scholarly journals Identification of Landslide-Prone Subsoil Using Wenner Configuration Geoelectric Method in Gayamharjo Village, Prambanan District, Sleman Regency

2019 ◽  
Vol 2 ◽  
pp. 125-129
Author(s):  
Najila Tihurua ◽  
Thaqibul Fikri Niyartama ◽  
Yunita Eri Setyaningrum ◽  
Qurrotul Uyun
Keyword(s):  
Data Processing ◽  
Research Area ◽  
Track Length ◽  
Subsurface Modeling

Landslides occur due to the field of slip. Identification of Soil Landslide Identification Using Geolistrik Method The Wenner configuration has been done in Gayamharjo Village, Prambanan Sub-district, Sleman District. This study aims to determine the structure of subsurface rocks and identify the field of ground slip in the landslide prone areas in the study area. Measurements were made as many as 3 trajectories, the smallest spaced between 20 meters electrode with 300 meters of track length. The tool used is Syscal Jr Switch-48. Data processing uses RES2DINV software that produces 2D subsurface modeling. The results of the interpretation showed that th e location of the study identified the constituent rock consisting of three layers of subsurface rocks (1,36 to 6,86) Ωm, sandstone (15,4 to 34,6) Ωm, and andesite rocks (77, 8 to 393) Ωm. In the three trajectories of the research area, there is a slip field with th e potential for landslide: track 1 at point 40 s.d.80 with a depth of 37 m, track 2 at point 220 s.d. 240 with depth 26 m, path 3 at point 100 s.d. 140 with a depth of 37 m.

METEOROLOGICAL GNSS APPLICATION FOR HEAVY RAIN ON DECEMBER 31, 2019 IN THE JAKARTA AND SURROUNDING AREAS

2021 ◽  
pp. 273
Author(s):  
Syachrul Arief ◽  
Ihsan Muhamad Muafiry
Keyword(s):  
Water Vapor ◽  
Data Processing ◽  
Average Distance ◽  
Heavy Rain ◽  
Research Area ◽  
High Rainfall ◽  
Interesting Pattern ◽  
Surrounding Areas ◽  
Gnss Data ◽  
Second Peak

This study aims to utilize GNSS for meteorology in Indonesia. With the "goGPS" software, the zenith troposphere delay (ZTD) value is estimated. Calculations in rainy conditions, the ZTD value is converted into a water vapor value (PWV). The research area for the phenomenon of heavy rain occurred at the end of 2019 in Jakarta and its surroundings, which caused flooding on January 1, 2020. According to the Geophysical Meteorology and Climatology Agency (BMKG), the flood's primary cause was high rainfall. Meanwhile, the rainfall at Taman Mini and Jatiasih stations was 335 mm/day and 260 mm/day, respectively. We get an interesting pattern of PWV values for this rain phenomenon. GNSS data processing, the PWV value at five GNSS stations around Jakarta, shows the same pattern even though the average distance between GNSS stations is ~ 30 km. The PWV value appears to increase at noon on December 30, 2019, and the peak occurs in the early hours of December 31, 2019. The PWV value suddenly decreases at noon on January 1, 2020. Next, the PWV value increases again but not as high as at the previous peak. Since January 2, 2020, the PWV value has decreased and remained almost constant until January 4, 2020. In that period, there were two events that the PWV value increased. The PWV value at the first peak is ~ 70 mm, and at the second peak ~ 65 mm. The most significant increase in PWV value was recorded at CJKT stations.

Analysis of landslide area of Tulung subdistrict, Ponorogo, Indonesia in 2017 using resistivity method

2020 ◽  
Vol 9 (4) ◽  
pp. 341-360
Author(s):  
Adi Susilo ◽  
Fina Fitriah ◽  
Sunaryo ◽  
Eng Turniningtyas Ayu Rachmawati ◽  
Eko Andi Suryo
Keyword(s):  
Design Methodology ◽  
Research Area ◽  
Electric Resistivity ◽  
Disaster Mitigation ◽  
Track Length ◽  
Landslide Area ◽  
Content Type ◽  
Resistivity Method ◽  
Geological Maps ◽  
Measured Resistivity

