Peramalan Curah Hujan Provinsi Bengkulu dengan Generalized Space-Time Autoregressive

Fransiska, Herlin; Sunandi, Etis; Agustina, Dian

doi:10.30651/must.v5i2.5326

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…Lin et al (2019) said that the negative correlation is influenced by the far distancewhere ENSO is in the Pacific Ocean, and other factors, such as Walker Circulation Anomaly and Surface Anomaly centered in Philippines. The rainfall in Bengkulu City also can be influenced by a local factor, for instance, geographical conditions among different areas (Fransiska et al, 2020). Based on the correlation data for 35 years, Bengkulu City's rainfall is strongly influenced by the La Nina and El Nino phenomena.…”

Section: Humiditymentioning

confidence: 99%

Correlation Among Rainfall, Humidity, and The El Niño-Southern Oscillation (ENSO) Phenomena in Bengkulu City During the Period from 1985-2020

Wardani

Akbar

Handayani³

et al. 2023

jppipa, pendidikan ipa, fisika, biologi, kimia

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A correlation analysis is needed to see the relations among the three variables, rainfall with El Niño-Southern Oscillation (ENSO) and Humidity with ENSO. The instability of rainfall and the decrease in the humidity in Bengkulu City can be affected by La Nina and El Nino phenomena. This research aimed to analyze the correlations among rainfall, humidity, and ENSO with Pearson correlation analysis. The rainfall and humidity data were collected from BMKG, and the ENSO data were obtained from NOAA PSL for the 1985-2020 period. The results of the correlation analysis of rainfall and ENSO for the last 35 years generally show a moderately strong correlation. However, a strong correlation in the La Nina phase specifically occurred in 1988-1989, 1998-1999, 1999-2000, 2007-2008 and 2010-2011 with values of 58.6, 40.1, 61.4, 96, 4, and 89.2%. Meanwhile, the correlation >80% (very strong) during the El Nino phase occurred in 1987, 1991-1992, 1997, 2015. The correlation analysis results of humidity and ENSO show little correlation in the La Nina phase of >70% (1988-1989, 1998-1999, 2010-2011) and the El Nino phase of <70% (1997, 2015, 2016). The humidity correlations with ENSO do not influence the humidity change in Bengkulu City. The decrease in the humidity in Bengkulu City can be affected by other factors, such as land use change to housing due to population growth in Bengkulu City.

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Section: Humiditymentioning

confidence: 99%

Correlation Among Rainfall, Humidity, and The El Niño-Southern Oscillation (ENSO) Phenomena in Bengkulu City During the Period from 1985-2020

Wardani

Akbar

Handayani³

et al. 2023

jppipa, pendidikan ipa, fisika, biologi, kimia

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“…Climate change is a significant change in weather patterns (temperature in the atmosphere and at sea, rainfall, wind patterns, and other variables) throughout tens to hundreds of years in regional and global scope. High rainfall is very dangerous because it hurts people's life activities, such as floods, landslides, and other natural disasters that break various sectors such as agriculture, tourism, economy, and transportation [5]. Bengkulu Province's location means that the weather changes quickly, with heavy rains and highly damp conditions.…”

Section: Introductionmentioning

confidence: 99%

Time Series Clustering Analysis Using Dynamic Time Warping Technique of Daily Rainfall in Bengkulu Province

Fransiska,

Agustina,

Setyorini

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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Climate change is an important issue. The Meteorology, Climatology, and Geophysics Agency indicates that the state of climate change is critical. Climate change, such as high rainfall, can hurt people’s daily lives, causing floods, landslides, and other disasters. As a result, this researcher will use the time series clustering approach to analyze the mapping of rainfall in Bengkulu Province using data from 2001-2020. This work uses Dynamic Time Warping (DTW) and Euclidean to determine the similarity. The best is DTW. The results obtained show that in Bengkulu province, there are 2 clusters. The members of Cluster 1 have one rain station, and Cluster 2 has 14 rain stations. Cluster 1 is included in the low rainfall category. Cluster 2 is a cluster with a high rainfall category.

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GLS estimation in python to forecast gross regional domestic product using generalized space–time autoregressive seemingly unrelated regression model

Rismawati Arum,

Fathoni Amri,

Amri

2024

Front. Appl. Math. Stat.

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Economic growth is essential for regional economic performance, with gross regional domestic product (GRDP) being a key indicator of economic development over time. In this research case, the GRDP data of various provinces on Java Island from 2010 to 2023 will be used as the variable being studied. The data obtained from the GRDP variable contain spatial and temporal information, requiring an appropriate model to forecast spatiotemporal data, namely, the Generalized Space–Time Autoregressive (GSTAR) model. However, in estimating the parameters, the GSTAR model is unable to detect correlated residuals between equations, resulting in inefficient estimators. Therefore, an appropriate estimation method is needed to address correlated residuals within the seemingly unrelated regression (SUR) framework, namely, the Generalized Least Square (GLS) estimation method. The GSTAR-SUR method is applied to forecast the economic growth rate of Java Island. The optimal model, GSTAR-SUR (11)-I(1) with inverse distance location weights, demonstrates high accuracy with a mean absolute percentage error (MAPE) of 8.451%. Forecasts for Banten, DKI Jakarta, West Java, Central Java, East Java, and DI Yogyakarta predict consistent monthly GRDP increases through December 2024.

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Peramalan Curah Hujan Provinsi Bengkulu dengan Generalized Space-Time Autoregressive

Cited by 3 publications

References 3 publications

Correlation Among Rainfall, Humidity, and The El Niño-Southern Oscillation (ENSO) Phenomena in Bengkulu City During the Period from 1985-2020

Correlation Among Rainfall, Humidity, and The El Niño-Southern Oscillation (ENSO) Phenomena in Bengkulu City During the Period from 1985-2020

Time Series Clustering Analysis Using Dynamic Time Warping Technique of Daily Rainfall in Bengkulu Province

GLS estimation in python to forecast gross regional domestic product using generalized space–time autoregressive seemingly unrelated regression model

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