Pub Date : 2020-12-07DOI: 10.1109/AGERS51788.2020.9452776
Mega Novetrishka Putri, H. Priyadi, Muhammad Dayuf Jusuf, Syaefudin, R. Amaliyah, A. Subandar, Suryanto
Ogan Komering Ilir Regency is one of the areas that experienced sever forest fires in 2015. The fire occurred because of the El Nino phenomenon which made the weather conditions dry out and prolonged the dry season. Also, it was caused by forest illegal burning and peatlands to open new land for plantations. According to the regional board of disaster management, known as BPBD data of OKI regency, around 316, 697 Ha of forest land were affected by forest fires. This causes material and immaterial losses to the local community. One of the important aspects of disaster mitigation is an assessment of the vulnerability of potential disaster-prone areas. The purposes of this paper is to determine the level of forest fire disaster vulnerability in the OKI regency to assist in disaster mitigation and recommendations for regional spatial development. The vulnerability assessment used in this study is based on the regulation of the head of the Indonesian national board of disaster management No.2 of 2012 concerning General Guidelines for Indonesian Disaster Risk Assessment. The assessment uses 2 parameters: economic and environmental vulnerability. The method used us weight calculation based on each parameter and data is processed using GIS. Based on the analysis result, a quite high fire potential is in: peatlands areas, forest fire-prones areas, and/or peat swamp forest fires, which covers the area of Kayuagung, Sungai Menang, Cengal, Tulung Selapan, Air Sugihan, Pedamaran Timur, Pedamaran, Pampangan, Pangkalan, Lampam, Lempuing, Lempuing Jaya, Mesuji, Mesuji, Makmur and Mesuji Raya District
{"title":"Analysis of Vulnerability Level to the Forest and Land Fire Hazard in Ogan Komering Ilir Regency","authors":"Mega Novetrishka Putri, H. Priyadi, Muhammad Dayuf Jusuf, Syaefudin, R. Amaliyah, A. Subandar, Suryanto","doi":"10.1109/AGERS51788.2020.9452776","DOIUrl":"https://doi.org/10.1109/AGERS51788.2020.9452776","url":null,"abstract":"Ogan Komering Ilir Regency is one of the areas that experienced sever forest fires in 2015. The fire occurred because of the El Nino phenomenon which made the weather conditions dry out and prolonged the dry season. Also, it was caused by forest illegal burning and peatlands to open new land for plantations. According to the regional board of disaster management, known as BPBD data of OKI regency, around 316, 697 Ha of forest land were affected by forest fires. This causes material and immaterial losses to the local community. One of the important aspects of disaster mitigation is an assessment of the vulnerability of potential disaster-prone areas. The purposes of this paper is to determine the level of forest fire disaster vulnerability in the OKI regency to assist in disaster mitigation and recommendations for regional spatial development. The vulnerability assessment used in this study is based on the regulation of the head of the Indonesian national board of disaster management No.2 of 2012 concerning General Guidelines for Indonesian Disaster Risk Assessment. The assessment uses 2 parameters: economic and environmental vulnerability. The method used us weight calculation based on each parameter and data is processed using GIS. Based on the analysis result, a quite high fire potential is in: peatlands areas, forest fire-prones areas, and/or peat swamp forest fires, which covers the area of Kayuagung, Sungai Menang, Cengal, Tulung Selapan, Air Sugihan, Pedamaran Timur, Pedamaran, Pampangan, Pangkalan, Lampam, Lempuing, Lempuing Jaya, Mesuji, Mesuji, Makmur and Mesuji Raya District","PeriodicalId":125663,"journal":{"name":"2020 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125772190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-07DOI: 10.1109/AGERS51788.2020.9452772
A. Nuraghnia, W. Windupranata, N. Hanifa, C. Nusantara
How the tsunami hazard affecting life in Palabuhanratu District is investigated in this paper. By considering this district as a tourist area that is visited by many tourists, the coastal activity is quite high compared to other areas. In the southern region of Java Island, or more precisely Pelabuhanratu, there is an active tectonic zone that can cause underwater earthquakes with a potential strength of up to 8.7 Mw. An underwater earthquake of this magnitude can cause a large tsunami potential, which will greatly affect or even paralyze community activities in coastal areas. This study is conducted to analyze the impact of a potential tsunami that could occur on community activities in the area. To achieve this, it is necessary to do tsunami modeling with numerical methods using the COMCOT v1.7 software on two scenarios. Then the results of the visualization of the inundation will be overlaid with land cover data and buildings data in Palabuhanratu District. The results of this study indicate that the tsunami immersion height in the first scenario modeling is much higher than the second scenario, which is more than 30 m. Meanwhile, for the second scenario, it reaches more than 4 m. The potential for this tsunami caused several losses to the residents, namely the residential area of 917.51 ha is submerged and 17,709 buildings affected in the first scenario, as well as the residential area of 260.54 ha is submerged and 2,779 buildings affected in the second scenario.
