Zobrazeno 1 - 10
of 100
pro vyhledávání: '"Shiang Jen Wu"'
Publikováno v:
Hydrology Research, Vol 54, Iss 12, Pp 1522-1556 (2023)
This study aims to develop a probabilistic model to quantify the reliability of estimating riverbed elevations due to the uncertainties in the runoff and sediment-related factors (named PM_MBEE_1D); the above uncertainties are quantified by reproduci
Externí odkaz:
https://doaj.org/article/cc085f69ffaa4209bfed082857d747ff
Autor:
Shiang-Jen Wu
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-26 (2023)
Abstract This study aims to develop a smart model for carrying out two-dimensional (2D) inundation simulation by estimating the gridded inundation depths via the ANN-derived models (ANN_GA-SA_MTF), named SM_EID_2D model. Within the SM_EID_2D model, t
Externí odkaz:
https://doaj.org/article/7638037717554fba85f32a2ba8dee8b1
Autor:
Shiang-Jen Wu, Han-Yuan Yang
Publikováno v:
Agriculture, Vol 14, Iss 7, p 1107 (2024)
This study aims to model the uncertainty and reliability quantification of estimating the planning irrigation water demands in the multi-canal irrigation zone, named the RA_IWD_Canal model. The proposed RA_IWD_Canal could estimate the zone-based and
Externí odkaz:
https://doaj.org/article/b9d7fa2928284c6a9f86a6e96341cf96
Publikováno v:
Agricultural Water Management, Vol 290, Iss , Pp 108588- (2023)
This study proposes an optimal analysis model for carrying out the canal-based irrigation water allocation (OPA_IWA_Canal) for a schedule-based irrigation zone under consideration of the variations in the runoff-related factor and irrigation-related
Externí odkaz:
https://doaj.org/article/d0ed1944dd20418083397082923f9328
Publikováno v:
Journal of Hydroinformatics, Vol 25, Iss 3, Pp 706-737 (2023)
This study aims to model a probabilistic-based reliability assessment of the gridded rainfall thresholds for shallow landslide occurrence (RA_GRTE_LS) to quantify the effect of the uncertainty of rainfall in time and space on the rainfall thresholds
Externí odkaz:
https://doaj.org/article/8a1af61b53f84ee5b34285900615003c
Publikováno v:
Hydrology Research, Vol 52, Iss 6, Pp 1490-1525 (2021)
This study proposes a stochastic artificial neural network (named ANN_GA-SA_MTF), in which the parameters of the multiple transfer functions considered are calibrated by the modified genetic algorithm (GA-SA), to effectively provide the real-time for
Externí odkaz:
https://doaj.org/article/3d2a444aeb3742efaad506edb9c5baf7
Publikováno v:
Hydrology Research, Vol 52, Iss 4, Pp 876-904 (2021)
This study aims to develop a stochastic method (SM_GSTR) for generating short-time (i.e., hourly) rainstorm events at all grids (named gridded rainstorm events) in a region. The proposed SM_GSTR model is developed by the non-normal correlated multiva
Externí odkaz:
https://doaj.org/article/fd365f3c3a5d481282c195c06ee59471
Publikováno v:
Water; Volume 14; Issue 20; Pages: 3346
Accurate real-time forecasts of inundation depth and area during typhoon flooding is crucial to disaster emergency response. The development of an inundation forecasting model has been recognized as essential to manage disaster risk. In the past, mos
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::13ef5466d6280492a6f6b82e7c80fce2
Publikováno v:
Sustainability; Volume 14; Issue 19; Pages: 12747
This study aims to model a probabilistic-based reliability analysis, named the RA_IWS_Canal model, for calculating the probability of the irrigation water supply exceeding the water demand (i.e., reliability) within a multi-canal irrigation zone due
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f812b41c040fafea2e7fb1b237ccca0a
Publikováno v:
Hydrology Research, Vol 52, Iss 6, Pp 1490-1525 (2021)
This study proposes a stochastic artificial neural network (named ANN_GA-SA_MTF), in which the parameters of the multiple transfer functions considered are calibrated by the modified genetic algorithm (GA-SA), to effectively provide the real-time for
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::86b9088e3b9b94153a03e4b456794852
https://doi.org/10.2166/nh.2021.030
https://doi.org/10.2166/nh.2021.030