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
  • [21] Strategic imputation of groundwater data using machine learning: Insights from diverse aquifers in the Chao-Phraya River Basin
    Sharma, Yaggesh Kumar
    Kim, Seokhyeon
    Charmchi, Amir Saman Tayerani
    Kang, Doosun
    Batelaan, Okke
    GROUNDWATER FOR SUSTAINABLE DEVELOPMENT, 2025, 28
  • [22] Applications of Land Surface Model to Assess Impacts of Climate Change of Rainfall Pattern and Surface Runoff in Chao Phraya River Basin of Thailand
    Vechpanich, Ekvichit
    Ruangrassamee, Piyatida
    Putthividhya, Aksara
    Tanaka, Kenji
    WORLD ENVIRONMENTAL AND WATER RESOURCES CONGRESS 2015: FLOODS, DROUGHTS, AND ECOSYSTEMS, 2015, : 952 - 961
  • [23] Hydrological simulation of the Betwa River basin (India) using the SWAT model
    Suryavanshi, Shakti
    Pandey, Ashish
    Chaube, Umesh Chandra
    HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES, 2017, 62 (06): : 960 - 978
  • [24] Streamflow and sediment simulation in the Song River basin using the SWAT model
    Quamar, Shams
    Kumar, Pradeep
    Singh, Harendra Prasad
    FRONTIERS IN WATER, 2025, 7
  • [25] Preliminary Assessment of Groundwater and Surface Water Characteristics in the Upper Chao Phraya River Basin Land Using a Stable Isotope Fingerprinting Technique
    Laonamsai, Jeerapong
    Putthividhya, Aksara
    WORLD ENVIRONMENTAL AND WATER RESOURCES CONGRESS 2016: ENVIRONMENTAL, SUSTAINABILITY, GROUNDWATER, HYDRAULIC FRACTURING, AND WATER DISTRIBUTION SYSTEMS ANALYSIS, 2016, : 367 - 386
  • [26] Simulation of Upper Kuantan River Basin Streamflow Using SWAT Model
    Mohd, Mohd Syazwan Faisal
    Juneng, Liew
    Tangang, Fredolin
    Abd Rahman, Nor Faiza
    Khalid, Khairi
    Haron, Siti Humaira
    2015 UKM FST POSTGRADUATE COLLOQUIUM, 2015, 1678
  • [27] Simulation of Flow in the Capim River (PA) using the SWAT Model
    Garcia Caldas Nunes, Hildo Giuseppe
    Leao de Sousa, Adriano Marlisom
    Souza dos Santos, Joyse Tatiane
    FLORESTA E AMBIENTE, 2019, 26 (01):
  • [28] Inclusion of flood diversion canal operation in the H08 hydrological model with a case study from the Chao Phraya River basin: model development and validation
    Gopalan, Saritha Padiyedath
    Champathong, Adisorn
    Sukhapunnaphan, Thada
    Nakamura, Shinichiro
    Hanasaki, Naota
    HYDROLOGY AND EARTH SYSTEM SCIENCES, 2022, 26 (09) : 2541 - 2560
  • [29] Modeling stream flow and sediment yield using the SWAT model: a case study of Ankara River basin, Turkey
    Duru, Umit
    Arabi, Mazdak
    Wohl, Ellen E.
    PHYSICAL GEOGRAPHY, 2018, 39 (03) : 264 - 289
  • [30] Simulated Precipitation and Reservoir Inflow in the Chao Phraya River Basin by Multi-model Ensemble CMIP3 and CMIP5
    Aribarg, Thannob
    Supratid, Seree
    DATA MINING AND BIG DATA, DMBD 2017, 2017, 10387 : 455 - 463