Data-Driven Approach for Rainfall-Runoff Modelling Using Equilibrium Optimizer Coupled Extreme Learning Machine and Deep Neural Network
نویسندگان
چکیده
Rainfall-runoff (R-R) modelling is used to study the runoff generation of a catchment. The quantity or rate change measure hydrological variable, called runoff, important for environmental scientists accomplish water-related planning and design. This paper proposes (i) an integrated model namely EO-ELM (an integration equilibrium optimizer (EO) extreme learning machine (ELM)) (ii) deep neural network (DNN) one day-ahead R-R modelling. proposed models are validated at two different benchmark stations catchments, river Teifi Glanteifi Fal Tregony in UK. Firstly, partial autocorrelation function (PACF) optimal number lag inputs deploy models. Six other well-known models, ELM, kernel ELM (KELM), particle swarm optimization-based (PSO-ELM), support vector regression (SVR), artificial (ANN) gradient boosting (GBM) utilized validate terms prediction efficiency. Furthermore, increase performance utilizes discrete wavelet-based data pre-processing technique applied rainfall data. DNN compared with (WELM), KELM (WKELM), PSO-ELM (WPSO-ELM), SVR (WSVR), ANN (WANN) GBM (WGBM). An uncertainty analysis two-tailed t-test carried out ensure trustworthiness efficacy experimental results time series datasets show that performs better lags than others. In case daily modelling, performed showed robustness used. Therefore, this shows efficient applicability may be field.
منابع مشابه
Rainfall-runoff modelling using artificial neural networks (ANNs): modelling and understanding
In recent years, artificial neural networks (ANNs) have become one of the most promising tools in order to model complex hydrological processes such as the rainfall-runoff process. In many studies, ANNs have demonstrated superior results compared to alternative methods. ANNs are able to map underlying relationship between input and output data without prior understanding of the process under in...
متن کاملapplication 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 کاملRainfall - Runoff Modelling Using Artificial Neural Networks ( ANNs )
Over the last decades or so, artificial neural networks (ANNs) have become one of the most promising tools for modelling hydrological processes such as rainfall-runoff processes. In most studies, ANNs have been demonstrated to show superior result compared to the traditional modelling approaches. They are able to map underlying relationships between input and output data without detailed knowle...
متن کاملSpeech emotion recognition using deep neural network and extreme learning machine
Speech emotion recognition is a challenging problem partly because it is unclear what features are effective for the task. In this paper we propose to utilize deep neural networks (DNNs) to extract high level features from raw data and show that they are effective for speech emotion recognition. We first produce an emotion state probability distribution for each speech segment using DNNs. We th...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Applied sciences
سال: 2021
ISSN: ['2076-3417']
DOI: https://doi.org/10.3390/app11136238