{"title":"Analysis of Tsunami Inundation Potential in the Coastal Area of Palabuhanratu District","authors":"A. Nuraghnia, W. Windupranata, N. Hanifa, C. Nusantara","doi":"10.1109/AGERS51788.2020.9452772","DOIUrl":"https://doi.org/10.1109/AGERS51788.2020.9452772","url":null,"abstract":"How the tsunami hazard affecting life in Palabuhanratu District is investigated in this paper. By considering this district as a tourist area that is visited by many tourists, the coastal activity is quite high compared to other areas. In the southern region of Java Island, or more precisely Pelabuhanratu, there is an active tectonic zone that can cause underwater earthquakes with a potential strength of up to 8.7 Mw. An underwater earthquake of this magnitude can cause a large tsunami potential, which will greatly affect or even paralyze community activities in coastal areas. This study is conducted to analyze the impact of a potential tsunami that could occur on community activities in the area. To achieve this, it is necessary to do tsunami modeling with numerical methods using the COMCOT v1.7 software on two scenarios. Then the results of the visualization of the inundation will be overlaid with land cover data and buildings data in Palabuhanratu District. The results of this study indicate that the tsunami immersion height in the first scenario modeling is much higher than the second scenario, which is more than 30 m. Meanwhile, for the second scenario, it reaches more than 4 m. The potential for this tsunami caused several losses to the residents, namely the residential area of 917.51 ha is submerged and 17,709 buildings affected in the first scenario, as well as the residential area of 260.54 ha is submerged and 2,779 buildings affected in the second scenario.","PeriodicalId":125663,"journal":{"name":"2020 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127481997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-07DOI: 10.1109/AGERS51788.2020.9452750
S. Mahelda, W. Windupranata, C. Nusantara
The Sunda Strait Tsunami that occurred on 22 December 2018 was an extraordinary event. This event began with an increase in the volcanic activity of Mount Anak Krakatau on 29 June 2018 that occurred in the form of a series of eruptions and earthquakes that continually caused landslides of Mount Anak Krakatau. The landslide of Mount Anak Krakatau's body that entered the sea generated tsunami waves and hit several areas in the Sunda Strait. Tsunami reconstruction is done by numerical modeling of tsunami wave formation, propagation of tsunami waves, and tsunami inundation using COMCOT software. Data used in tsunami reconstruction are topographical and bathymetry data in the Sunda Strait region and landslide parameters in the form of snapshots (time phases) of landslide movements as tsunami wave generators. Tsunami reconstruction generates information about the propagation of tsunami waves from sources to coastal areas and the height of the tsunami inundation in the area affected by the tsunami. This study also validated the results of the tsunami reconstruction compared with actual events using BIG tide station data in the Sunda Strait area, the results of the study by Syamsidik et al. (2019) [1], and the arrival time of the tsunami originating from BMKG. Validation shows that the waves resulting from reconstruction are larger and the periods are faster than the actual events, but the height of tsunami inundation results from reconstruction is lower and the tsunami arrival time is slower than the actual event.
