scholarly journals Reservoir Inflow Prediction under GCM Scenario Downscaled by Wavelet Transform and Support Vector Machine Hybrid Models

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Gusfan Halik ◽  
Nadjadji Anwar ◽  
Budi Santosa ◽  
Edijatno

Climate change has significant impacts on changing precipitation patterns causing the variation of the reservoir inflow. Nowadays, Indonesian hydrologist performs reservoir inflow prediction according to the technical guideline of Pd-T-25-2004-A. This technical guideline does not consider the climate variables directly, resulting in significant deviation to the observation results. This research intends to predict the reservoir inflow using the statistical downscaling (SD) of General Circulation Model (GCM) outputs. The GCM outputs are obtained from the National Center for Environmental Prediction/National Center for Atmospheric Research Reanalysis (NCEP/NCAR Reanalysis). A new proposed hybrid SD model named Wavelet Support Vector Machine (WSVM) was utilized. It is a combination of the Multiscale Principal Components Analysis (MSPCA) and nonlinear Support Vector Machine regression. The model was validated at Sutami Reservoir, Indonesia. Training and testing were carried out using data of 1991–2008 and 2008–2012, respectively. The results showed that MSPCA produced better extracting data than PCA. The WSVM generated better reservoir inflow prediction than the one of technical guideline. Moreover, this research also applied WSVM for future reservoir inflow prediction based on GCM ECHAM5 and scenario SRES A1B.

2018 ◽  
Vol 211 ◽  
pp. 03009
Author(s):  
Purushottam Gangsar ◽  
Rajiv Tiwari

This paper analyzes the effect of noise on support vector machine (SVM) based fault diagnosis of IM (IM). For this, a number of mechanical (bearing fault, unbalanced rotor, bowed rotor and misaligned rotor) and electrical faults (broken rotor bar, stator winding fault with two severity levels and phase unbalance with two severity levels) of IM are considered here. The vibration and current signals are used here for the diagnosis. Different experiments were performed in order to generate these signals at various operating condition of IM (Speed and Load). Time domain feature are then extracted from the raw vibration and current signals obtained from the experiments. Then, the noise are added in the raw signals and the same features are extracted from this corrupted signals. The features from the original and corrupted signals are used to feed the classifier. The one-versus-one multiclass SVM are used here to perform multi-fault diagnosis. The comparative analysis of performance of the SVM classifier using data with and without noise is presented.


2007 ◽  
Vol 7 (1) ◽  
pp. 127-202 ◽  
Author(s):  
A. Pozzer ◽  
P. Jöckel ◽  
H. Tost ◽  
R. Sander ◽  
L. Ganzeveld ◽  
...  

Abstract. The atmospheric-chemistry general circulation model ECHAM5/MESSy1 is evaluated with observations of different organic ozone precursors. This study continues a prior analysis which focused primarily on the representation of atmospheric dynamics and ozone. We use the results of the same reference simulation and apply a statistical analysis using data from numerous field campaigns. The results serve as a basis for future improvements of the model system. ECHAM5/MESSy1 generally reproduces the spatial distribution and the seasonal cycle of carbon monoxide (CO) very well. However, for the background in the northern hemisphere we obtain a negative bias (mainly due to an underestimation of emissions from fossil fuel combustion), and in the high latitude southern hemisphere a yet unexplained positive bias. The model results agree well with observations of alkanes, whereas severe problems in the simulation of alkenes are present. For oxygenated compounds the results are ambiguous: The model results are in good agreement with observations of formaldehyde, but systematic biases are present for methanol and acetone. The discrepancies between the model results and the observations are explained (partly) by means of sensitivity studies.


2020 ◽  
Vol 14 ◽  
pp. 37-42
Author(s):  
Artur Całuch ◽  
Adam Cieślikowski ◽  
Małgorzata Plechawska-Wójcik

This article presents the process of adapting support vector machine model’s parameters used for studying the effect of traffic light cycle length parameter’s value on traffic quality. The survey is carried out using data collected during running simulations in author’s traffic simulator. The article shows results of searching for optimum traffic light cycle length parameter’s value.


