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Application of Vector Auto Regression Model for Rainfall-River Discharge Analysis Hartini, Sri; Hadi, Muhammad Pramono; Sudibyakto, Sudibyakto; Poniman, Aris
Forum Geografi Vol 29, No 1 (2015): Forum Geografi
Publisher : Forum Geografi

Show Abstract | Download Original | Original Source | Check in Google Scholar

River discharge quantity is highly depended on rainfall and initial condition of river discharge; hence, the river discharge has auto-correlation relationships. This study used Vector Auto Regression (VAR) model for analysing the relationship between rainfall and river discharge variables. VAR model was selected by considering the nature of the relationship between rainfall and river discharge as well as the types of rainfall and discharge data, which are in form of time series data. This research was conducted by using daily rainfall and river discharge data obtained from three weirs, namely Sojomerto and Juwero, in Kendal Regency and Glapan in Demak Regency, Central Java Province. Result of the causality tests shows significant relationship of both variables, those on the influence of rainfall to river discharge as well as the influence of river discharge to rainfall variables. The significance relationships of river discharge to rainfall indicate that the rainfall in this area has moved downstream. In addition, the form of VAR model could explain the variety of the relationships ranging between 6.4% - 70.1%. These analyses could be improved by using rainfall and river discharge time series data measured in shorter time interval but in longer period.

Application of Vector Auto Regression Model for Rainfall-River Discharge Analysis Hartini, Sri; Hadi, Muhammad Pramono; Sudibyakto, S; Poniman, Aris
Forum Geografi Vol 29, No 1 (2015): July 2015
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/forgeo.v29i1.786

River discharge quantity is highly depended on rainfall and initial condition of river discharge; hence, the river discharge has auto-correlation relationships. This study used Vector Auto Regression (VAR) model for analysing the relationship between rainfall and river discharge variables. VAR model was selected by considering the nature of the relationship between rainfall and river discharge as well as the types of rainfall and discharge data, which are in form of time series data. This research was conducted by using daily rainfall and river discharge data obtained from three weirs, namely Sojomerto and Juwero, in Kendal Regency and Glapan in Demak Regency, Central Java Province. Result of the causality tests shows significant relationship of both variables, those on the influence of rainfall to river discharge as well as the influence of river discharge to rainfall variables. The significance relationships of river discharge to rainfall indicate that the rainfall in this area has moved downstream. In addition, the form of VAR model could explain the variety of the relationships ranging between 6.4% - 70.1%. These analyses could be improved by using rainfall and river discharge time series data measured in shorter time interval but in longer period.

Distribution of Accuracy of TRMM Daily Rainfall in Makassar Strait Giarno, G; Hadi, Muhammad Pramono; Suprayogi, Slamet; Murti, Sigit Heru
Forum Geografi Vol 32, No 1 (2018): July 2018
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/forgeo.v32i1.5774

This research aims to evaluate rainfall estimates of satellite products in regions that have high variations of rainfall pattern. The surrounding area of Makassar Strait have chosen because of its distinctive rainfall pattern between the eastern and western parts of the Makassar Strait. For this purpose, spatial distribution of Pearsonâs coefficient correlation and Root Mean Square Error (RMSE) is used to evaluate accuracy of rainfall in the eastern part of Kalimantan Island and the western part of Sulawesi Island. Moreover, we also used the contingency table to complete the parameter accuracy of the TRMM rainfall estimates. The results show that the performance of TRMM rainfall estimates varies depending on space and time. Overall, the coefficient correlation between TRMM and rain observed from no correlation was -0.06 and 0.78 from strong correlation. The best correlation is on the eastern coast of South West Sulawesi located in line with the Java Sea. While, no variation in the correlation was related to flatland such as Kalimantan Island. On the other hand, in the mountain region, the correlation of TRMM rainfall estimates and observed rainfall tend to decrease. The RMSE distribution in this region depends on the accumulation of daily rainfall. RMSE tends to be high where there are higher fluctuations of fluctuating rainfall in a location. From contingency indicators, we found that the TRMM rainfall estimates were overestimate. Generally, the absence of rainfall during the dry season contributes to improving TRMM rainfall estimates by raising accuracy (ACC) in the contingency table.

