Impact of multi-resolution analysis of artificial intelligence models inputs on multi-step ahead river flow forecasting

Badrzadeh, Honey; Sarukkalige, Ranjan; Jayawardena, A. W.

doi:10.1016/j.jhydrol.2013.10.017

Cited by 70 publications

(34 citation statements)

References 46 publications

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“…Including other hydro-climatological variables such as rainfall (Toth and Brath 2007) and evaporation could increase the multi-step forecasting performance of ANNs. As mentioned in Badrzadeh et al (2013), the best forecasting performance obtained with ANN is by using an input combination of both river flow and rainfall. Additionally, pre-processing of the data could increase the multi-step-ahead forecasting performances of ANNs (Badrzadeh et al 2013).…”

Section: Multi-step Forecasting Results Of Setar Ann and K-nnmentioning

confidence: 97%

“…As mentioned in Badrzadeh et al (2013), the best forecasting performance obtained with ANN is by using an input combination of both river flow and rainfall. Additionally, pre-processing of the data could increase the multi-step-ahead forecasting performances of ANNs (Badrzadeh et al 2013). For k-nn models, since the observed streamflows exhibit chaotic dynamics, initially nearby trajectories where the nearest neighbors are located, diverge exponentially in which deterministic forecasting of higher lead times becomes almost meaningless due to the propagation of initial errors over the entire attractor (Sivakumar et al 1998).…”

Section: Multi-step Forecasting Results Of Setar Ann and K-nnmentioning

confidence: 97%

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Impact of complexity on daily and multi-step forecasting of streamflow with chaotic, stochastic, and black-box models

Tongal

Berndtsson

2016

Stoch Environ Res Risk Assess

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Despite significant research advances achieved during the last decades, seemingly inconsistent forecasting results related to stochastic, chaotic, and black-box approaches have been reported. Herein, we attempt to address the entropy/complexity resulting from hydrological and climatological conditions. Accordingly, mutual information function, correlation dimension, averaged false nearest neighbor with E1 and E2 quantities, and complexity analysis that uses sample entropy coupled with iterative amplitude adjusted Fourier transform were employed as nonlinear deterministic identification tools. We investigated forecasting of daily streamflow for three climatologically different Swedish rivers, Helge, Ljusnan, and Kalix Rivers using self-exciting threshold autoregressive (SETAR), k-nearest neighbor (k-nn), and artificial neural networks (ANN). The results suggest that the streamflow in these rivers during the 1957-2012 period exhibited dynamics from low to high complexity. Specifically, (1) lower complexity lead to higher predictability at all leadtimes and the models' worst performances were obtained for the most complex streamflow (Ljusnan River), (2) ANN was the best model for 1-day ahead forecasting independent of complexity, (3) SETAR was the best model for 7-day ahead forecasting by means of performance indices, especially for less complexity, (4) the largest error propagation was obtained with the k-nn and ANN and thus these models should be carefully used beyond 2-day forecasting, and (5) higher number input variables except for the dominant variables made insignificant impact on forecasting performances for ANN and k-nn models.

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Section: Multi-step Forecasting Results Of Setar Ann and K-nnmentioning

confidence: 97%

Section: Multi-step Forecasting Results Of Setar Ann and K-nnmentioning

confidence: 97%

Impact of complexity on daily and multi-step forecasting of streamflow with chaotic, stochastic, and black-box models

Tongal

Berndtsson

2016

Stoch Environ Res Risk Assess

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“…The most popular hybrid wavelet model for river flow forecasting is wavelet neural networks method (Kim and Valdes, 2003;Wang and Ding, 2003;Cannas et al, 2006;Kisi, 2009;Adamowski and Sun, 2010;Krishna et al, 2012;Nourani et al, 2013). The application of combining the wavelet analysis and neuro-fuzzy technique for hydrological forecasting has been investigated in a very few studies (Partal and Kisi, 2007;Kisi and Shiri, 2012;Badrzadeh et al, 2013). The application of hybrid wavelet-base model for river flow forecasting needs more investigation for different area with different characteristics.…”

Section: Introductionmentioning

confidence: 99%

Hourly runoff forecasting for flood risk management: Application of various computational intelligence models

Badrzadeh

Sarukkalige

Jayawardena

2015

Journal of Hydrology

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“…Other investigations, such as "Impact of multi-resolution analysis of artificial intelligence models inputs on multi-step ahead river flow forecasting" in Harvey River, Western Australia and "Real-time multi-step-ahead water level forecasting by recurrent neural networks for urban flood control" demonstrate the benefits that learning models based in artificial neural networks have in predicting the dynamics of rivers [18,19]. The same studies proved also that performance can be improved by creating hybrid models based on ANN models.…”

Section: Introductionmentioning

confidence: 99%

Daily river level forecast based on the development of an artificial neural network case study in La Virginia - Risaralda

Morales-Pinzón

Restrepo

Flórez-Calderón

2015

Rev. Fac. Ing. Antioquia

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ABSTRACT:The municipality of La Virginia (Risaralda, Colombia) is constantly affected by fl oods that originate from increased water levels in the Cauca River. Disaster relief agencies do not currently have adequate monitoring systems to identify potential overfl ow events in time-series observations to prevent fl ood damage to homes or injury to the general population. In this paper, various simulation models are proposed for the prediction of fl ooding that contributes as a technical tool to the development and implementation of early warning systems to improve the responsiveness of disaster relief agencies. The models, which are based on artifi cial neural networks, take hydroclimatological information from different stations along the Cauca River Basin, and the trend indicates the average daily level of the river within the next 48 hours. This methodology can be easily applied to other urban areas exposed to fl ood risks in developing countries. RESUMEN:El municipio de La Virginia (Risaralda, Colombia) está constantemente afectado por inundaciones que se originan debido al aumento de los niveles de agua en el río Cauca. Los organismos de socorro y atención de desastres actualmente no tienen sistemas adecuados de vigilancia para identifi car eventos potenciales de desbordamiento en el tiempo, y así evitar daños por inundaciones a las viviendas o daños a la población en general. En este trabajo, se proponen diversos modelos de simulación para la predicción de inundaciones que contribuye como una herramienta técnica para el desarrollo e implementación de sistemas de alerta temprana para mejorar la capacidad de respuesta de los organismos de socorro. Los modelos, están basados en redes neuronales artifi ciales y toman la información hidroclimatológica de diferentes estaciones existentes a lo largo de la cuenca del río Cauca, donde la tendencia indica el nivel medio diario del río durante las siguientes 48 horas. Esta metodología se puede aplicar fácilmente a otras zonas urbanas expuestas a los riesgos de inundación en países en desarrollo.

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Impact of multi-resolution analysis of artificial intelligence models inputs on multi-step ahead river flow forecasting

Cited by 70 publications

References 46 publications

Impact of complexity on daily and multi-step forecasting of streamflow with chaotic, stochastic, and black-box models

Impact of complexity on daily and multi-step forecasting of streamflow with chaotic, stochastic, and black-box models

Hourly runoff forecasting for flood risk management: Application of various computational intelligence models

Daily river level forecast based on the development of an artificial neural network case study in La Virginia - Risaralda

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