2018年12月22日发生的巽他海峡海啸是一次非同寻常的事件。这一事件始于2018年6月29日喀拉喀托火山活动的增加，以一系列喷发和地震的形式发生，不断造成喀拉喀托火山滑坡。喀拉喀托火山的山体滑坡进入大海，引发海啸，并袭击了巽他海峡的几个地区。利用COMCOT软件对海啸波的形成、传播和海啸淹没进行数值模拟，完成海啸重建。海啸重建中使用的数据是巽他海峡地区的地形和测深数据，以及作为海啸波发生器的滑坡运动快照(时相)形式的滑坡参数。海啸重建可以产生海啸波从震源向沿海地区传播的信息，以及海啸影响地区的海啸淹没高度。本研究还利用巽他海峡地区BIG潮汐站数据、Syamsidik et al.(2019)[1]的研究结果以及源自BMKG的海啸到达时间，将海啸重建结果与实际事件进行了对比验证。验证结果表明，重建产生的波浪比实际事件大，周期快，但重建产生的海啸淹没高度比实际事件低，海啸到达时间比实际事件慢。
{"title":"Reconstruction of Sunda Strait Tsunami 2018","authors":"S. Mahelda, W. Windupranata, C. Nusantara","doi":"10.1109/AGERS51788.2020.9452750","DOIUrl":"https://doi.org/10.1109/AGERS51788.2020.9452750","url":null,"abstract":"The Sunda Strait Tsunami that occurred on 22 December 2018 was an extraordinary event. This event began with an increase in the volcanic activity of Mount Anak Krakatau on 29 June 2018 that occurred in the form of a series of eruptions and earthquakes that continually caused landslides of Mount Anak Krakatau. The landslide of Mount Anak Krakatau's body that entered the sea generated tsunami waves and hit several areas in the Sunda Strait. Tsunami reconstruction is done by numerical modeling of tsunami wave formation, propagation of tsunami waves, and tsunami inundation using COMCOT software. Data used in tsunami reconstruction are topographical and bathymetry data in the Sunda Strait region and landslide parameters in the form of snapshots (time phases) of landslide movements as tsunami wave generators. Tsunami reconstruction generates information about the propagation of tsunami waves from sources to coastal areas and the height of the tsunami inundation in the area affected by the tsunami. This study also validated the results of the tsunami reconstruction compared with actual events using BIG tide station data in the Sunda Strait area, the results of the study by Syamsidik et al. (2019) [1], and the arrival time of the tsunami originating from BMKG. Validation shows that the waves resulting from reconstruction are larger and the periods are faster than the actual events, but the height of tsunami inundation results from reconstruction is lower and the tsunami arrival time is slower than the actual event.","PeriodicalId":125663,"journal":{"name":"2020 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125455255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-07DOI: 10.1109/AGERS51788.2020.9452774
B. Muslim, Charisma Juni Kumalasari, N. Widjajanti
The GNSS signal reflection technique at the tide station can provide accurate results and is more far-reaching from the coast to the sea, it is possible to provide real-time sealevel information including high wave and tsunami. With the use of two receivers with two antennas, one facing upwards as a direct signal receiver and a second antenna facing downwards as a signal receiver of reflection can be obtained both phase data and GNSS signal codes from both direct and reflected signals. With the different methods of signal trajectory that propagates directly and propagates through reflection by sea level, it can be determined the difference in distance between the master and the shadow rover receivers. The sea level can be determined which is half the distance between the two receivers. In this paper, the results of the data analysis of the different trajectory distances of GNSS signals from code data are presented, from the simplest one, namely a GNSS satellite is assumed to have a linear relationship with the satellite elevation angle with a gradient proportional to sea level. The computation results show that the reflected signal data is suspected not only from sea level but originating from the surrounding environment. It is necessary to experiment with the reflected signal in an area that ensures that the signal is only reflected once by sea level or other reflected plane.