PurposeResearch has been conducted to analyze the landslide in Banaran area, Ponorogo Regency. The landslides occurred on April 1, 2017. This study was conducted to know the subsurface conditions in the Banaran area to analyze the disaster mitigation efforts. The mitigation efforts are made to reduce the risk from landslides and possible landslides.Design/methodology/approachThe method used is the geo-electric resistivity method of Wenner–Schlumberger configuration. The research was conducted in three villages namely Banaran Village, Bekirang Village and Mendak Village.FindingsThere are 12 resistivity measuring points with a track length of 410 m and a space of 10 m. The measured resistivity range is between 1.42 Ω.m and 67.500 Ω.m. The resistivity data and the local geological maps interpreted that the rocks in the Banaran area consist of clay, tuff lapilli, volcanic breccia and andesite lava. The landslide area begins at a depth of 8–35 m below the surface which is interpreted as tuff. Also, the thickness of the landslide material and the slope is = 400 which supports the occurrence of a more prominent landslide. The results of the parameter scoring of the landslide-prone areas indicated that the research area is very vulnerable to a landslide. The results of the interpretation indicate that the geo-electric resistivity method can provide a good overview for conducting landslide analysis, that is field slippage and potential material thickness occurrence landslide.Originality/valueThis article is very specific as it attempts to discover how prone Banaran are is to landslide.

Resource Provisioning and Scheduling of Big Data Processing Jobs

2018 ◽  
pp. 382-401
Author(s):  
Rajni Aron ◽  
Deepak Kumar Aggarwal
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Processing ◽  
Research Area ◽  
Resource Provisioning ◽  
Time Data ◽  
Big Data Processing ◽  
Cloud Resource Management ◽  
Big Data Applications ◽  
Cloud Resource

Cloud Computing has become a buzzword in the IT industry. Cloud Computing which provides inexpensive computing resources on the pay-as-you-go basis is promptly gaining momentum as a substitute for traditional Information Technology (IT) based organizations. Therefore, the increased utilization of Clouds makes an execution of Big Data processing jobs a vital research area. As more and more users have started to store/process their real-time data in Cloud environments, Resource Provisioning and Scheduling of Big Data processing jobs becomes a key element of consideration for efficient execution of Big Data applications. This chapter discusses the fundamental concepts supporting Cloud Computing & Big Data terms and the relationship between them. This chapter will help researchers find the important characteristics of Cloud Resource Management Systems to handle Big Data processing jobs and will also help to select the most suitable technique for processing Big Data jobs in Cloud Computing environment.

Applying the Horizon Based Tomography Method to Update Interval Velocity Model, Identify The Structure of Pre-Stack Depth Migration 3D and Estimate The Hydrocarbon Reserve In SBI Field of North West Java Basin

2014 ◽  
Vol 69 (6) ◽  
Author(s):  
Sudra Irawan ◽  
Sismanto Sismanto ◽  
Adang Sukmatiawan
Keyword(s):  
Data Processing ◽  
Seismic Data ◽  
Velocity Model ◽  
Research Area ◽  
Seismic Data Processing ◽  
West Java ◽  
North West ◽  
Depth Migration ◽  
Interval Velocity ◽  
Tomography Method

Seismic data processing is one of the three stages in the seismic method that has an important role in the exploration of oil and gas. Without good data processing, it is impossible to get seismic image cross section for good interpretation. A research using seismic data processing was done to update the velocity model by horizon based tomography method in SBI Field, North West Java Basin. This method reduces error of seismic wave travel time through the analyzed horizon because the existence velocity of high lateral variation in research area. There are three parameters used to determine the accuracy of the resulting interval velocity model, namely, flat depth gathers, semblance residual moveout that coincides with the axis zero residual moveout, and the correspondence between image depth (horizon) with wells marker  (well seismic tie). Pre Stack Depth Migration (PSDM) form interval velocity model and updating using horizon-based tomography method gives better imaging of under-surfaced structure results than PSDM before using tomography. There are three faults found in the research area, two normal faults have southwest-northeast strike and the other has northwest-southeast strike. The thickness of reservoir in SBI field, North West Java Basin, is predicted between 71 to 175 meters and the hydrocarbon (oil) reserve is predicted about  with 22.6% porosity and 70.7% water saturation. 

