A new hybrid artificial neural networks for rainfall–runoff process modeling

Asadi, Shahrokh; Shahrabi, Jamal; Abbaszadeh, Peyman; Tabanmehr, Shabnam

doi:10.1016/j.neucom.2013.05.023

Cited by 117 publications

(49 citation statements)

References 64 publications

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“…The hybrid model development for rainfall-runoff and streamflow modeling can be classified into the following four types. First, to improve the model performance, the MLMs have been combined with statistical methods, including phase-space reconstruction [22,23], principal component analysis [24,25], fuzzy c-means clustering [7,22], k-means clustering [26,27], self-organizing map (SOM) [28,29] and bootstrap [30]. Second, the MLMs have been coupled with evolutionary optimization algorithms, including genetic algorithm (GA) [31,32], particle swarm optimization (PSO) [11,33], artificial bee colony [34], bat algorithm [35], and firefly algorithm [36].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Models Coupled with Variational Mode Decomposition: A New Approach for Modeling Daily Rainfall-Runoff

2018

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Accurate modeling for nonlinear and nonstationary rainfall-runoff processes is essential for performing hydrologic practices effectively. This paper proposes two hybrid machine learning models (MLMs) coupled with variational mode decomposition (VMD) to enhance the accuracy for daily rainfall-runoff modeling. These hybrid MLMs consist of VMD-based extreme learning machine (VMD-ELM) and VMD-based least squares support vector regression (VMD-LSSVR). The VMD is employed to decompose original input and target time series into sub-time series called intrinsic mode functions (IMFs). The ELM and LSSVR models are selected for developing daily rainfall-runoff models utilizing the IMFs as inputs. The performances of VMD-ELM and VMD-LSSVR models are evaluated utilizing efficiency and effectiveness indices. Their performances are also compared with those of VMD-based artificial neural network (VMD-ANN), discrete wavelet transform (DWT)-based MLMs (DWT-ELM, DWT-LSSVR, and DWT-ANN) and single MLMs (ELM, LSSVR, and ANN). As a result, the VMD-based MLMs provide better accuracy compared with the single MLMs and yield slightly better performance than the DWT-based MLMs. Among all models, the VMD-ELM and VMD-LSSVR models achieve the best performance in daily rainfall-runoff modeling with respect to efficiency and effectiveness. Therefore, the VMD-ELM and VMD-LSSVR models can be an alternative tool for reliable and accurate daily rainfall-runoff modeling.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Models Coupled with Variational Mode Decomposition: A New Approach for Modeling Daily Rainfall-Runoff

2018

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“…The following cases are some of the uses of neural networks in combination with other methods. Asadi et al (2013) offered a new combination of ANN for modeling rainfallrunoff in Aq Chay Basin, Iran. The proposed model was a combination of data processing methods, genetic algorithms, and Levenberg-Marquardt Algorithm for training the neural network input.…”

Section: Introductionmentioning

confidence: 99%

A new approach to flow simulation using hybrid models

et al. 2017

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The necessity of flow prediction in rivers, for proper management of water resource, and the need for determining the inflow to the dam reservoir, designing efficient flood warning systems and so forth, have always led water researchers to think about models with highspeed response and low error. In the recent years, the development of Artificial Neural Networks and Wavelet theory and using the combination of models help researchers to estimate the river flow better and better. In this study, daily and monthly scales were used for simulating the flow of Gamasiyab River, Nahavand, Iran. The first simulation was done using two types of ANN and ANFIS models. Then, using wavelet theory and decomposing input signals of the used parameters, sub-signals were obtained and were fed into the ANN and ANFIS to obtain hybrid models of WANN and WANFIS. In this study, in addition to the parameters of precipitation and flow, parameters of temperature and evaporation were used to analyze their effects on the simulation. The results showed that using wavelet transform improved the performance of the models in both monthly and daily scale. However, it had a better effect on the monthly scale and the WANFIS was the best model.

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“…In this study, a nonparametric Artificial Neural Network (ANN) model will also be proposed to describe the functional relationships between terrestrial water storage anomalies (TWSA) and hydrological variables (e.g., SMS, precipitation, temperature, and ET) with the purpose of generating a longer record of TWSA across the YTR basin. The ANN model has been widely and successfully applied to various problems in water resources management and forecasting of meteorological variables such as flood frequency analysis (Shu & Ouarda, ), rainfall‐runoff processing modeling (Asadi et al, ; Nourani, ), simulation and forecasting of streamflow (Noori & Kalin, ; Tongal & Booij, ; Wang et al, ), and spatial downscaling of global climate model outputs (Seyoum & Milewski, ). Among the most widely used network structures are the convolutional neural network, the recurrent neural network, the radial basis function network, long/short time memory, and the multilayer feedforward network (Hsu et al, ).…”

Section: Introductionmentioning

confidence: 99%

Total Basin Discharge From GRACE and Water Balance Method for the Yarlung Tsangpo River Basin, Southwestern China

Xie

Gao

et al. 2019

JGR Atmospheres

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Total basin discharge plays an important role in the hydrological cycle for the regions. Estimation of total basin discharge cannot only help us manage water resources well but also provide a better understanding about water resources variability and hydrologic cycle. In this study, the Gravity Recovery and Climate Experiment (GRACE) data combined with precipitation and evapotranspiration (ET) is used to present first estimate of total basin discharge (~60.2 km3/year) based on water balance method over the entire Yarlung Tsangpo River basin during 2003–2014. The estimated basin discharge shows a good correlation with observed runoff (NSE = 0.70; NRMSE = 0.37; BIAS = −6.1%). An artificial neural network (ANN) model is also proposed to build the relationship between terrestrial water storage anomalies (TWSAs) with the other hydrological data available (e.g., precipitation, temperature, ET, and soil moisture storage) during 2003–2010 and then applied to hindcasting TWSA before 2003 in order to generate a longer record of TWSA. The results show that ANN‐generated TWSA using soil moisture storage and ET matches best with GRACE‐derived TWSA during 2003–2010, showing a correlation coefficient (r) of 0.89 and a normalized root‐mean‐square error (NRMSE) of 0.40, which indicate that the ANN model is an effective way to generate TWSA beyond the GRACE data record. The comparison between total basin discharge using ANN‐generated TWSA with the observed runoff data during 1998–2010 indicates a significant consistency with NSE = 0.66, NRMSE = 0.41, and BIAS = −4.9%. Our findings illustrate that GRACE data and the ANN model can be jointly and effectively used to assess the total basin discharge in the large‐scale basins with limited hydrological data.

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A new hybrid artificial neural networks for rainfall–runoff process modeling

Cited by 117 publications

References 64 publications

Machine Learning Models Coupled with Variational Mode Decomposition: A New Approach for Modeling Daily Rainfall-Runoff

Machine Learning Models Coupled with Variational Mode Decomposition: A New Approach for Modeling Daily Rainfall-Runoff

A new approach to flow simulation using hybrid models

Total Basin Discharge From GRACE and Water Balance Method for the Yarlung Tsangpo River Basin, Southwestern China

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