{"title":"Sea High-Level Determination using Pseudorange Range Difference of Carrier Phase and Code between GPS Reflection and Directly Signal","authors":"B. Muslim, Charisma Juni Kumalasari, N. Widjajanti","doi":"10.1109/AGERS51788.2020.9452774","DOIUrl":"https://doi.org/10.1109/AGERS51788.2020.9452774","url":null,"abstract":"The GNSS signal reflection technique at the tide station can provide accurate results and is more far-reaching from the coast to the sea, it is possible to provide real-time sealevel information including high wave and tsunami. With the use of two receivers with two antennas, one facing upwards as a direct signal receiver and a second antenna facing downwards as a signal receiver of reflection can be obtained both phase data and GNSS signal codes from both direct and reflected signals. With the different methods of signal trajectory that propagates directly and propagates through reflection by sea level, it can be determined the difference in distance between the master and the shadow rover receivers. The sea level can be determined which is half the distance between the two receivers. In this paper, the results of the data analysis of the different trajectory distances of GNSS signals from code data are presented, from the simplest one, namely a GNSS satellite is assumed to have a linear relationship with the satellite elevation angle with a gradient proportional to sea level. The computation results show that the reflected signal data is suspected not only from sea level but originating from the surrounding environment. It is necessary to experiment with the reflected signal in an area that ensures that the signal is only reflected once by sea level or other reflected plane.","PeriodicalId":125663,"journal":{"name":"2020 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125152733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-07DOI: 10.1109/AGERS51788.2020.9452784
A. Darmawan, N. Setyaningrum, Riisyani, Ilvi Fauziyah Cahyaningtyas, Aji Putra Perdana
The incidence of the COVID-19 pandemic is not expected, it has a positive side, namely improving air quality, especially in major cities in Indonesia. This study aims to observe and evaluate the effect of PSBB on concentrations of NO2 and CO in eight major cities in Java Island before and during the COVID-19 pandemic. Remote sensing data can undoubtedly be observed more widely. NO2 and CO concentration data are obtained from the Sentinel 5P Tropomi Satellite, while the rainfall data is from the CHIRPS (Climate Hazard Infrared Precipitation with Station) were used to analyze the relationship with the impact of PSBB. The result shows that the implementation of PSBB significantly affects concentrations of NO2 and CO decrease in major cities in Java.
COVID-19大流行的发病率没有预料到，它有积极的一面，即改善空气质量，特别是在印度尼西亚的主要城市。本研究旨在观察和评价PSBB在COVID-19大流行前和期间对爪哇岛8个主要城市NO2和CO浓度的影响。遥感数据无疑可以得到更广泛的观察。NO2和CO浓度数据来自Sentinel 5P Tropomi卫星，降雨数据来自CHIRPS (Climate Hazard Infrared Precipitation with Station)，分析了与PSBB影响的关系。结果表明，实施PSBB对爪哇主要城市NO2和CO浓度的降低有显著影响。
