Menentukan Metode Interpolasi Spasial Curah Hujan Bulanan Terbaik Di Jawa Timur

Kurniawan, Andang; Makmur, Erwin Eka Syahputra; Supari, Supari

doi:10.24895/sng.2020.0-0.1142

Cited by 9 publications

(9 citation statements)

References 9 publications

(9 reference statements)

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“…In this case, Inverse Distance Weighting (IDW) is used due to its superiority compared to Spline [9]. IDW is proven as the best interpolation in East Java [10]. The parameter used in IDW is the nearest neighbour values.…”

Section: Methodsmentioning

confidence: 99%

IMPORTANCE OF UPDATING FOR MONTHLY RAINFALL PREDICTION BASED ON ECMWFs4

Rafi¹

2022

JMG

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<em>There is uniqueness in climate services in East Java. Rainfall information is delivered as N-1 analysis for ongoing months N+1, N+2, and N+3 for monthly prediction. This study aims to investigate whether updating monthly predictions improves prediction accuracy. The verification method for this study is based on the percentage accuracy of the rain class category according to SNI 8196: 2015. The data used for this study is ECMWF's monthly rainfall prediction that has three lags system (1, 2, and 3). Rasters of monthly rainfall interpolation from the main rainfall observation (197 locations) in East Java from April 2015 to May 2020 (62 months) are used for the verification process. The temporal and spatial analysis then conducted using R (+ package raster). Studies based on the local governmental zone are also used. In general, the result shows that almost all months need updating, except April-September-October. Verification of ECMWFs4 shows a better verification result (0,56) in the past five years (2016-2020) for March. The regions that need monthly updating are Bawean island, the coast of Gresik, Pasuruan, and Banyuwangi</em>

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Section: Methodsmentioning

confidence: 99%

IMPORTANCE OF UPDATING FOR MONTHLY RAINFALL PREDICTION BASED ON ECMWFs4

Rafi¹

2022

JMG

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“…This map is obtained by interpolating daily rainfall data from each BMKG rainfall station using the Inverse Distance Weight (IDW) method. This technique has been proven to have the smallest error and faster computation time compared to other interpolation methods 18 . After generating the rainfall intensity map, a simple linear regression test is performed on the flood events, tweets, and depth at each flood point.…”

Section: Regression Calculation Of Flood Point and Rainfallmentioning

confidence: 97%

Flood mapping using Twitter crowdsourcing data with rainfall data analysis (case study: Jakarta, January 2020)

Malahayati,

Farda,

Berlyanti

2024

Eighth Geoinformation Science Symposium 2023: Geoinformation Science for Sustainable Planet

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Jakarta, the capital of Indonesia, experiences frequent flooding every year. In the event of natural disasters such as floods, the government is obliged to implement a series of disaster emergency responses, including disaster modeling. Disaster modeling can be done using various data sources, but field survey data sources, remote sensing, and aerial photographs are considered less efficient. Field surveys take longer, while remote sensing and aerial photography are limited during the rainy season due to high cloud intensity. Currently, social media, particularly Twitter, is receiving significant attention from various groups as a data source for flood modeling. This research aims to construct a spatial database, conduct flood modeling, assess achieved accuracy levels, and examine tweet data in relation to rain events in Jakarta. The Kernel-Based Flood Mapping Model method is used for flood modeling, which utilizes DEMNAS data, river distribution data, administrative boundaries, water depth data at each river floodgate, tweet data, BPBD flood area maps, and surface observation rainfall data provided by BMKG. The accuracy of the flood modeling results was assessed using the overall accuracy method against all the obtained tweet data and BPBD flood area maps. A regression test was conducted to determine the relationship between rainfall and tweet data related to flooding in Jakarta. The results showed that out of 12,345 tweets, 149 could be compiled into a database for modeling. The flood modeling results indicate an accuracy rate of 70% based on the total flood points and 57% based on BPBD flood zones, which are considered low. The regression test indicates that there is no significant relationship between flood points and January rainfall data. The regression test values for rainfall at the flood location and the number of tweets is 0.020822 and for flood depth is 0.049214. This suggests that the rainfall variable has only a 2% effect on the number of tweets and a 4.9% effect on the depth of the flood, with other factors influencing the remainder.

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“…The calculated values of the erosivity factor are then interpolated using the Inverse Distance Weighting (IDW) method. After conducting various tests, it was found that the IDW method is better than the Spline method for creating R factor maps [9]. The interpolation procedure results in a final output of an R factor layer in raster format, where each closed polygon contains pixel R values with varying R values.…”

Section: Erosion Parameters Of Usle 231 Rainfall Erosivity (R)mentioning

confidence: 99%

Assessment of Land Erosion Hazard in the Sampean Hulu Sub Watershed, Bondowoso Regency

Setyawan,

Nurtjahjaningtyas,

Hidayah

2023

pengairan

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The Sub-watershed of Sampean Hulu, located on the slopes of Mount Raung is susceptible to erosion hazards. Erosion indicators have been observed in the irrigation channels, mainly due to frequent sedimentation, which compromises the irrigation infrastructure's optimization and water distribution. To address this, erosion monitoring using the Universal Soil Loss Erosion (USLE) model, along with GIS and remote sensing techniques, is essential. It was found that the irrigation networks associated with Clangap and Tamanan rain gauges are at higher risk due to intense rainfall. Maesan and Wonosari II rain gauges cover a larger irrigation network area, highlighting the need for erosion prevention measures. The dominant soil type, Tv, with a high clay content, is highly susceptible to erosion. Flat and gently sloping slopes have a lower risk compared to steeper slopes, while very steep and steep slopes pose higher erosion risks. Paddy fields and well-managed forest plantations have lower erosion hazards, while bare land and certain agricultural practices contribute to increased erosion risks. The analysis classified the erosion hazard into five classes, with the sub-watershed being predominantly low and very low susceptible to soil erosion. Implementing conservation practices, sustainable land management, and land use regulations are crucial for erosion prevention.

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Menentukan Metode Interpolasi Spasial Curah Hujan Bulanan Terbaik Di Jawa Timur

Cited by 9 publications

References 9 publications

IMPORTANCE OF UPDATING FOR MONTHLY RAINFALL PREDICTION BASED ON ECMWFs4

IMPORTANCE OF UPDATING FOR MONTHLY RAINFALL PREDICTION BASED ON ECMWFs4

Flood mapping using Twitter crowdsourcing data with rainfall data analysis (case study: Jakarta, January 2020)

Assessment of Land Erosion Hazard in the Sampean Hulu Sub Watershed, Bondowoso Regency

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