Data-Driven Approach for Rainfall-Runoff Modelling Using Equilibrium Optimizer Coupled Extreme Learning Machine and Deep Neural Network

Abstract

Rainfall-runoff (R-R) modelling is used to study the runoff generation of a catchment. The quantity or rate of change measure of the hydrological variable, called runoff, is important for environmental scientists to accomplish water-related planning and design. This paper proposes (i) an integrated model namely EO-ELM (an integration of equilibrium optimizer (EO) and extreme learning machine (ELM)) and (ii) a deep neural network (DNN) for one day-ahead R-R modelling. The proposed R-R models are validated at two different benchmark stations of the catchments, namely river Teifi at Glanteifi and river Fal at Tregony in the UK. Firstly, a partial autocorrelation function (PACF) is used for optimal number of lag inputs to deploy the proposed models. Six other well-known machine learning models, called ELM, kernel ELM (KELM), and particle swarm optimization-based ELM (PSO-ELM), support vector regression (SVR), artificial neural network (ANN) and gradient boosting machine (GBM) are utilized to validate the two proposed models in terms of prediction efficiency. Furthermore, to increase the performance of the proposed models, paper utilizes a discrete wavelet-based data pre-processing technique is applied in rainfall and runoff data. The performance of wavelet-based EO-ELM and DNN are compared with wavelet-based ELM (WELM), KELM (WKELM), PSO-ELM (WPSO-ELM), SVR (WSVR), ANN (WANN) and GBM (WGBM). An uncertainty analysis and two-tailed t-test are carried out to ensure the trustworthiness and efficacy of the proposed models. The experimental results for two different time series datasets show that the EO-ELM performs better in an optimal number of lags than the others. In the case of wavelet-based daily R-R modelling, proposed models performed better and showed robustness compared to other models used. Therefore, this paper shows the efficient applicability of EO-ELM and DNN in R-R modelling that may be used in the hydrological modelling field.

Application of classical and novel integrated machine learning models to predict sediment discharge during free-flow flushing

“…The EO-ELM model was compared with a Deep Neural Network (DNN) with ELM, Kernel ELM (KELM), particle swarm optimization-based ELM (PSO-ELM), SVR, ANN, and Gradient Boosting Machine (GBM). The Results demonstrated that the EO-ELM was the most accurate model 15 . Mahdavi-Meymand et al applied different kinds of GMDH that integrated with PSO and HGSO algorithms to simulate the maximum hydro-suction dredging depth.…”

Application of Rainfall-Runoff Simulation Based on the NARX Dynamic Neural Network Model

“…With the fast development of computer technology, ANN has been successfully applied in many fields due to its unique advantages of nonlinearity, non-convexity and self-adaptability [20][21][22]. The rainfall-runoff process is a highly nonlinear, time-varying, spatially uneven and dynamically uncertain process, and therefore ANN has been widely used to handle water resource problems such as rainfall forecasting and urban drainage systems [23][24][25]. A series of researches are used to explore the applicability of static ANNs, such as typical back propagation neural networks (BPNN) and extreme learning machines (ELM) [26][27][28].…”

A Novel Runoff Forecasting Model Based on the Decomposition-Integration-Prediction Framework

“…The runoff time series usually shows nonlinear and non-stationary characteristics [2][3][4][5]. In addition, many factors can impact the streamflow from upstream to downstream, such as tributaries, agricultural utilization, and dams; accurate runoff prediction is thus difficult and challenging [6][7][8].…”

“…However, stationarity is a sufficient and necessary condition for establishing time series models, which makes these models difficult to capture the nonstationary of runoff time series [12]. In contrast, because of its strong nonlinear fitting ability and advantages of nonstationary series processing, the machine learning models are widely introduced into runoff forecasting research, from the Back Propagation (BP) neural network model, which was initially applied to runoff forecasting to Support Vector Machine (SVM) [1,17,18], Extreme Learning Machine (ELM) [8,[19][20][21], and other machine learning models [22,23]. However, these classical models are easy to fall into local optimum and sensitive to parameter selection.…”