scholarly journals Probabilistic Assessment of Monthly River Flow Discharge Using Copula And OSVR Approaches

2021 ◽  
Author(s):  
Mohammad Nazeri Tahroudi ◽  
Rasoul Mirabbasi ◽  
Yousef Ramezani ◽  
Farshad Ahmadi
Keyword(s):  
River Flow ◽  
Garch Model ◽  
Support Vector ◽  
Monthly Precipitation ◽  
Arch Models ◽  
Vector Autoregressive ◽  
Flow Discharge ◽  
Simulation Results ◽  
Hydrometric Station ◽  
Hydrology And Water Resources

Abstract Simulation of flow discharge based on monthly precipitation values as inputs is one of the important issues in hydrology and water resources studies, especially in areas where data with the shorter time scales are not available. In this study, the applicability of support vector regression (SVR) model optimized by Ant colony and Copula-GARCH algorithms was investigated and compared to simulate the flow discharge based on total monthly rainfall in Talezang Basin, Iran. Entropy theory was used to select a suitable meteorological station corresponding to a hydrometric station. The vector autoregressive model was also used as the base model in Copula-GARCH simulations. The correlation results of the studied paired variable confirmed the possibility of using copula-based models. The simulation results were evaluated using R2, Nash-Sutcliffe Efficiency (NSE) and root mean square error (RMSE) statistics. According to the 99% confidence intervals of the simulations, the accuracy of both models was confirmed. The simulation results showed that the Copula-GARCH model was more accurate than the optimized SVR (OSVR) model. Considering the 90% efficiency (NSE = 0.90) of Copula-GARCH approach, the results show a 36% improvement of RMSE statistics by Copula-GARCH model compared to OSVR model in simulating the flow discharge on a monthly scale. The results also showed that by combining nonlinear ARCH models with the copula-based simulations, the reliability of the simulation results increases, which was also confirmed using the violin plot. The results also showed an increase in the accuracy of the Copula-GARCH model at the minimum and maximum values of the data.

Multivariate Hyper-Rotated GARCH-BEKK

2022 ◽  
Vol 0 (0) ◽  
Author(s):  
Manabu Asai ◽  
Michael McAleer
Keyword(s):  
Moving Average ◽  
Garch Model ◽  
Multivariate Garch ◽  
Regularity Conditions ◽  
Finite Sample ◽  
Arch Models ◽  
Vector Autoregressive ◽  
Alternative Representation ◽  
Multivariate Garch Model ◽  
Coefficient Vector

Abstract For large multivariate models of generalized autoregressive conditional heteroskedasticity (GARCH), it is important to reduce the number of parameters to cope with the ‘curse of dimensionality’. Recently, Laurent, Rombouts and Violante (2014 “Multivariate Rotated ARCH Models” Journal of Econometrics 179: 16–30) developed the rotated multivariate GARCH model, which focuses on the parameters for standardized variables. This paper extends the rotated multivariate GARCH model by considering a hyper-rotation, which uses a more flexible structure for the rotation matrix. The paper shows an alternative representation based on a random coefficient vector autoregressive and moving-average (VARMA) process, and provides the regularity conditions for the consistency and asymptotic normality of the quasi-maximum likelihood (QML) estimator for VARMA with hyper-rotated multivariate GARCH. The paper investigates the finite sample properties of the QML estimator for the new model. Empirical results for four exchange rate returns show the new specifications works satisfactory for reducing the number of parameters.

scholarly journals Obscure Image Classification and Restoration using Support Vector Machines

2019 ◽  
Vol 8 (4) ◽  
pp. 8231-8236
Keyword(s):  
Kernel Estimation ◽  
Classification Algorithm ◽  
Support Vector ◽  
Svm Classification ◽  
Vector Machines ◽  
Blurred Image ◽  
Simulation Results ◽  
Blurred Images ◽  
Identification And Characterization ◽  
Restoration Model

A restoration and classification computation for blurred image which depends on obscure identification and characterization is proposed in this paper. Initially, new obscure location calculation is proposed to recognize the Gaussian, Motion and Defocus based blurred locales in the image. The degradation-restoration model referred with pre-processing followed by binarization and features extraction/classification algorithm applied on obscure images. At this point, support vector machine (SVM) classification algorithm is proposed to cluster the blurred images. Once the obscure class of the locales is affirmed, the structure of the obscure kernels of the blurred images are affirmed. At that point, the obscure kernel estimation techniques are embraced to appraise the obscure kernels. At last, the blurred locales are re-established utilizing nonblind image deblurring calculation and supplant the blurred images with the restored images. The simulation results demonstrate that the proposed calculation performs well

scholarly journals Dampak Perubahan Iklim Terhadap Debit Andalan Sungai Krueng Aceh

