Flood routing modelling with Artificial Neural Networks

Peters, Ronny; Schmitz, Gerd; Cullmann, Johannes

doi:10.5194/adgeo-9-131-2006

Cited by 29 publications

(21 citation statements)

References 3 publications

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“…Further they describe that the used model has two layers one is hidden layer and another is an output layer. Kumar et al, [45], have developed an artificial neural network (ANN) model for prediction of weekly mean temperature. They used various algorithms and found that the back-propagation algorithm is better for weekly temperature prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of Neural Network in Drought Forecasting; An Intense Literature Review

Yadu¹,

Shrivastava²

2019

IJCET

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India is the agrarian country. The overall economy of our country is based on agriculture. Although the methods of cultivation are traditional and not hi-tech thus more over 75% of our farmers are dependent on monsoon. Prediction of actual monsoon is a challenge for meteorological scientists. Since the climatic data time series shows highly non-linear and chaotic behavior thus its forecast is still an enigma. Thus, forecasting of climate phenomenon is a challenging issue for the researchers round the globe. However, it is a prime necessity to forecast climatic changes such as Rainfall (daily rainfall, monthly rainfall, heavy rainfall etc.), Flood, Drought, minimum and maximum Temperature, River flow etc. To recognize applications of Artificial Neural Network (ANNs) in weather forecasting, especially in drought forecasting a comprehensive literature review from 2000 to 2017 is done and presented in this paper. In the study, more over 90 contributions have been surveyed and it has been observed that the architecture of ANN such as BPN, RBFN, MLP, ANFIS, ARIMA etc. are found best to forecast chaotic behavior and have efficient enough to forecast drought as well as other weather phenomenon over broader or smaller homogeneous region.

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Section: Literature Reviewmentioning

confidence: 99%

Application of Neural Network in Drought Forecasting; An Intense Literature Review

Yadu¹,

Shrivastava²

2019

IJCET

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“…Examples of applications have included the accurate identification of slope failure processes (Fernandez-Steeger 2002), the modelling of spatial air pollution patterns (Lin et al 2004) or the propagation of flood waves (Peters et al 2006). Beak Consultants GmbH uses artificial neural networks for the modelling of geoscientific problems since 2006.…”

Section: Method: Artificial Neural Networkmentioning

confidence: 99%

Spatial predictive mapping using artificial neural networks

Noack¹,

Knobloch²,

Etzold³

et al. 2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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The modelling or prediction of complex geospatial phenomena (like formation of geo-hazards) is one of the most important tasks for geoscientists. But in practice it faces various difficulties, caused mainly by the complexity of relationships between the phenomena itself and the controlling parameters, as well by limitations of our knowledge about the nature of physical/ mathematical relationships and by restrictions regarding accuracy and availability of data. In this situation methods of artificial intelligence, like artificial neural networks (ANN) offer a meaningful alternative modelling approach compared to the exact mathematical modelling. In the past, the application of ANN technologies in geosciences was primarily limited due to difficulties to integrate it into geo-data processing algorithms. In consideration of this background, the software advangeo® was developed to provide a normal GIS user with a powerful tool to use ANNs for prediction mapping and data preparation within his standard ESRI ArcGIS environment. In many case studies, such as land use planning, geo-hazards analysis and prevention, mineral potential mapping, agriculture & forestry advangeo® has shown its capabilities and strengths. The approach is able to add considerable value to existing data.

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“…It may take a long time for these models to solve the equations numerically, and they may run into instability. Peters et al (2006) discussed the difficulties of using hydrodynamic models in real-time operations. To avoid instabilities, it is recommended to decrease computational time step that leads to considerable computational times.…”

Section: Introductionmentioning

confidence: 99%

Application of surrogate artificial intelligent models for real‐time flood routing

Ghalkhani

Golian

Saghafian

et al. 2012

Water & Environment J

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Developing a robust flood forecasting and warning system (FFWS) is essential in flood‐prone areas. Hydrodynamic models, which are a major part of such systems, usually suffer from computational instabilities and long runtime problems, which are particularly important in real‐time applications. In this study, two artificial intelligence models, namely artificial neural network (ANN) and adaptive neuro‐fuzzy inference system (ANFIS), were used for flood routing in an FFWS in Madarsoo river basin, Iran. For this purpose, different rainfall patterns were transformed to run‐off hydrographs using the Hydrologic Engineering Center (HEC)‐1 hydrological model and routed along the river using HEC river analysis system RAS hydrodynamic model. Then, the simulated hydrographs with different lag times were used as inputs for training of ANN and ANFIS models to simulate flood hydrograph at the basin outlet. Results showed that the simulations obtained from ANN and ANFIS coincided with the results simulated by the HEC‐RAS, and application of such models is strongly suggested as a backup tool for flood routing in FFWSs.

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Flood routing modelling with Artificial Neural Networks

Cited by 29 publications

References 3 publications

Application of Neural Network in Drought Forecasting; An Intense Literature Review

Application of Neural Network in Drought Forecasting; An Intense Literature Review

Spatial predictive mapping using artificial neural networks

Application of surrogate artificial intelligent models for real‐time flood routing

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