SUITABLE PROPORTION SAMPLE OF HOLDOUT VALIDATION FOR SPATIAL RAINFALL INTERPOLATION IN SURROUNDING THE MAKASSAR STRAIT Giarno, Giarno; Hadi, Muhammad Pramono; Suprayogi, Slamet; Murti, Sigit Heru
Forum Geografi Vol 33, No 2 (2019): December 2019
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/forgeo.v33i2.8351

Spatial rainfall interpolation requires a number of suitable validation samples to maintain accuracy. Generally, the larger the areas which can be predicted, the better the interpolation. In addition, the data used for validation should be separated from the modelling data. Moreover, the number of samples determine optimally proportion the independent sites. The objective of this study is to determine the optimal sample ratio for holdout validation in interpolation methods; the Makassar Strait was chosen as the study location because of its daily rainfall variation. The accuracy of the sample selection is tested using correlation, root mean square error (RMSE), mean absolute error (MAE) and the indicators of contingency tables. The results show that accuracy depends on the ratio of the modelling data. Therefore, the more extensive the data used for interpolation, the better the accuracy. Otherwise, if the rain gauge data is separated according to province, there will be a variation in accuracy in the portion of independent samples. For rainfall interpolation, it is recommended to use a minimum 75% of data sites to maintain accuracy. Comparison between kriging and inverse distance weighting or IDW methods indicates that IDW is better. Moreover, rainfall characteristics affect the accuracy and portion of the independent sample.

Mitigasi Risiko Penyebaran Virus Covid-19 di Stasiun Kereta Api Hadi, Muhammad Pramono; Putra, Ika; Widyastuti, Dyah Titisari; Nugroho, Deni Prasetio; Putro, Arsito Bayu Pramono; Sasmito, Dindi Eneng Chandraning; Nurdjanah, Nunuj
Warta Penelitian Perhubungan Vol. 34 No. 2 (2022): Warta Penelitian Perhubungan
Publisher : Sekretariat Badan Penelitian dan Pengembangan Perhubungan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25104/warlit.v34i2.2113

Pergerakan orang antarwilayah dapat mempengaruhi penyebaran virus dalam wilayah tersebut. Status zona pandemi di wilayah menjadi salah satu faktor analisis risiko stasiun sebagai klaster penularan dan risiko penumpang tertular COVID-19 di kereta. Tujuan penelitian ini adalah untuk mengetahui tingkat risiko penyebaran virus ketika menggunakan perjalanan dengan moda kereta api dan kebijakan yang dapat dilakukan untuk mengurangi risiko penyebaran virus di stasiun kereta api. Metode penelitian yang digunakan adalah metode pengukuran risiko yaitu pengukuran potensi penyebaran COVID-19 di stasiun Kereta Api. Metode ini dilakukan dengan mengukur tingkat risiko penumpang Kereta Api tertular virus COVID-19. Metode pengukuran konsep risiko terkait dengan fenomena COVID-19 menggunakan pendekatan persamaan risiko dengan parameter Bahaya, Kerentanan, dan Kapasitas. Penelitian ini menggunakan studi kasus Stasiun Tugu Yogyakarta yang pada saat sebelum pandemi melayani sekitar 1.219 penumpang per jam sibuk. Pada saat pandemi, jumlah rata-rata penumpang per jam sibuk adalah sekitar 189 penumpang dengan berbagai kebijakan pembatasan yang dilakukan. Intervensi yang dilakukan dengan membatasi kerentanan orang (pembatasan jumlah penumpang) dan kerentanan ruang (penerapan pemanfaatan ruang agar lebih terbuka, tidak menumpuk, dan mengurangi kontak) serta meningkatkan kapasitas dengan penerapan protokol kebijakan kesehatan akan mampu mengurangi potensi risiko penyebaran virus COVID-19. Hasil penelitian menunjukkan bahwa status wilayah merah memiliki risiko dua kali lipat dari status oranye. Status wilayah merah diharapkan menjadi dasar mitigasi kebijakan pembatasan jumlah penumpang yang diperbolehkan naik, peningkatan protokol COVID-19 yang semakin ketat, seperti kebijakan bagi penumpang untuk diwajibkan swab atau tidak, penggunaan alat pendeteksi awal COVID-19, dan pemisahan kereta bagi penumpang yang berasal dari stasiun wilayah berstatus merah. Kebijakan pengurangan jumlah penumpang pada tiap perjalanan kereta mencapai 50% dari jumlah maksimal penumpang sudah sesuai dengan Kajian Manajemen Risiko dalam studi ini karena akan mengurangi risiko lebih dari sampai 75% dibandingkan dengan jumlah penumpang maksimal

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