Runoff prediction using an integrated hybrid modelling scheme

Remesan, Renji; Shamim, Muhammad Ali; Han, Dawei; Mathew, Jimson

doi:10.1016/j.jhydrol.2009.03.034

Cited by 94 publications

(35 citation statements)

References 46 publications

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“…Among the various machine learning methods, artificial neural networks (ANNs), which include back-propagation neural network (BPNN), radial basis function (RBF) neural network, generalized regression neural network (GRNN), Elman neural network, and multilayer feed-forward (MLFF) network, are among the most popular techniques for hydrological time series forecasting [17]. Although data driven models have attained high levels in the hydrological field, there is still space present to improve the forecasting methods [18]. Hydrological processes are non-linear and arbitrary.…”

Section: Introductionmentioning

confidence: 99%

Wavelet-ANN versus ANN-Based Model for Hydrometeorological Drought Forecasting

Khan

Muhammad

El-Shafie

2018

Water

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Malaysia is one of the countries that has been experiencing droughts caused by a warming climate. This study considered the Standard Index of Annual Precipitation (SIAP) and Standardized Water Storage Index (SWSI) to represent meteorological and hydrological drought, respectively. The study area is the Langat River Basin, located in the central part of peninsular Malaysia. The analysis was done using rainfall and water level data over 30 years, from 1986 to 2016. Both of the indices were calculated in monthly scale, and two neural network-based models and two wavelet-based artificial neural network (W-ANN) models were developed for monthly droughts. The performance of the SIAP and SWSI models, in terms of the correlation coefficient (R), was 0.899 and 0.968, respectively. The application of a wavelet for preprocessing the raw data in the developed W-ANN models achieved higher correlation coefficients for most of the scenarios. This proves that the created model can predict meteorological and hydrological droughts very close to the observed values. Overall, this study helps us to understand the history of drought conditions over the past 30 years in the Langat River Basin. It further helps us to forecast drought and to assist in water resource management.

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Section: Introductionmentioning

confidence: 99%

Wavelet-ANN versus ANN-Based Model for Hydrometeorological Drought Forecasting

Khan

Muhammad

El-Shafie

2018

Water

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“…Some recent studies reported that integration of wavelet transformation technique with ANN yields superior results compared to simple ANN and regression models (Anctil & Tape 2004;Chou & Wang 2004;Zhou et al 2006;Partal & Kisi 2007;Nourani et al 2008;Kisi 2008bKisi , 2009aRemesan et al 2009). This advanced pre-processing of raw data to capture the non-stationary behaviour of the time series data by decomposing the original series into wavelet coefficients of different frequency bands has been effectively applied by site is shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Daily river flow forecasting using wavelet ANN hybrid models

Pramanik

Panda

Singh

2010

Journal of Hydroinformatics

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“…The M test analysis on the 30 min data is shown in Figure 3 To check the authenticity of GT analysis, we performed a cross-correlation analysis (Tayfur & Guldal 2006) between the target runoff dataset Q(t) and different lag time series of precipitation and runoff using daily rainfallrunoff data. A study by Remesan et al (2009) identified three-step antecedent runoff values (Q(t 2 1), Q(t 2 2), Q(t 2 3)), one-step antecedent rainfall (P(t 2 1)) and current rainfall information (P(t)) are the best for daily rainfall -runoff modelling, with a training data length of 1,056 data points. The cross-correlation analysis between the target runoff dataset Q(t) and different lag time series of precipitation and runoff data (viz.…”

Section: The Gamma Statistic (G) and Standard Error (Se) Variationmentioning

confidence: 99%

“…This novel technique, the Gamma Test, enables us to quickly evaluate and estimate the best mean squared error that can be achieved by a smooth model on unseen data for a given selection of inputs, prior to complex and time-consuming model construction. The abilities of GT have been demonstrated in case studies in water level and flow modelling (Durrant 2001;Remesan et al 2009), daily solar radiation prediction (Remesan et al 2008;Moghaddamnia et al 2009) and evapotranspiration estimation (Ghafari et al 2009;Piri et al 2009). This technique can be used to find the best embedded dimensions and time lags for time series analysis.…”

Section: Introductionmentioning

confidence: 99%

Effect of data time interval on real-time flood forecasting

Remesan

Ahmadi

Shamim

et al. 2010

Journal of Hydroinformatics

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Rainfall-runoff is a complicated nonlinear process and many data mining tools have demonstrated their powerful potential in its modelling but still there are many unsolved problems. This paper addresses a mostly ignored area in hydrological modelling: data time interval for models. Modern data collection and telecommunication technologies can provide us with very high resolution data with extremely fine sampling intervals. We hypothesise that both too large and too small time intervals would be detrimental to a model's performance, which has been illustrated in the case study. It has been found that there is an optimal time interval which is different from the original data time interval (i.e. the measurement time interval). It has been found that the data time interval does have a major impact on the model's performance, which is more prominent for longer lead times than for shorter ones. This is highly relevant to flood forecasting since a flood modeller usually tries to stretch his/her model's lead time as far as possible. If the selection of data time interval is not considered, the model developed will not be performing at its full potential. The application of the Gamma Test and Information Entropy introduced in this paper may help the readers to speed up their data input selection process.

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Runoff prediction using an integrated hybrid modelling scheme

Cited by 94 publications

References 46 publications

Wavelet-ANN versus ANN-Based Model for Hydrometeorological Drought Forecasting

Wavelet-ANN versus ANN-Based Model for Hydrometeorological Drought Forecasting

Daily river flow forecasting using wavelet ANN hybrid models

Effect of data time interval on real-time flood forecasting

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