretable machine learning guided by physical mechanisms reveals drivers of runoff under dynamic land use changes

  15. Enhancing flow rate prediction of the Chao Phraya River Basin using SWAT–LSTM model coupling

  16. Explainable deep learning identifies patterns and drivers of freshwater harmful algal blooms

  17. Deep learning model based on coupled SWAT and interpretable methods for water quality prediction under the influence of non-point source pollution

  18. Enhancing daily streamflow simulation using the coupled SWAT-BiLSTM approach for climate change impact assessment in Hai-River Basin

  19. Exploring the potential of deep learning for streamflow forecasting: A comparative study with hydrological models for seasonal and perennial rivers

  20. Physics-aware machine learning revolutionizes scientific paradigm for process-based modeling in hydrology

    · Qingsong Xu, Yilei Shi, Jonathan Bamber, Ye Tuo +2 more

  21. Enhancing daily runoff prediction: A hybrid model combining GR6J-CemaNeige with wavelet-based gradient boosting technique

  22. Coupling SWAT and LSTM for Improving Daily Streamflow Simulation in a Humid and Semi-humid River Basin: Z. Mei et al.

  23. Climate change projections and impacts on future temperature, precipitation, and stream flow in the Vea Catchment, Ghana

  24. Physics-aware machine learning revolutionizes scientific paradigm for machine learning and process-based hydrology

    · Qingsong Xu, Yilei Shi, Jonathan Bamber, Ye Tuo +2 more

  25. Dynamic patterns and potential drivers of river water quality in a coastal city: Insights from a machine-learning-based framework and water management

  26. Improving estimation capacity of a hybrid model of LSTM and SWAT by reducing parameter uncertainty

  27. Enhancing streamflow prediction physically consistently using process-Based modeling and domain knowledge: A review

  28. Improving the accuracy of flood forecasting for Northeast China by the correction of global forecast rainfall based on deep learning

  29. Enhancing the streamflow simulation of a process-based hydrological model using machine learning and multi-source data

  30. Data-driven and numerical simulation coupling to quantify the impact of ecological water replenishment on surface water-groundwater interactions

  31. Runoff simulation of the Kaidu River Basin based on the GR4J-6 and GR4J-6-LSTM models

  32. A hybrid approach to improvement of watershed water quality modeling by coupling process–based and deep learning models

    · Dae Seong Jeong, Heewon Jeong, Jin Hwi Kim, Joon Ha Kim +1 more

  33. Incorporating multi-timescale data into a single long short-term memory network to enhance reservoir-regulated streamflow simulation

  34. Predicting Ili River streamflow change and identifying the major drivers with a novel hybrid model

  35. Enhancing physically-based hydrological modeling with an ensemble of machine-learning reservoir operation modules under heavy human regulation using easily …

  36. Does grouping watersheds by hydrographic regions offer any advantages in fine-tuning transfer learning model for temporal and spatial streamflow predictions?

  37. Evaluation of best management practices for mitigating harmful algal blooms risk in an agricultural lake basin using a watershed model integrated with Bayesian …

  38. Comparative study of daily streamflow prediction based on coupling SWAT+ with interpretable machine learning algorithms

  39. Heterogeneous impacts of climate change on streamflow in typical watersheds of three mountain systems in Xinjiang, Northwest China

  40. Improving land surface model accuracy in soil moisture simulations using parametric schemes and machine learning

  41. Utilizing hybrid deep learning models for streamflow prediction

  42. Comparison of process-based hydrological modeling and deep learning approaches for streamflow simulation

  43. Effects of land use/cover change on propagation dynamics from meteorological to soil moisture drought considering nonstationarity

  44. Coupling SWAT and transformer models for enhanced monthly streamflow prediction

  45. Enhancing hydrological predictions: optimised decision tree modelling for improved monthly inflow forecasting

  46. Deciphering nonlinear hydrological process by a coupled deep learning and physical based model in Southern Tibetan Plateau

    · Zhanliang Zhu, Xiongpeng Tang, Jianyun Zhang, Yehai Tang +8 more

  47. Runoff simulation in data-scarce alpine regions: Comparative analysis based on LSTM and physically based models

  48. Digital twin-enabled intelligent irrigation-drainage system for precision water-salt management in saline agroecosystems

