Rainfall Forecasting Model Using ARIMA and Kalman Filter in Makassar, Indonesia

Sukarna, Sukarna; Ananda, Elma Yulia Putri; Wahyuni, Maya Sari

doi:10.1088/1742-6596/2123/1/012044

Cited by 4 publications

(4 citation statements)

References 7 publications

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“…The combination of the ARIMA method and the Kalman Filter is novel in this study, which aims to reduce estimation error compared to the ground truth. Of course, some previous studies have used the ARIMA method in conjunction with the Kalman Filter, but not to develop IAQIs, as demonstrated in research on rainfall forecasting [20].…”

Section: A Principle Of Indoor Air Quality Indexmentioning

confidence: 99%

Application of ARIMA Kalman Filter with Multi-Sensor Data Fusion Fuzzy Logic to Improve Indoor Air Quality Index Estimation

Erfianto

Rahmatsyah²

2022

JOIV : Int. J. Inform. Visualization

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Air quality monitoring is a process that determines the number of pollutants in the air, one of which is indoor air quality. The Fuzzy Indoor Air Quality Index was developed in this research. It is a method for determining the indoor air quality index using sensor fusion and fuzzy logic. By combining several different time series determinants of air quality, a fuzzy logic-based sensor fusion method is used to build a knowledge base about indoor air quality levels. Without the use of complicated calculation models, fuzzy logic-based fusion will make it easier to determine indoor air quality levels based on various sensor parameters. The input for fuzzy-based data fusion is obtained from the ARIMA method with Kalman Filter's air quality parameter values estimation. The application of ARIMA with a Kalman Filter was used to improve the accuracy of indoor air quality estimation in this study. ARIMA(3,1,3) had a MAPE of 0.1 percent on the CO2 dataset, and ARIMA(1,0,1) had a MAPE of 0.63 percent on the TVOC dataset based on approximately three experimental days. ARIMA (3,1,3) estimation with a Kalman Filter results in a MAPE of 0.03 percent for the CO2 dataset and a MAPE of 0.24 percent for ARIMA(1,0,1) Kalman Filter estimation on TVOC dataset. As a result, the Fuzzy Indoor Air Quality Index (FIAQI) developed in this research reasonably estimates indoor air quality. This can be seen by examining the percentage of estimation errors obtained from the experiment.

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Section: A Principle Of Indoor Air Quality Indexmentioning

confidence: 99%

Application of ARIMA Kalman Filter with Multi-Sensor Data Fusion Fuzzy Logic to Improve Indoor Air Quality Index Estimation

Erfianto

Rahmatsyah²

2022

JOIV : Int. J. Inform. Visualization

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“…Oleh karena itu, diperlukan suatu peramalan yang dapat memberikan informasi atau gambaran pada proses jumlah kunjungan wisatawan mancanegara. Model yang berkaitan dengan ramalan biasanya menggunakan model Autoregressive Integrated Moving Average (ARIMA) (Aswi & Sukarna, 2017;Maulana, 2018;Sukarna, Ananda, & Wahyuni, 2021), namun penelitian ini menggunakan model Autoregressive Fractionally Integrated Moving Average (ARFIMA). ARFIMA merupakan model ARIMA yang nilai integrated d-nya adalah pecahan.…”

Section: Pendahuluanunclassified

Peramalan Jumlah Kedatangan Wisatawan Mancanegara di Sulawesi Selatan Menggunakan Model ARFIMA

Sukarna,

Abdy,

Aswi

et al. 2022

JMathCoS

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Pariwisata dianggap sebagai suatu aset yang strategis untuk mendorong pembangunan pada wilayah-wilayah tertentu yang mempunyai potensi objek wisata. Faktor-faktor yang mempengaruhi wisatawan mancanegara berkunjung ke suatu wilayah negara, diantaranya nilai tukar mata uang, inflasi disuatu wilayah kunjungan wisatawan, dan letak geografis suatu wilayah negara. Peningkatan yang tidak terduga pada jumlah kunjungan wisatawan ini dapat berdampak kesulitan bagi para pelaku wisatawan dalam hal memberikan pelayanan terbaik dan sebaliknya jika terjadi penurunan jumlah kunjungan wisatawan hal yang dikhawatirkan akan terjadi pengangguran.Oleh karena itu, diperlukan suatu peramalan yang dapat memberikan informasi atau gambaran pada proses jumlah kunjungan wisatawan mancanegara. Dalam analisis runtun waktu terdapat data yang memiliki ciri proses jangka pendek dan data yang memiliki ciri proses jangka panjang. Model yang dapat menangani kedua jenis data ini adalah model autoregressive fractionally integrated moving average (ARFIMA). Model ARFIMA merupakan pengembangan dari model ARIMA, dengan differencing bernilai pecahan. Penelitian ini bertujuan untuk menentukan model ARFIMA pada peramalan jumlah kunjungan wisatawan mancanegara Sulawesi Selatan di masa yang akan datang. Pada penelitian ini, nilai AIC antara ARFIMA([1,8],d,0) dengan d ̂_gph=0,02 dan ARFIMA(0,d,1) dengan d ̂_(R/S)=0,12 relatif sama, hasil komparasi dengan model ARIMA memberikan hasil bahwa tidak diperoleh nilai ARIMA yang sesuai sehingga penulis menggunakan model ARFIMA untuk peramalan dan hasil peramalan jumlah kedatangan wisatawan mancanegara Sulawesi Selatan dapat dilihat dengan perbandingan data out sample.Keywords: Parawisata, peramalan, ARFIMA.

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“…Geographically, the city of Makassar, which is the capital of the province of South Sulawesi, is located between 119 °24'17" east longitude and 5 °8'6" south latitude [1]- [3]. Makassar city is located on the equator, which results in Makassar having a tropical climate with an average air temperature of 26.2℃ − 29.3℃ with a humidity of 77 percent and an average wind speed of 5.2 knots [1]. Therefore, the rainfall in Makassar is quite varied throughout the year.…”

Section: Introductionmentioning

confidence: 99%

Quantile Regression Model on Rainfall in Makassar 2019

Sanusi

Sukarna

Harisahani

2023

BAREKENG: J. Math. & App.

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Makassar is an area that has a monsoon rainfall pattern. This study aims to find a quantile regression model and to determine the factors that significantly influence rainfall in the city of Makassar. This applied research applies a quantile regression model to rainfall data which is seasonal data. The advantage of this quantile regression model is that it is able to detect extreme conditions of rainfall, such as heavy rain. The data used is daily data in 2019. The estimation results obtained 9 (nine) models from each quantile used. The best model is obtained based on the largest coefficient of determination ( ), namely the 0,8th quantile ( ) of 0.28%. Furthermore, based on the model, it is found that the factor that significantly influences rainfall in the city of Makassar is humidity. At the same time, the air temperature and wind speed have no significant effect on rainfall in the city of Makassar.

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Rainfall Forecasting Model Using ARIMA and Kalman Filter in Makassar, Indonesia

Cited by 4 publications

References 7 publications

Application of ARIMA Kalman Filter with Multi-Sensor Data Fusion Fuzzy Logic to Improve Indoor Air Quality Index Estimation

Application of ARIMA Kalman Filter with Multi-Sensor Data Fusion Fuzzy Logic to Improve Indoor Air Quality Index Estimation

Peramalan Jumlah Kedatangan Wisatawan Mancanegara di Sulawesi Selatan Menggunakan Model ARFIMA

Quantile Regression Model on Rainfall in Makassar 2019

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