{"title":"Impact of the COVID-19 Large-scale Social Restrictions on NO2 and CO Concentrations in the Most Populated Cities in Java Island, Indonesia","authors":"A. Darmawan, N. Setyaningrum, Riisyani, Ilvi Fauziyah Cahyaningtyas, Aji Putra Perdana","doi":"10.1109/AGERS51788.2020.9452784","DOIUrl":"https://doi.org/10.1109/AGERS51788.2020.9452784","url":null,"abstract":"The incidence of the COVID-19 pandemic is not expected, it has a positive side, namely improving air quality, especially in major cities in Indonesia. This study aims to observe and evaluate the effect of PSBB on concentrations of NO2 and CO in eight major cities in Java Island before and during the COVID-19 pandemic. Remote sensing data can undoubtedly be observed more widely. NO2 and CO concentration data are obtained from the Sentinel 5P Tropomi Satellite, while the rainfall data is from the CHIRPS (Climate Hazard Infrared Precipitation with Station) were used to analyze the relationship with the impact of PSBB. The result shows that the implementation of PSBB significantly affects concentrations of NO2 and CO decrease in major cities in Java.","PeriodicalId":125663,"journal":{"name":"2020 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125324886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-07DOI: 10.1109/AGERS51788.2020.9452752
Muhammad Iqbal Habibie, T. Ahamed, R. Noguchi, S. Matsushita
Climate change has had a global effect on staple crops. Indonesia is a developed country facing a significant threat to climate change. The study uses the Normalized Difference Water Index (NDWI) obtained from Landsat 8 OLI to define the water scarcity in the study area. This research proposes a CNN-based YOLO model that can detect Drought in growing maize development stages. The study was observed in 2018. The detection drought based on the growing season using deep learning was found IoU, Precision, Recall, F1-Score, mean Average Precision (mAP), 83.4%, 98%, 99%, 98%, 96% in the drought-prone areas. The model allows combining remote sensing technology to detect object detection in real-time with acceptable accuracy.
气候变化对全球主要作物产生了影响。印度尼西亚是一个发达国家，面临着气候变化的重大威胁。本研究使用Landsat 8 OLI获得的归一化差水指数(NDWI)来定义研究区域的缺水程度。本研究提出了一种基于cnn的YOLO模型，该模型可以检测玉米生长发育阶段的干旱。这项研究是在2018年进行的。基于生长季节的深度学习干旱检测方法在干旱易发地区的IoU、Precision、Recall、F1-Score、mean Average Precision (mAP)分别为83.4%、98%、99%、98%、96%。该模型可以结合遥感技术，以可接受的精度实时检测目标。
{"title":"Deep Learning Algorithms to determine Drought prone Areas Using Remote Sensing and GIS","authors":"Muhammad Iqbal Habibie, T. Ahamed, R. Noguchi, S. Matsushita","doi":"10.1109/AGERS51788.2020.9452752","DOIUrl":"https://doi.org/10.1109/AGERS51788.2020.9452752","url":null,"abstract":"Climate change has had a global effect on staple crops. Indonesia is a developed country facing a significant threat to climate change. The study uses the Normalized Difference Water Index (NDWI) obtained from Landsat 8 OLI to define the water scarcity in the study area. This research proposes a CNN-based YOLO model that can detect Drought in growing maize development stages. The study was observed in 2018. The detection drought based on the growing season using deep learning was found IoU, Precision, Recall, F1-Score, mean Average Precision (mAP), 83.4%, 98%, 99%, 98%, 96% in the drought-prone areas. The model allows combining remote sensing technology to detect object detection in real-time with acceptable accuracy.","PeriodicalId":125663,"journal":{"name":"2020 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125368419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-07DOI: 10.1109/AGERS51788.2020.9452753
R. Sulistyowati, F. Meliani, Winarno, H. A. Belgaman, F. Syamsudin
Radar rainfall data will be used as an input for the distributed hydrological model to understand flood disaster mitigation. By using multiparameter weather radar data during 13 - 15 February 2016 to calculate total rainfall amount and simulate the inundation area over the Ciliwung river basin. This preliminary study has been supported by Insinas - Ministry of Research and Technology.