2016 ◽  
Vol 9 (1) ◽  
pp. 50-61
Author(s):  
Teuku Ferijal ◽  
Mustafril Mustafril ◽  
Dewi Sri Jayanti
Keyword(s):  
Climate Changes ◽  
River Flow ◽  
Daily Precipitation ◽  
Balance Model ◽  
Monthly Precipitation ◽  
Variable Infiltration Capacity ◽  
Dry Period ◽  
Infiltration Capacity ◽  
Hydrological Data ◽  
Daily Streamflow

Abstrak. Perubahan iklim yang menyebabkan perubahan karakteristik curah hujan berdampak pada aliran sungai. Penelitian ini bertujuan untuk menganalisa dampak perubahan iklim terhadap debit andalan. Data-data yang digunakan dalam penelitian ini adalah data klimatologi dan hidrologi yang semuanya dikumpulkan dari stasiun-stasiun yang ada dalam wilayah penelitian yaitu DAS Krueng Aceh. Model kesetimbangan air variable infiltration capacity digunakan dalam penelitian ini untuk menghitung debit sungai harian berdasarkan data curah hujan dan evapotranspirasi harian. Hasil analisa menunjukkan bahwa suhu udara tahunan rata-rata DAS Krueng Aceh telah mengalami peningkatan yang drastis sebesar 0,6°C sejak tahun 2001. Perubahan tersebut juga diikuti dengan adanya tren peningkatan curah hujan (22%) pada bulan-bulan basah (November-Januari) serta penurunan curah hujan (26%) pada bulan-bulan kering (Mei-Agustus). Dampak dari perubahan iklim tersebut adalah terjadinya penurunan debit sungai Krueng Aceh yang ditandai semakin meningkatnya kemungkinan debit aliran lebih kecil dari 18,77 m3/s dan menurunkan debit andalan terutama pada periode April-Desember sebesar 23,5%.  Impact of Climate Change on Dependable Discharge in the Krueng Aceh River Abstract. Climate changes altering precipitation characteristic bring impact on streamflow. This research aims to analyze impact of climate changes on dependable discharge. Climatological and hydrological data were collected from stations within Krueng Aceh Watershed. Variable infiltration capacity water balance model was applied to calculate daily streamflow base on daily precipitation and evapotranspiration. The results suggested that annual air temperature of Krueng Aceh Watershed has been squally increasing 0.6°C since 2001. The changes were also detected on monthly precipitation i.e. a 22% increase in wet period (November-January) and a 26% decrease in dry period (Mei-August). The changes have impacted the Krueng Aceh River flow by increasing possibility of flow lower than 18.77m3/s and decreasing dependable discharge by 23.5% for period of April-December.

Are Shocks Asymmetric to Volatility of Chinese Stock Markets?

2004 ◽  
Vol 07 (03) ◽  
pp. 379-395 ◽  
Author(s):  
Wei-Chiao Huang ◽  
Yuanlei Zhu
Keyword(s):  
Stock Returns ◽  
Stock Markets ◽  
Garch Model ◽  
Leverage Effect ◽  
Arch Models ◽  
Before And After ◽  
Chinese Markets ◽  
Chinese Stock Markets ◽  
Better Than ◽  
A Share

This paper uses ARCH models to examine if there is a leverage effect and also to test if A- and B-share holdings have different risks in Chinese stock markets before and after B-share markets open to domestic investors in February 2001. The empirical results suggest that leverage effect was not present and shocks have symmetric impact on the volatility of Chinese B-share stock returns in both periods and A-share returns in Period I. Thus GARCH model would be a better model to fit the Chinese B-share stock returns than EGARCH or GJR-GARCH model. But EGARCH or GJR-GARCH model fits recent (Period II) A-share markets data better than GARCH model. Another finding of this paper is that holding A- or B-share bears different risk in returns in the two Chinese markets. Furthermore, news or shocks have a larger impact on volatility of B-share returns in Period I than in Period II.

Modeling Knowledge Employee’s Turnover Based on P-SVM

2010 ◽  
Vol 121-122 ◽  
pp. 825-831
Author(s):  
Yong Zhao ◽  
Ye Zheng Liu
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Model Development ◽  
Forecast Model ◽  
Support Vector ◽  
Multi Criteria Decision Making ◽  
Parameters Selection ◽  
Vector Machines ◽  
Decision Making Problem ◽  
Simulation Results

Knowledge employee’s turnover forecast is a multi-criteria decision-making problem involving various factors. In order to forecast accurately turnover of knowledge employees, the potential support vector machines(P-SVM) is introduced to develop a turnover forecast model. In the model development, a chaos algorithm and a genetic algorithm (GA) are employed to optimize P-SVM parameters selection. The simulation results show that the model based on potential support vector machine with chaos not only has much stronger generalization ability but also has the ability of feature selection.

scholarly journals Forecasting hydrologic parameters using linear and nonlinear stochastic models

