scholarly journals Conjunction of SOM-based feature extraction method and hybrid wavelet-ANN approach for rainfall–runoff modeling

2013 ◽  
Vol 15 (3) ◽  
pp. 829-848 ◽  
Author(s):  
Vahid Nourani ◽  
Masoumeh Parhizkar
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Model Building ◽  
Self Organizing Map ◽  
Rainfall Runoff ◽  
Ann Model ◽  
Two Stage ◽  
Runoff Modeling ◽  
Proposed Model ◽  
Runoff Discharge

In rainfall–runoff modeling, the wavelet-ANN model, which includes a wavelet transform to capture multi-scale features of the process, as well as an artificial neural network (ANN) to predict the runoff discharge, is a beneficial approach. One of the essential steps in any ANN-based development process is determination of dominant input variables. This paper presents a two-stage procedure to model the rainfall–runoff process of the Delaney Creek and Payne Creek Basins, Florida, USA. The two-stage procedure includes data pre-processing and model building stages. In the data pre-processing stage, a wavelet transform is used to decompose the rainfall and runoff time series into several sub-series at different scales. Subsequently, independent sub-series are chosen via a self-organizing map (SOM). In the model building stage, selected sub-series are imposed as input data to a feed-forward neural network (FFNN) to forecast runoff discharge. To make a better interpretation of the model efficiency, the proposed model is compared with the Auto Regressive Integrated Moving Average with eXogenous input (ARIMAX) and with the ad hoc FFNN methods, without any data pre-processing. The results proved that the proposed model leads to better outcome especially in term of determination coefficient for detecting peak points (DCpeak).

scholarly journals Monthly Rainfall-Runoff Modeling for Sondur Reservoir

2021 ◽  
Vol 23 (07) ◽  
pp. 1453-1459
Author(s):  
Shashi Kant Jaiswal ◽  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Model Development ◽  
Monthly Rainfall ◽  
Nonlinear Mapping ◽  
Rainfall Runoff ◽  
Ann Model ◽  
Regression Procedure ◽  
Runoff Modeling ◽  
Artificial Neural Network Ann

This study presents the application of Artificial Neural Network (ANN) to modeling the rainfall-inflow relationship for Sondur Reservoir located in Chhattisgarh State of India. ANNs are usually assumed to be powerful tools for nonlinear mapping in various applications. ANN is superior to linear regression procedure used for rainfallinflow modeling. For model development twenty nine years data of monthly rainfall and inflow have been used. The results extracted from study indicated that the ANN model is efficient for rainfall-inflow modeling.

scholarly journals IMPLICATION OF SOM-ANN BASED CLUSTERING FOR MULTI-STATION RAINFALL-RUNOFF MODELING

2015 ◽  
pp. 198-210
Author(s):  
Vahid Nourani ◽  
Masoud Mehrvand ◽  
Aida Hosseini Baghanam
Keyword(s):  
Stream Flow ◽  
Precipitation Event ◽  
Time Lags ◽  
Self Organizing Map ◽  
Rainfall Runoff ◽  
Ann Model ◽  
Discharge Data ◽  
Runoff Modeling ◽  
Rainfall Runoff Model ◽  
Runoff Model

In this study the performance of ANN with feed-forward neural network (FFNN) algorithm evaluated rainfall-runoff modeling in five gauging stations in Florida State. In addition, for investigating the performance of ANN in multi-station discharge prediction, self-organizing map (SOM) clustering tool employed in order to cluster the input data with similar patterns, due to the large amount of records in multiple stations. The main aim of study is to investigate capability and accuracy of ANN based methods in multi-station discharge prediction. In order to consider multiple stations effect on watershed outlet discharge, different combinations for precipitation and discharge data of all stations with antecedent values over the watershed have been taken into account. In this way, application of the representatives from each cluster led to significantly reduction in the numbers of the input variables so that the optimal ANN structure could be proposed. Therefore, ANN as a data-driven model was trained to predict daily runoff for the Peace River basin via recorded values from July 1995 to July 2011. Three scenarios conducted the aim of research; first scenario was an integrated ANN model trained by the data of rainfall and runoff at multiple stations. The second scenario was a sequential ANN model processed with upstream discharge records in addition to rainfall data as inputs and downstream discharge values as target. Finally, third scenario was a SOM-ANN model, in which rainfall and runoff data were clustered according the homogeneity of data via (SOM). The center of each cluster as the dominant component of each cluster was imposed to ANN in order to present an optimal rainfall-runoff model over the watershed. In all scenarios, different data sets at various time lags in both rainfall and stream flow data were applied as inputs in ANN-based model to predict stream flow. Results show that ANN model coupled with SOM is useful tools for forecasting multi-station discharge and precipitation event response in the watershed. Furthermore, the comparison of scenarios leads to select the most efficient and optimal inputs to ANN which subsequently, presents the optimal multi-station rainfall-runoff model over the watershed.