{"title":"Flood Early Warning System (FEWS) based on weather radar observation at Ciliwung river basin: Preliminary study","authors":"R. Sulistyowati, F. Meliani, Winarno, H. A. Belgaman, F. Syamsudin","doi":"10.1109/AGERS51788.2020.9452753","DOIUrl":"https://doi.org/10.1109/AGERS51788.2020.9452753","url":null,"abstract":"Radar rainfall data will be used as an input for the distributed hydrological model to understand flood disaster mitigation. By using multiparameter weather radar data during 13 - 15 February 2016 to calculate total rainfall amount and simulate the inundation area over the Ciliwung river basin. This preliminary study has been supported by Insinas - Ministry of Research and Technology.","PeriodicalId":125663,"journal":{"name":"2020 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127046325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-07DOI: 10.1109/AGERS51788.2020.9452762
S. Lestari, A. King, C. Vincent
Heavy rainfall has been known as one of the factors inducing hydrometeorological hazards over the Megacity Jakarta. However, research on changes in rainfall extremes (REs) is somehow very limited in the region. This study will investigate annual and seasonal of REs and how it varies with different topography. We used daily rainfall record at nine observational sites (1975–2016) and reanalysis data of ERA INTERIM (1979–2016). The result shows that the severest rainfall (maximum of consecutive 5-day rainfall/RX5day and 99th percentile/R99p) has strong positive trends particularly at Kemayoran (coastal site) although the increased trends are also found at any other stations over the inland and mountainous areas. The analysis of seasonal trends demonstrates that significant increasing trends only occur in the wet (Dec-Feb) and transitional season (Mar-May, Sep-Nov) over Kemayoran (the coastal station) while a positive trend is observed in all seasons over Citeko (the mountain site). Compared to the wet season, in the dry (Jun-Aug) and transitional (Sep-Nov) seasons, there is concentrated areas near Jakarta and its surroundings with a large standard deviation in Mean Sea Level Pressure (MSLP), Outgoing Longwave Radiation (OLR) as well as cloud cover indicating that this region has a larger variability than the average. A distinct season of increased rainfall trends between the coast and mountain along with high anomalies in the regional OLR, MSLP, and cloud cover in the dry season implies that the development of REs might as a result of an interaction between local topography and large-scale climate condition.
{"title":"Annual Changes in Rainfall Extremes over the Megacity Jakarta","authors":"S. Lestari, A. King, C. Vincent","doi":"10.1109/AGERS51788.2020.9452762","DOIUrl":"https://doi.org/10.1109/AGERS51788.2020.9452762","url":null,"abstract":"Heavy rainfall has been known as one of the factors inducing hydrometeorological hazards over the Megacity Jakarta. However, research on changes in rainfall extremes (REs) is somehow very limited in the region. This study will investigate annual and seasonal of REs and how it varies with different topography. We used daily rainfall record at nine observational sites (1975–2016) and reanalysis data of ERA INTERIM (1979–2016). The result shows that the severest rainfall (maximum of consecutive 5-day rainfall/RX5day and 99th percentile/R99p) has strong positive trends particularly at Kemayoran (coastal site) although the increased trends are also found at any other stations over the inland and mountainous areas. The analysis of seasonal trends demonstrates that significant increasing trends only occur in the wet (Dec-Feb) and transitional season (Mar-May, Sep-Nov) over Kemayoran (the coastal station) while a positive trend is observed in all seasons over Citeko (the mountain site). Compared to the wet season, in the dry (Jun-Aug) and transitional (Sep-Nov) seasons, there is concentrated areas near Jakarta and its surroundings with a large standard deviation in Mean Sea Level Pressure (MSLP), Outgoing Longwave Radiation (OLR) as well as cloud cover indicating that this region has a larger variability than the average. A distinct season of increased rainfall trends between the coast and mountain along with high anomalies in the regional OLR, MSLP, and cloud cover in the dry season implies that the development of REs might as a result of an interaction between local topography and large-scale climate condition.","PeriodicalId":125663,"journal":{"name":"2020 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"19 8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123553244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-07DOI: 10.1109/AGERS51788.2020.9452770
P. K. Putra, D. B. Sencaki, G. P. Dinanta, F. Alhasanah, R. Ramadhan
Flood natural disasters that often occur in Jakarta have a bad impact on many sectors. Countermeasures, fast action, and monitoring need to be done to minimize the impact that occurs. Social Media is a technology platform that can provide flood-related data that can be used as primary data or complementary data for monitoring systems. This study focuses on using social media data to be used as flood monitoring data. The analysis used is an analysis with a natural language processing approach. The classification algorithm method used in this study is naive Bayes, random forest, support vector machine, logistic regression, and conditional random field. Location information extraction methods used are Standford NER and Geocoding. This research produces three models. The first model is the classification model used to classify relevant data with an f1- score evaluation value of 82.5%. The second model is the NER model which is used to extract location entities from sentences with an f1-score evaluation value of 73%. The last one is the locator of geocoding with a success rate of 75% for identifying roads. This research also produces a simple dashboard that can be used as a visualization tool.