  49. Bridging the gap: An interpretable coupled model (SWAT-ELM-SHAP) for blue-green water simulation in data-scarce basins

  50. Exploring alternate coupling inputs of a data-driven model for optimum daily streamflow prediction in calibrated SWAT-BiLSTM rainfall-runoff modeling

  51. Identification of key factors influencing monthly runoff simulation through the integration of deep learning and physically-based hydrological models

  52. Application of LSTM coupled models in runoff simulation and prediction: a review

  53. An interpretable coupled model (SWAT-STFT) for multispatial-multistep evapotranspiration prediction in the river basin

  54. Bridging data-driven and process-based approaches for hydrological modeling in the tropics: insights from the Kelani River Basin, Sri Lanka

  55. Water Quality Prediction Method Coupling Mechanism Model and Machine Learning for Water Diversion Projects with a Lack of Data: X. Yang et al.

  56. Land Use–Future Climate Coupling Mechanism Analysis of Regional Agricultural Drought Spatiotemporal Patterns

  57. Enhancing hydrological extremes forecasting capabilities in data‐scarce regions through transfer learning with data augmentation

    · Yehai Tang, Xiongpeng Tang, Zhanliang Zhu, Chao Gao +3 more

  58. Optimizing flood resilience in China's mountainous areas: Design flood estimation using advanced machine learning techniques

  59. The role of Gala Lake Wetland Site on flood control

  60. Super-Resolution enhanced deep learning for efficient and accurate urban flood simulation at the street scale

  61. Coupled SWAT, stationary wavelet transform, and interpretable machine learning to improve watershed streamflow simulation

  62. Advancing Machine Learning-Based Streamflow Prediction Through Event Greedy Selection, Asymmetric Loss Function, and Rainfall Forecasting Uncertainty

  63. Climate change impacts on in-stream carbon cycling dynamics in the Miho River Watershed, South Korea

  64. A Coupled SWAT-LSTM Approach for Climate-Driven Runoff Dynamics in a Snow-and Ice-Fed Arid Basin

  65. A review of machine learning applications in the prediction of selected groundwater quality parameters: Key lessons, knowledge gaps, and future directions

  66. Enhancing daily streamflow prediction: A comparative analysis of univariate LSTM and N-BEATS models with coupled SWAT-LSTM and SWAT-N-BEATS models …

  67. A stochastic deep-learning-based approach for improved streamflow simulation

  68. A Climate-Informed Scenario Generation Method for Stochastic Planning of Hybrid Hydro–Wind–Solar Power Systems in Data-Scarce Regions

  69. Applicability of ERA5 reanalysis precipitation data in runoff modeling in China's Ili River Basin

    · Zilong Li, Zhenxia Mu, Rui Gao

  70. Explainable Artificial Intelligence in Hydrology: A Review

    · Mohammad Zounemat‐Kermani, M. Kheimi

  71. Enhanced streamflow prediction using SWAT's influential parameters: a comparative analysis of PCA-MLR and XGBoost models

  72. Streamflow Simulation Using a Hybrid Approach Combining HEC-HMS and LSTM Model in the Tlawng River Basin of Mizoram, India: S. Debbarma et al.

  73. A coupled modeling framework to screen reclaimed water supplement schemes in an urban watershed

  74. A spatial explainable deep learning framework for prediction classification of hydrological drought in ungauged basin

  75. Evaluating multi-source precipitation data for streamflow simulation using the SWAT model in the Alpine Manas River Basin, Northwest China

  76. Comparative analysis of GAMLSS modeling approaches for nonstationary runoff dynamics in the Yellow River Basin of China

  77. Spatiotemporal variations of surface and groundwater interactions under climate and land use land cover change scenarios

  78. Enhancing runoff simulation in data-scarce mountainous regions: a coupled SWAT and transferable transformer approach

  79. Realistic daily discharge modelling in data-deficient regions using DL-assisted, parametrically-optimized hydrological model

  80. A hybrid SWAT-LSTM model for streamflow simulation with SHAP-based interpretability: Application in the Wei River Basin, China

  81. Predicting effects of non-point source pollution emission control schemes based on VMD-BiLSTM and MIKE21

  82. The dynamic urban river water quality prediction based on hybrid model

  83. A physically guided and interpretable SWAT-BiLSTM framework with Bayesian optimization for bias correction in daily streamflow forecasting

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