scholarly journals Conjunction of emotional ANN (EANN) and wavelet transform for rainfall-runoff modeling

2018 ◽  
Vol 21 (1) ◽  
pp. 136-152 ◽  
Author(s):  
Elnaz Sharghi ◽  
Vahid Nourani ◽  
Amir Molajou ◽  
Hessam Najafi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wavelet Transform ◽  
Rainfall Runoff ◽  
Runoff Modeling ◽  
Dry Conditions ◽  
Model Efficiency ◽  
Artificial Neural ◽  
Unique Framework ◽  
Emotional System

Abstract The current research introduces a combined wavelet-emotional artificial neural network (WEANN) approach for one-time-ahead rainfall-runoff modeling of two watersheds with different geomorphological and land cover conditions at daily and monthly time scales, to utilize within a unique framework the ability of both wavelet transform (to mitigate the effects of non-stationary) and emotional artificial neural network (EANN, to identify and individualize wet and dry conditions by hormonal components of the artificial emotional system). To assess the efficiency of the proposed hybrid model, the model efficiency was also compared with so-called EANN models (as a new generation of ANN-based models) and wavelet-ANN (WANN) models (as a multi-resolution forecasting tool). The obtained results indicated that for daily scale modeling, WEANN outperforms the other models (EANN and WANN). Also, the obtained results for monthly modeling showed that WEANN could outperform the WANN and EANN models up to 17% and 35% in terms of validation and training efficiency criteria, respectively. Also, the obtained results highlighted the capability of the proposed WEANN approach to better learning of extraordinary and extreme conditions of the process in the training phase.

scholarly journals A new hybrid algorithm for rainfall–runoff process modeling based on the wavelet transform and genetic fuzzy system

2014 ◽  
Vol 16 (5) ◽  
pp. 1004-1024 ◽  
Author(s):  
Vahid Nourani ◽  
Ahmad Tahershamsi ◽  
Peyman Abbaszadeh ◽  
Jamal Shahrabi ◽  
Esmaeil Hadavandi
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Fuzzy System ◽  
Mean Squared Error ◽  
Rainfall Runoff ◽  
Time Step ◽  
Genetic Fuzzy System ◽  
Runoff Forecasting ◽  
Runoff Modeling ◽  
Runoff Discharge

In this paper, two hybrid artificial intelligence (AI) based models were introduced for rainfall–runoff modeling. In the first model, a genetic fuzzy system (GFS) was developed and evolved for the prediction of watersheds' runoff one time step ahead. In the second model, the wavelet-GFS (WGFS) model, wavelet transform was also used as a data pre-processing method prior to GFS modeling and in this way the main time series of two variables (rainfall and runoff) were decomposed into some multi-frequency time series by the wavelet transform. Then, the GFS was trained using the transformed time series, and finally the runoff discharge was predicted one time step ahead. In addition, to specify the capability and reliability of the proposed WGFS model, multi-step ahead runoff forecasting was also implemented for the watersheds. The obtained results through the application of the models for rainfall–runoff modeling of two distinct watersheds, located in Azerbaijan, Iran showed that the runoff could be better forecasted through the proposed WGFS model than other AI-based models in terms of determination coefficient and root mean squared error criteria in both training and verifying steps.

scholarly journals A Two-Stage Exon Recognition Model Based on Synergetic Neural Network

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Zhehuang Huang ◽  
Yidong Chen
Keyword(s):  
Neural Network ◽  
Digital Signal ◽  
Two Stage ◽  
Recognition Model ◽  
Artificial Fish Swarm Algorithm ◽  
Proposed Model ◽  
Artificial Fish Swarm ◽  
Recognition Efficiency ◽  
Processing Techniques ◽  
Signal Processing Techniques

Exon recognition is a fundamental task in bioinformatics to identify the exons of DNA sequence. Currently, exon recognition algorithms based on digital signal processing techniques have been widely used. Unfortunately, these methods require many calculations, resulting in low recognition efficiency. In order to overcome this limitation, a two-stage exon recognition model is proposed and implemented in this paper. There are three main works. Firstly, we use synergetic neural network to rapidly determine initial exon intervals. Secondly, adaptive sliding window is used to accurately discriminate the final exon intervals. Finally, parameter optimization based on artificial fish swarm algorithm is used to determine different species thresholds and corresponding adjustment parameters of adaptive windows. Experimental results show that the proposed model has better performance for exon recognition and provides a practical solution and a promising future for other recognition tasks.

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