Enhancing flow rate prediction of the Chao Phraya River Basin using SWAT-LSTM model coupling

被引:10
|
作者
Phetanan, Kritnipit [1 ]
Hong, Seok Min [1 ]
Yun, Daeun [1 ]
Lee, Jiye [2 ]
Chotpantarat, Srilert [3 ,4 ]
Jeong, Heewon [6 ]
Cho, Kyung Hwa [5 ]
机构
[1] Ulsan Natl Inst Sci & Technol, Dept Civil Urban Earth & Environm Engn, 50 UNIST gil,Eonyang eup, Ulsan 44919, South Korea
[2] Univ Maryland, Dept Environm Sci & Technol, College Pk, MD 20740 USA
[3] Chulalongkorn Univ, Fac Sci, Dept Geol, Bangkok 10330, Thailand
[4] Chulalongkorn Univ, Environm Res Inst, Ctr Excellence Environm Innovat & Management Met E, Bangkok 10330, Thailand
[5] Korea Univ, Sch Civil Environm & Architectural Engn, Seoul 02841, South Korea
[6] Korea Univ, Future & Fus Lab Architectural Civil & Environm En, Seoul 02841, South Korea
关键词
Soil and water assessment tool; Long short-term memory; Tidal river; Flow rate prediction; Chao Phraya River Basin; WATER-QUALITY; SENSITIVITY-ANALYSIS; NEURAL-NETWORK; CATCHMENT; IMPACTS; AREA; EVAPOTRANSPIRATION; CALIBRATION; VALIDATION; SIMULATION;
D O I
10.1016/j.ejrh.2024.101820
中图分类号
TV21 [水资源调查与水利规划];
学科分类号
081501 ;
摘要
Study region: Chao Phraya River Basin-a major river with unique characteristics located in Thailand. Study focus: This study sought to simulate the flow rates in the Chao Phraya River Basin, which is a tidal river that poses challenges to traditional modeling approaches. The soil and water assessment tool (SWAT) is a hydrological model extensively employed for simulating flow rates. However, limitations arise in applying the SWAT model to the Chao Phraya River Basin due to its tidal nature, resulting in an unsatisfactory model performance. To address this, a long short-term memory (LSTM) model, i.e., the SWAT-LSTM model, was introduced to complement the SWAT model. New hydrological insights for the Region: The collaborative coupling of hydrological information derived from the SWAT and LSTM notably enhanced the model performance. This improvement was assessed using the Nash-Sutcliffe efficiency (NSE), demonstrating an increase from 0.13 to 0.72. The incorporation of topographic static data in the coupling model was also investigated to provide the basic characteristics of the basin to the model. The results yielded an NSE exceeding 0.79. The shoreline water level was identified as a crucial input feature for indicating tidal patterns. The findings highlight the effectiveness of coupling the SWAT with LSTM for predicting tidal river flow rates, implying their applicability in similar scenarios across different basins.
引用
收藏
页数:19
相关论文
共 50 条
  • [1] Prediction of water resources in the Chao Phraya River Basin, Thailand
    Wichakul, Supattana
    Tachikawa, Yasuto
    Shiiba, Michiharu
    Yorozu, Kazuaki
    HYDROLOGY IN A CHANGING WORLD: ENVIRONMENTAL AND HUMAN DIMENSIONS, 2014, 363 : 151 - 157
  • [2] A One-dimensional Salinity Measurement Model in the Chao Phraya River with the Chao Phraya Barrage Dam Using a Shooting Method
    Vanishkorn, Buddhaporn
    Pochai, Nopparat
    IAENG International Journal of Computer Science, 2023, 50 (01)
  • [3] Development of a Flow Routing Model Including Inundation Effect for the Extreme Flood in the Chao Phraya River Basin, Thailand 2011
    Wichakul, Supattana
    Tachikawa, Yasuto
    Shiiba, Michiharu
    Yorozu, Kazuaki
    JOURNAL OF DISASTER RESEARCH, 2013, 8 (03) : 415 - 423
  • [4] An Approach to Flood Hazard Mapping for the Chao Phraya River Basin Using Rainfall-Runoff-Inundation Model
    Sriariyawat, Anurak
    Kimmany, Bounhome
    Miyamoto, Mamoru
    Kakinuma, Daiki
    Shakti, P. C.
    Visessri, Supattra
    JOURNAL OF DISASTER RESEARCH, 2022, 17 (06) : 864 - 876
  • [5] Water Budget Closure in the Upper Chao Phraya River Basin, Thailand Using Multisource Data
    Kinouchi, Tsuyoshi
    Abolafia-Rosenzweig, Ronnie
    Ito, Megumi
    Abhishek
    REMOTE SENSING, 2022, 14 (01)
  • [6] Hydrological assessment using stable isotope fingerprinting technique in the Upper Chao Phraya river basin
    Putthividhya, A.
    Laonamsai, J.
    Lowland Technology International, 2017, 19 (01) : 27 - 40
  • [7] Impact of large-scale reservoir operation on flow regime in the Chao Phraya River basin, Thailand
    Tebakari, Taichi
    Yoshitani, Junichi
    Suvanpimol, Pongthakorn
    HYDROLOGICAL PROCESSES, 2012, 26 (16) : 2411 - 2420
  • [8] Modeling of a River Basin Using SWAT Model
    Venkatesh, B.
    Chandramohan, T.
    Purandara, B. K.
    Jose, Mathew K.
    Nayak, P. C.
    HYDROLOGIC MODELING, 2018, 81 : 707 - 714
  • [9] Water Level Prediction Model Using Back Propagation Neural Network Case study : The Lower of Chao Phraya Basin
    Truatmoraka, Panjaporn
    Waraporn, Narongrit
    Suphachotiwatana, Dhanasite
    2016 4TH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL AND BUSINESS INTELLIGENCE (ISCBI), 2016, : 200 - 205
  • [10] Distributed tank model and GAME reanalysis data applied to the simulation of runoff within the Chao Phraya River Basin, Thailanda
    Huang, Wenfeng
    Nakane, Kazurou
    Matsuura, Reiko
    Matsuura, Tomonori
    HYDROLOGICAL PROCESSES, 2007, 21 (15) : 2049 - 2060