{"title":"Flood Monitoring with Information Extraction Approach from Social Media Data","authors":"P. K. Putra, D. B. Sencaki, G. P. Dinanta, F. Alhasanah, R. Ramadhan","doi":"10.1109/AGERS51788.2020.9452770","DOIUrl":"https://doi.org/10.1109/AGERS51788.2020.9452770","url":null,"abstract":"Flood natural disasters that often occur in Jakarta have a bad impact on many sectors. Countermeasures, fast action, and monitoring need to be done to minimize the impact that occurs. Social Media is a technology platform that can provide flood-related data that can be used as primary data or complementary data for monitoring systems. This study focuses on using social media data to be used as flood monitoring data. The analysis used is an analysis with a natural language processing approach. The classification algorithm method used in this study is naive Bayes, random forest, support vector machine, logistic regression, and conditional random field. Location information extraction methods used are Standford NER and Geocoding. This research produces three models. The first model is the classification model used to classify relevant data with an f1- score evaluation value of 82.5%. The second model is the NER model which is used to extract location entities from sentences with an f1-score evaluation value of 73%. The last one is the locator of geocoding with a success rate of 75% for identifying roads. This research also produces a simple dashboard that can be used as a visualization tool.","PeriodicalId":125663,"journal":{"name":"2020 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115886100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-07DOI: 10.1109/AGERS51788.2020.9452761
Sarah K. Anwar, N. P. Purba, Yuniarti, Subiyanto
This study aimed to reveal the coastal vulnerability of West Java. This vulnerability is supported by the shoreline changes map. The method was used Coastal Vulnerability Index (CVI) using waves, mangrove ecosystems, bathymetry, and relief as the main data from 2016–2019. The coastal vulnerability was assessed using models from InVEST software and coastline change were detected using CoastSat software with Landsat 7 and 8 images data from 2000–2019. The result showed that the vulnerability in West Java coast is still at a high level, especially in the northern region. Each city or district has different parameters that influence its vulnerability. The occurrence of shoreline changes is due to the vulnerability parameter as well.
{"title":"Coastal Vulnerability Based on Oceanographic and Ecosystem Parameters on the North and South Coast of West Java","authors":"Sarah K. Anwar, N. P. Purba, Yuniarti, Subiyanto","doi":"10.1109/AGERS51788.2020.9452761","DOIUrl":"https://doi.org/10.1109/AGERS51788.2020.9452761","url":null,"abstract":"This study aimed to reveal the coastal vulnerability of West Java. This vulnerability is supported by the shoreline changes map. The method was used Coastal Vulnerability Index (CVI) using waves, mangrove ecosystems, bathymetry, and relief as the main data from 2016–2019. The coastal vulnerability was assessed using models from InVEST software and coastline change were detected using CoastSat software with Landsat 7 and 8 images data from 2000–2019. The result showed that the vulnerability in West Java coast is still at a high level, especially in the northern region. Each city or district has different parameters that influence its vulnerability. The occurrence of shoreline changes is due to the vulnerability parameter as well.","PeriodicalId":125663,"journal":{"name":"2020 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123580773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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