2019 ◽  
Vol 11 (4) ◽  
pp. 1284-1301
Author(s):  
Hamed Nozari ◽  
Fateme Tavakoli
Keyword(s):  
Support Vector Machine ◽  
Moving Average ◽  
Sustainable Use ◽  
Rain Gauge ◽  
Support Vector ◽  
Autoregressive Moving Average ◽  
Monthly Precipitation ◽  
Precipitation Prediction ◽  
Armax Model ◽  
Monthly Discharge

Abstract One of the most important bases in the management of catchments and sustainable use of water resources is the prediction of hydrological parameters. In this study, support vector machine (SVM), support vector machine combined with wavelet transform (W-SVM), autoregressive moving average with exogenous variable (ARMAX) model, and autoregressive integrated moving average (ARIMA) models were used to predict monthly values of precipitation, discharge, and evaporation. For this purpose, the monthly time series of rain-gauge, hydrometric, and evaporation-gauge stations located in the catchment area of Hamedan during a 25-year period (1991–2015) were used. Out of this statistical period, 17 years (1991–2007), 4 years (2008–2011), and 4 years (2012–2015) were used for training, calibration, and validation of the models, respectively. The results showed that the ARIMA, SVM, ARMAX, and W-SVM ranked from first to fourth in the monthly precipitation prediction and SVM, ARIMA, ARMAX, and W-SVM were ranked from first to fourth in the monthly discharge and monthly evaporation prediction. It can be said that the SVM has fewer adjustable parameters than other models. Thus, the model is able to predict hydrological changes with greater ease and in less time, because of which it is preferred to other methods.

scholarly journals Predicting Benzene Concentration Using Machine Learning and Time Series Algorithms

Mathematics ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 2205
Author(s):  
Luis Alfonso Menéndez García ◽  
Fernando Sánchez Lasheras ◽  
Paulino José García Nieto ◽  
Laura Álvarez de Prado ◽  
Antonio Bernardo Sánchez
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Moving Average ◽  
Environmental Pollutants ◽  
Multivariate Adaptive Regression Splines ◽  
Support Vector ◽  
Learning Models ◽  
Vector Autoregressive ◽  
Benzene Concentration ◽  
Machine Learning Models

Benzene is a pollutant which is very harmful to our health, so models are necessary to predict its concentration and relationship with other air pollutants. The data collected by eight stations in Madrid (Spain) over nine years were analyzed using the following regression-based machine learning models: multivariate linear regression (MLR), multivariate adaptive regression splines (MARS), multilayer perceptron neural network (MLP), support vector machines (SVM), autoregressive integrated moving-average (ARIMA) and vector autoregressive moving-average (VARMA) models. Benzene concentration predictions were made from the concentration of four environmental pollutants: nitrogen dioxide (NO2), nitrogen oxides (NOx), particulate matter (PM10) and toluene (C7H8), and the performance measures of the model were studied from the proposed models. In general, regression-based machine learning models are more effective at predicting than time series models.

Pola Arus Permukaan dan Kondisi Fisika Perairan di Sekitar Pulau Selayar pada Musim Peralihan 1 dan Musim Timur

2017 ◽  
Vol 2 (1) ◽  
pp. 83 ◽  
Author(s):  
Ahmad Bayhaqi ◽  
Mochamad Riza Iskandar ◽  
Dewi Surinati
Keyword(s):  
Surface Water ◽  
Average Velocity ◽  
River Flow ◽  
Climatic Factors ◽  
Monsoon Season ◽  
Surface Current ◽  
Current Pattern ◽  
Flow Discharge ◽  
Oceanographic Data ◽  
Mean Current

<strong>Surface Current Pattern and Physics Condition of Waters Around Selayar Island in the First Transitional and Southeast Monsoons. </strong> Seasonal observations of the flow of surface water and physics conditions around Selayar Island adjacent to Arlindo throughflow pathways of Makassar Strait have been conducted with a focus on the first transitional season and the southeast monsoon season. The purpose of this research is to obtain the pattern of seasonal surface current and physics characteristics of water column, i.e. temperature and salinity in Selayar Island waters during those seasons. The observations conducted on 22–27 May 2015 and 7–10 August 2015 illustrated the successive periods of the first transitional season and the southeast monsoon season. The methods used for taking oceanographic data such as temperature, salinity, and current were the stationary oceanographic measurement using CTD and currentmeter at 29 stations located in surrounding waters of Selayar Island. The surface current pattern generated from the interpolation process of the overall observation stations indicated that during the first transitional season the current moved eastward with an average velocity of 0.25 m/s. During the southeast monsoon season, the same pattern was still observed with a slightly higher average velocity of 0.26 m/s. The temperatures and salinity of Selayar Island waters during the southeast monsoon season were 2°C lower and 0.5 psu higher than during the first transitional season. Differences in mean current velocity values tended to be more affected by local tidal conditions. Different salinity was thought to be influenced by upwelling phenomena and local climatic factors such as rainfall, wind, and river flow discharge.

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