optimizing neural network for monthly rainfall-runoff modeling with denoised-jittered data
نویسندگان
چکیده
successful modeling of hydro-environmental processes widely relies on quantity and quality of accessible data and noisy data might effect on the functioning of the modeling. on the other hand in training phase of any artificial intelligence (ai) based model, each training data set is usually a limited sample of possible patterns of the process and hence, might not show the behavior of whole population. accordingly in the present article first, wavelet-based denoising method was used in order to smooth hydrological time series and then small normally distributed noises with the mean of zero and various standard deviations were generated and added to the smoothed time series to form different denoised-jittered training data sets, for artificial neural network (ann) modeling of monthly rainfall – runoff process of the pole saheb(anyan) station in zarrineh river watershed, which is a portion of orumiyeh lake drainage basin, that is located in iran. to evaluate the modeling performance, the obtained results were compared with those of multi linear regression and auto regressive integrated moving average models. comparison of the obtained results via the trained ann using denoised- jittered data showed that the proposed data pre-processing approach could improve performance of the ann based rainfall-runoff modeling of the case study up to 38% in the verification phase.
منابع مشابه
application of several data-driven techniques for rainfall-runoff modeling
in this study, several data-driven techniques including system identification, adaptive neuro-fuzzy inference system (anfis), artificial neural network (ann) and wavelet-artificial neural network (wavelet-ann) models were applied to model rainfall-runoff (rr) relationship. for this purpose, the daily stream flow time series of hydrometric station of hajighoshan on gorgan river and the daily rai...
متن کاملArtificial Neural Network Model for Rainfall-Runoff Relationship
Conceptual models have been widely used and are considered to be the best choice for describing the runoff process in a watershed. In most cases, the solution accuracy is mainly based on the topographic and hydrologic information subject to certain requirements for model calibration. Thus, these types of model are inappropriate for watershed area with little hydrologic data. Artificial neural n...
متن کاملDual Artificial Neural Network for Rainfall-Runoff Forecasting
One of the principal issues related to hydrologic models for prediction of runoff is the estimation of extreme values (floods). It is well understood that unless the models capture the dynamics of rainfall-runoff process, the improvement in prediction of such extremes is far from reality. In this paper, it is proposed to develop a dual (combined and paralleled) artificial neural network (D-ANN)...
متن کاملMonthly Rainfall Prediction Using Wavelet Neural Network Analysis
Rainfall is one of the most significant parameters in a hydrological model. Several models have been developed to analyze and predict the rainfall forecast. In recent years, wavelet techniques have been widely applied to various water resources research because of their timefrequency representation. In this paper an attempt has been made to find an alternative method for rainfall prediction by ...
متن کاملNeural network emulation of a rainfall-runoff model
The potential of an artificial neural network to perform simple non-linear hydrological transformations is examined. Four neural network models were developed to emulate different facets of a recognised non-linear hydrological transformation equation that possessed a small number of variables and contained no temporal component. The 5 modeling process was based on a set of uniform random distri...
متن کاملمنابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
journal of tethysجلد ۴، شماره ۴، صفحات ۲۸۴-۲۹۴
میزبانی شده توسط پلتفرم ابری doprax.com
copyright © 2015-2023