Author(s):  
Saeed Farzin ◽  
Mahdi Valikhan Anaraki

Abstract In the present study, for the first time, a new strategy based on a combination of the hybrid least-squares support-vector machine (LS-SVM) and flower pollination optimization algorithm (FPA), average 24 general circulation model (GCM) output, and delta change factor method has been developed to achieve the impacts of climate change on runoff and suspended sediment load (SSL) in the Lighvan Basin in the period (2020–2099). Also, the results of modeling were compared to those of LS-SVM and adaptive neuro-fuzzy inference system (ANFIS) methods. The comparison of runoff and SSL modeling results showed that the LS-SVM-FPA algorithm had the best results and the ANFIS algorithm had the worst results. After the acceptable performance of the LS-SVM-FPA algorithm was proved, the algorithm was used to predict runoff and SSL under climate change conditions based on ensemble GCM outputs for periods (2020–2034, 2035–2049, 2070–2084, and 2085–2099) under three scenarios of RCP2.6, RCP4.5, and RCP8.5. The results showed a decrease in the runoff in all periods and scenarios, except for the two near periods under the RCP2.6 scenario for runoff. The predicted runoff and SSL time series also showed that the SSL values were lower than the average observation period, except for 2036–2039 (up to an 8% increase in 2038).


Mathematics ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 152 ◽  
Author(s):  
Su-qi Zhang ◽  
Kuo-Ping Lin

Short-term traffic flow forecasting is the technical basis of the intelligent transportation system (ITS). Higher precision, short-term traffic flow forecasting plays an important role in alleviating road congestion and improving traffic management efficiency. In order to improve the accuracy of short-term traffic flow forecasting, an improved bird swarm optimizer (IBSA) is used to optimize the random parameters of the extreme learning machine (ELM). In addition, the improved bird swarm optimization extreme learning machine (IBSAELM) model is established to predict short-term traffic flow. The main researches in this paper are as follows: (1) The bird swarm optimizer (BSA) is prone to fall into the local optimum, so the distribution mechanism of the BSA optimizer is improved. The first five percent of the particles with better fitness values are selected as producers. The last ten percent of the particles with worse fitness values are selected as beggars. (2) The one-day and two-day traffic flows are predicted by the support vector machine (SVM), particle swarm optimization support vector machine (PSOSVM), bird swarm optimization extreme learning machine (BSAELM) and IBSAELM models, respectively. (3) The prediction results of the models are evaluated. For the one-day traffic flow sequence, the mean absolute percentage error (MAPE) values of the IBSAELM model are smaller than the SVM, PSOSVM and BSAELM models, respectively. The experimental analysis results show that the IBSAELM model proposed in this study can meet the actual engineering requirements.


2005 ◽  
Vol 18 (4) ◽  
pp. 597-609 ◽  
Author(s):  
Jacques Derome ◽  
Hai Lin ◽  
Gilbert Brunet

Abstract A primitive equation dry atmospheric model is used to perform ensemble seasonal predictions. The predictions are done for 51 winter seasons [December–January–February (DJF)] from 1948 to 1998. Ensembles of 24 forecasts are produced, with initial conditions of 1 December plus small perturbations. The model uses a forcing field that is calculated empirically from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalyses. The forcing used to forecast a given winter is the sum of its winter climatological forcing plus an anomaly. The anomalous forcing is obtained as that of the month prior to the start of the forecast (November), which is also calculated from NCEP data. The predictions are thus made without using any information about the season to be predicted. The ensemble-mean predictions for the 51 winters are verified against the NCEP–NCAR reanalyses. Comparisons are made with the results obtained with a full GCM. It is found that the skill of the simple GCM is comparable in many ways to that of the full GCM. The skill in predicting the amplitude of the main patterns of Northern Hemisphere mean-seasonal variability, the Arctic Oscillation (AO) and the Pacific–North American (PNA) pattern is also discussed. The simple GCM has skill not only in predicting the PNA pattern during winters with strong ENSO forcing, but it also has skill in predicting the AO in winters without appreciable ENSO forcing.


2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Han-Ying Kao ◽  
Tao-Ku Chang ◽  
Yi-Cheng Chang

This study proposes the hybrid data envelopment analysis (DEA) and support vector machine (SVM) approaches for efficiency estimation and classification in web security. In the proposed framework, the factors and efficiency scores from DEA models are integrated with SVM for learning patterns of web security performance and provide further decision support. The numerical case study of hospital web security efficiency is demonstrated to support the feasibility of this design.


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