EFFICIENT METHODS OF AUTOMATIC CALIBRATION FOR RAINFALL-RUNOFF MODELLING IN THE FLOREON&lt;sup&gt;+&lt;/sup&gt; SYSTEM

Theuer, Matyáš; Vavrik, Radim; Vondrák, Vít; Kuchař, Štěpán; Šír, Boris; Portero, Antoni

doi:10.14311/nnw.2017.27.022

Cited by 2 publications

(2 citation statements)

References 25 publications

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“…Rainfall-runoff models are usually used for predicting behaviour of river discharge and water level and one of the inputs of rainfall-runoff models is the information about weather conditions in the near future. These data are provided by numerical weather prediction models [2,5]. Common method for estimating precision of the output of Rainfall-Runoff simulation is computation of the Nash-Sutcliffe model efficiency coefficient.…”

Section: Disaster Management: Uncertainty In Rainfall Run-off Model Ementioning

confidence: 99%

Run-Time Application Developing Phases for Energy Trade-Offs in HPC System Node

Portero¹,

Vavrik²,

Kuchař³

et al. 2015

Proceedings of the 2015 International Conference on Electrical, Automation and Mechanical Engineering

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In this paper is proposed a HPC system running with an RTOS that is aware and monitors the health of the platform. On the system, an application for disaster management is running. The application can be executed with different QoS depending on the situation. We defined two main situations normal execution when there is no possibility of disaster. And, a scenario where the possibilities for a disaster is very high. Then, the resource allocation for improving the precision and human decision has to be taken into account. The paper shows that at design time, it is possible to describe the different phases for developing a run-time application that is aware of the power consumption and tries to minimized when the situation is not in the scenario of criticality. Since, power consumption is a critical consideration in high performance computing systems and it is becoming the limiting factor to build and operate Petascale and Exascale systems.

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Section: Disaster Management: Uncertainty In Rainfall Run-off Model Ementioning

confidence: 99%

Run-Time Application Developing Phases for Energy Trade-Offs in HPC System Node

Portero¹,

Vavrik²,

Kuchař³

et al. 2015

Proceedings of the 2015 International Conference on Electrical, Automation and Mechanical Engineering

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“…Consequently, the present article proposes a Hybrid Neural Network (HNN) based machine learning model for rainfall prediction [39], [20], [9] based on weather data consisting of features namely vapor content, relative humidity, atmospheric pressure, and temperature. The data is gathered by Dumdum meteorological center situated at West Bengal, India within a span of six years.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Neural Network Based Rainfall Prediction Supported by Flower Pollination Algorithm

Chatterjee¹,

Datta²,

Dey³

2018

NNW

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The present work proposes a hybrid neural network based model for rainfall prediction in the Southern part of the state West Bengal of India. The hybrid model is a multistep method. Initially, the data is clustered into a reasonable number of clusters by applying fuzzy c-means algorithm, then for every cluster a separate Neural Network (NN) is trained with the data points of that cluster using well known metaheuristic Flower Pollination Algorithm (FPA). In addition, as a preprocessing phase a feature selection phase is included. Greedy forward selection algorithm is employed to find the most suitable set of features for predicting rainfall. To establish the ingenuity of the proposed hybrid prediction model (Hybrid Neural Network or HNN) has been compared with two well-known models namely multilayer perceptron feed-forward network (MLP-FFN) using different performance metrics. The data set for simulating the model is collected from Dumdum meteorological station (West Bengal, India), recorded with in the 1989 to 1995. The simulation results have revealed that the proposed model is significantly better than traditional methods in predicting rainfall.

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EFFICIENT METHODS OF AUTOMATIC CALIBRATION FOR RAINFALL-RUNOFF MODELLING IN THE FLOREON<sup>+</sup> SYSTEM

Cited by 2 publications

References 25 publications

Run-Time Application Developing Phases for Energy Trade-Offs in HPC System Node

Run-Time Application Developing Phases for Energy Trade-Offs in HPC System Node

Hybrid Neural Network Based Rainfall Prediction Supported by Flower Pollination Algorithm

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