Copula-based model for rainfall and El- Niño in Banyuwangi Indonesia

Caraka, Rezzy Eko; Supari,; Tahmid, Muhammad

doi:10.1088/1742-6596/1008/1/012025

Cited by 9 publications

(5 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Numbers of techniques are used in climatological analysis such as copula used for pattern analysis of El Nino and La Nina [6,19]. Application of short memory and long memory method for rainfall prediction and temperature [20] using of spline method, wavelet, and traditional time series.…”

Section: Neuro Computingmentioning

confidence: 99%

“…Rainfall is very closely related to the various sectors of human activities such as agriculture, forestry, fisheries, or even in ecological and biodiversity [3][4][5] El Niño begins with rainy season withdrawal and the beginning of the dry season forward and the length of the rainy season occurs shorter while the dry season is more extended. La Niña begins with the rainy season happening faster and the beginning of the dry season backward and the length of the rainy season occurs longer term shorter dry season [6,7]. However, Statistics has an essential role in explaining the phenomenon and information on the problems that exist around.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Neurocomputing fundamental climate analysis

Caraka

Bakar

Tahmid³

et al. 2019

TELKOMNIKA

View full text Add to dashboard Cite

Rainfall is a natural phenomenon that needs to be studied more deeply and interesting to be analyzed. It involves numbers of human activities such as aviation, agriculture, fisheries, and also disaster risk reduction. Moreover, the characteristics of rainfall data follows seasonality, fluctuation, not normally distributed and it makes traditional time series challenging to use. Therefore, neurocomputing model can be used as an alternative to extraction information from rainfall data and give high performance also accuracy. In this paper, we give short preview about SST Anomalies in Manado, Northern Sulawesi and at the same time comparing the performance of rainfall forecasting by using three types of neurocomputing methods such as Generalized Regression Neural Network (GRNN), Feed forward Neural Network (FFNN), and Localized Multi Kernel Support Vector Regression (LMKSVR). In a nutshell, all of neurocomputing methods give highly accurate forecasting as well as reach low MAPE FFNN 1.65%, GRNN 2.65% and LMKSVR 0.28%, respectively.

show abstract

Section: Neuro Computingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Neurocomputing fundamental climate analysis

Caraka

Bakar

Tahmid³

et al. 2019

TELKOMNIKA

View full text Add to dashboard Cite

show abstract

“…The copula is used to analyse the dependence of random variables in structures described by the combined function (Caraka, Yasin, Sugiyarto, Sugiarto, & Ismail, 2016) and (Caraka, Supari, & Tahmid, 2018). Here is Gaussian copula for two variables, which can be easily extended to multivariable.…”

Section: The Step Constructionmentioning

confidence: 99%

The Step Construction of Copula Gaussian Multivariate and AR(1)-N.GARCH(1,1) Models

Caraka

Yasin

Toharudin

2018

Proceedings of the Joint Workshop KO2PI and the 1st International Conference on Advance &Amp; Scientific Innovation

View full text Add to dashboard Cite

Abstract. Copulas are a powerful tool in multivariate statistics. If copula functions used for modeling dependence between random variables, there is an immediate and obvious need to test whether the model can describe the data at hand accurately enough or not. Copulas involve several underlying functions: the marginal cumulative distribution functions (CDF) and a joint CDF. To estimating the copula functions, first issue consists in specifying how to estimate the margins separately. Moreover, some of these functions can be fully known. Depending on the assumptions made, some quantities have to be estimated parametric, semiparametric or even non-parametric. In this paper, we propose the step of parameter estimation in modeling Copula Gaussian Multivariate and AR (1)-N. GARCH (1,1) Models

show abstract

“…In analyzing, the relationship is between climatological variables in VAR [31]. It is not necessary to distinguish between endogenous and exogenous variables, which means that all variables used in VAR are used as endogenous variables.…”

Section: Introductionmentioning

confidence: 99%

“…IV. ANALYSIS A. PROBABILITY TRANSACTION MARKOV CHAINThe first step is to calculate the probability transition PM 2.5 data, which is classified by No Risk, Medium Risk(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48), and Moderate (> 49). In this paper, we are using monthly data from January 2014 to May 2019 in 2 Taiwanese locations, Pingtung and Chaozhou.…”

mentioning

confidence: 99%

Prediction of Status Particulate Matter 2.5 Using State Markov Chain Stochastic Process and HYBRID VAR-NN-PSO

et al. 2019

View full text Add to dashboard Cite

Air pollution is the entry or inclusion of living things, energy substances, and other components into the air. Moreover, Air pollution is the presence of one or several contaminants in the outside atmospheric air such as dust, foam, gas, fog, smoke or steam in large quantities with various properties and time intervals of the contaminants in the air resulting in disturbances to the lives of humans, plants or animals. One of the parameters measured in determining air quality is PM 2.5. However, PM 2.5 has a higher probability of being able to enter the lower respiratory tract because small particle diameters can potentially pass through the lower respiratory tract. In this paper, we will get two different insight. First, the probability of status change using Markov chain and second, forecasting by using VAR-NN-PSO. More details we classify by three classifications no risk (1-30), medium risk (30-48), and moderate (>49) in Pingtung and Chaozhou. This data is starting from January 2014 to May 2019 and it can be modeled with the Markov chain. At the same time, we perform Hybrid VAR-NN-PSO to forecast PM 2.5 in Pingtung and Chaozhou. In this optimization, the search for best solutions is carried out by a population consisting of several particles. Based on the results of the discussion, opportunities for the transition from monthly status change are obtained continuous stochastic time with a stationary probability distribution. Regarding the VAR-NN-PSO, we obtained the mean absolute percentage error (MAPE) 3.57% for PM 2.5 data in Pingtung and 4.87% for PM 2.5 data in Chaozhou, respectively. This model can be predicted to forecasting 180 days ahead. Besides, the population in PSO has generated randomly with the smallest value and the high value the accuracy.

show abstract

Copula-based model for rainfall and El- Niño in Banyuwangi Indonesia

Cited by 9 publications

References 6 publications

Neurocomputing fundamental climate analysis

Neurocomputing fundamental climate analysis

The Step Construction of Copula Gaussian Multivariate and AR(1)-N.GARCH(1,1) Models

Prediction of Status Particulate Matter 2.5 Using State Markov Chain Stochastic Process and HYBRID VAR-NN-PSO

Contact Info

Product

Resources

About