Choice of rainfall inputs for event-based rainfall-runoff modeling in a catchment with multiple rainfall stations using data-driven techniques

2017 ◽  
Vol 545 ◽  
pp. 100-108 ◽  
Author(s):  
Tak Kwin Chang ◽  
Amin Talei ◽  
Sina Alaghmand ◽  
Melanie Po-Leen Ooi
Keyword(s):  
Data Driven ◽  
Rainfall Runoff ◽  
Runoff Modeling ◽  
Using Data ◽  
Event Based

scholarly journals Rainfall Runoff Modelling Based on Genetic Programming

2002 ◽  
Vol 33 (5) ◽  
pp. 331-346 ◽  
Author(s):  
Vladan Babovic ◽  
Maarten Keijzer
Keyword(s):  
Genetic Programming ◽  
Conceptual Models ◽  
Linear Time ◽  
Physical Models ◽  
Data Driven ◽  
Rainfall Runoff ◽  
Considerable Time ◽  
Hydrologic Models ◽  
Spatially Distributed ◽  
Using Data

The runoff formation process is believed to be highly non-linear, time varying, spatially distributed, and not easily described by simple models. Considerable time and effort has been directed to model this process, and many hydrologic models have been built specifically for this purpose. All of them, however, require significant amounts of data for their respective calibration and validation. Using physical models raises issues of collecting the appropriate data with sufficient accuracy. In most cases it is difficult to collect all the data necessary for such a model. By using data driven models such as genetic programming (GP), one can attempt to model runoff on the basis of available hydrometeorological data. This work addresses use of genetic programming for creating rainfall-runoff models on the basis of data alone, as well as in combination with conceptual models (i.e taking advantage of knowledge about the problem domain).

RAINFALL RUNOFF MODELING BY MULTILAYER PERCEPTRON NEURAL NETWORK FOR LUI RIVER CATCHMENT

2016 ◽  
Vol 78 (6-12) ◽  
Author(s):  
Nadeem Nawaz ◽  
Sobri Harun ◽  
Rawshan Othman ◽  
Arien Heryansyah
Keyword(s):  
Neural Network ◽  
Multilayer Perceptron ◽  
Moving Average ◽  
Rainfall Runoff ◽  
Runoff Modeling ◽  
Hourly Rainfall ◽  
Physically Based ◽  
Auto Regressive ◽  
Event Based ◽  
Relationship Of

Reliable modeling for the rainfall-runoff processes embedded with high complexity and non-linearity can overcome the problems associated with managing a watershed. Physically based rainfall-runoff models need many realistic physical components and parameters which are sometime missing and hard to be estimated. In last decades the artificial intelligence (AI) has gained much popularity for calibrating the nonlinear relationships of rainfall–runoff processes. The AI models have the ability to provide direct relationship of the input to the desired output without considering any internal processes. This study presents an application of Multilayer Perceptron neural network (MLPNN) for the continuous and event based rainfall-runoff modeling to evaluate its performance for a tropical catchment of Lui River in Malaysia. Five years (1999-2013) daily and hourly rainfall and runoff data was used in this study. Rainfall-runoff processes were also simulated with a traditionally used statistical modeling technique known as auto-regressive moving average with exogenous inputs (ARMAX). The study has found that MLPNN model can be used as reliable rainfall-runoff modeling tool in tropical catchments.  

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