Results 41 to 50 of about 76,535 (212)

Perbandingan Penggunaan Data Hujan Lapangan dan Data Hujan Satelit untuk Analisis Hujan-aliran Menggunakan Model Ihacres [PDF]

, 2015
This study conducted an analysis of satellite rainfall data utilization as an alternative for hydrological modeling. Reliability of satellite rainfall data for hydrological modeling iscompared to the use of ground rainfall data.
Fadhli, R. A. (Reza), Sujatmoko, B. \u27. (Bambang), Sutikno, S. \u27. (Sigit)   +2 more
core  

TOPMODEL CAPABILITY FOR RAINFALL-RUNOFF MODELING OF THE AMMAMEH WATERSHED AT DIFFERENT TIME SCALES USING DIFFERENT TERRAIN ALGORITHMS

Journal of Urban and Environmental Engineering, 2011
In this study, the rainfall-runoff response of the Ammameh watershed located in Tehran, Iran, was studied using TOPMODEL which is a semi-distributed and geomorphologic model that simulates runoff at the watershed’s outlet on the basis of saturation ...
Vahid Nourani, Arash Roughani, Mekonnen Gebremichael   +2 more
doaj   +2 more sources

Optimization Rainfall-runoff Modeling For Ciujung River Using Back Propagation Method [PDF]

, 2018
The rainfall-runoff model is required to ascertain the relationship between rainfall and runoff. Hydrologists are often confronted with problems of prediction and estimation of runoff using the rainfall date.
Laoli, A. G. (Alnis), Sebayang, I. S. (Ika), Suroso, A. (Agus)   +2 more
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Applying Multiscale Entropy to the Complexity Analysis of Rainfall-Runoff Relationships

Entropy, 2012
This paper presents a novel framework for the complexity analysis of rainfall, runoff, and runoff coefficient (RC) time series using multiscale entropy (MSE).
Chien-Ming Chou
doaj   +1 more source

Ensemble evaluation of hydrological model hypotheses [PDF]

, 2010
It is demonstrated for the first time how model parameter, structural and data uncertainties can be accounted for explicitly and simultaneously within the Generalized Likelihood Uncertainty Estimation (GLUE) methodology.
Abrahart, Ajami, Ajami, Armstrong, Beck, Beven, Beven, Beven, Boyle, Butts, Chow, Christopher J. A. Macleod, Clark, Duan, Freer, Freer, Freer, Gary S. Bilotta, Georgakakos, Harmel, Hsu, Huard, Huard, Jim Freer, John N. Quinton, Jothityangkoon, Kavetski, Kavetski, Kavetski, Kavetski, Kennedy, Kirkby, Klemeš, Kuczera, Liu, Marshall, Nash, Neuman, Pappenberger, Pappenberger, Patricia Butler, Philip M. Haygarth, Poeter, Priestley, Richard E. Brazier, Richards, See, See, Shamseldin, Shamseldin, Sivapalan, Smith, Thornley, Tobias Krueger, Vache, Vereecken, Vrugt, Vrugt, Vrugt, Vrugt, Vrugt, Wagener, Wagener, Wagener, Xiong, Ye, Young, Younger   +67 more
core   +2 more sources

Multi-step ahead forecasting of daily streamflow based on the transform-based deep learning model under different scenarios

Scientific Reports
Predicting runoff with precision holds immense importance for flood control, water resource management, and basin ecological dispatch. Deep learning, especially long short-term memory (LSTM) neural networks, has excelled in runoff prediction, often ...
Miao He, Xian Xu, Shaofei Wu, Chuanxiong Kang, Binbin Huang   +4 more
doaj   +1 more source

A Physical Modelling Environment for Laboratory‐Scale Assessment of Rainfall‐Runoff Responses in Urban Areas

Journal of Flood Risk Management
A laboratory‐based physical modeling environment has great potential to reproduce the complex physical hydrologic phenomena and understand the interactions of rainfall‐runoff processes in a visual and informative manner.
Haksoo Kim, Hojun Keum
doaj   +1 more source

Self-organizing linear output map (SOLO): An artificial neural network suitable for hydrologic modeling and analysis [PDF]

, 2002
Artificial neural networks (ANNs) can be useful in the prediction of hydrologic variables, such as streamflow, particularly when the underlying processes have complex nonlinear interrelationships.
Gao, X, Gupta, HV, Hsu, KL, Imam, B, Sorooshian, S   +4 more
core   +1 more source

Fuzzy awakening in rainfall-runoff modeling [PDF]

Hydrology Research, 2004
Rainfall-runoff relationships are widely used in many engineering hydrologic designs in urban and rural areas. Such relationships are obtained through the application of regression analysis in many studies. Unfortunately, in the classical regression approach to determine rainfall-runoff relationships, internal uncertainties are not taken explicitly ...
Abdüsselam Altunkaynak, Zekai Şen
openaire   +1 more source

Pemodelan Curah Hujan-Limpasan Menggunakan Artificial Neural Network (ANN) Dengan Metode Backpropagation [PDF]

, 2005
. Rainfall-runoff relation has been developed continuously by applying artificial intelligence as one of the black box model alternative called Artificial Neural Network.
Hadihardaja, I. K. (Iwan), Sutikno, S. (Sugeng)   +1 more
core