Streaming Remote Sensing Data Processing for the Future Smart Cities

2016 ◽  
Vol 7 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Xihuang Sun ◽  
Peng Liu ◽  
Yan Ma ◽  
Dingsheng Liu ◽  
Yechao Sun
Keyword(s):  
Remote Sensing ◽  
Data Processing ◽  
Smart Cities ◽  
Remote Sensing Data ◽  
Data Access ◽  
Research Area ◽  
Data Streaming ◽  
Real Time Processing ◽  
Sensing Data ◽  
Programming Interfaces

The explosion of data and the increase in processing complexity, together with the increasing needs of real-time processing and concurrent data access, make remote sensing data streaming processing a wide research area to study. This paper introduces current situation of remote sensing data processing and how timely remote sensing data processing can help build future smart cities. Current research on remote sensing data streaming is also introduced where the three typical and open-source stream processing frameworks are introduced. This paper also discusses some design concerns for remote sensing data streaming processing systems, such as data model and transmission, system model, programming interfaces, storage management, availability, etc. Finally, this research specifically addresses some of the challenges of remote sensing data streaming processing, such as scalability, fault tolerance, consistency, load balancing and throughput.

Big data analytics with swarm intelligence

2016 ◽  
Vol 116 (4) ◽  
pp. 646-666 ◽  
Author(s):  
Shi Cheng ◽  
Qingyu Zhang ◽  
Quande Qin
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Swarm Intelligence ◽  
Data Analytics ◽  
Large Scale ◽  
Big Data Analytics ◽  
Underlying Mechanism ◽  
Research Area ◽  
Future Research ◽  
Content Type

Purpose – The quality and quantity of data are vital for the effectiveness of problem solving. Nowadays, big data analytics, which require managing an immense amount of data rapidly, has attracted more and more attention. It is a new research area in the field of information processing techniques. It faces the big challenges and difficulties of a large amount of data, high dimensionality, and dynamical change of data. However, such issues might be addressed with the help from other research fields, e.g., swarm intelligence (SI), which is a collection of nature-inspired searching techniques. The paper aims to discuss these issues. Design/methodology/approach – In this paper, the potential application of SI in big data analytics is analyzed. The correspondence and association between big data analytics and SI techniques are discussed. As an example of the application of the SI algorithms in the big data processing, a commodity routing system in a port in China is introduced. Another example is the economic load dispatch problem in the planning of a modern power system. Findings – The characteristics of big data include volume, variety, velocity, veracity, and value. In the SI algorithms, these features can be, respectively, represented as large scale, high dimensions, dynamical, noise/surrogates, and fitness/objective problems, which have been effectively solved. Research limitations/implications – In current research, the example problem of the port is formulated but not solved yet given the ongoing nature of the project. The example could be understood as advanced IT or data processing technology, however, its underlying mechanism could be the SI algorithms. This paper is the first step in the research to utilize the SI algorithm to a big data analytics problem. The future research will compare the performance of the method and fit it in a dynamic real system. Originality/value – Based on the combination of SI and data mining techniques, the authors can have a better understanding of the big data analytics problems, and design more effective algorithms to solve real-world big data analytical problems.

scholarly journals Bayesian Inference and Maximum Entropy Methods in Science and Engineering—MaxEnt 2019†

Proceedings ◽  
2019 ◽  
Vol 33 (1) ◽  
pp. 8
Author(s):  
Udo von Toussaint ◽  
Roland Preuss
Keyword(s):  
Parameter Estimation ◽  
Bayesian Inference ◽  
Data Processing ◽  
Maximum Entropy ◽  
Density Estimation ◽  
Model Comparison ◽  
Building Blocks ◽  
Research Area ◽  
Science And Engineering ◽  
Entropy Methods

As key building blocks for modern data processing and analysis methods—ranging from AI, ML and UQ to model comparison, density estimation and parameter estimation—Bayesian inference and entropic concepts are in the center of this rapidly growing research area. [...]

Streaming Remote Sensing Data Processing for the Future Smart Cities

2019 ◽  
pp. 1711-1726 ◽  
Author(s):  
Xihuang Sun ◽  
Peng Liu ◽  
Yan Ma ◽  
Dingsheng Liu ◽  
Yechao Sun
Keyword(s):  
Remote Sensing ◽  
Data Processing ◽  
Smart Cities ◽  
Remote Sensing Data ◽  
Data Access ◽  
Research Area ◽  
Data Streaming ◽  
Real Time Processing ◽  
Sensing Data ◽  
Programming Interfaces

The explosion of data and the increase in processing complexity, together with the increasing needs of real-time processing and concurrent data access, make remote sensing data streaming processing a wide research area to study. This paper introduces current situation of remote sensing data processing and how timely remote sensing data processing can help build future smart cities. Current research on remote sensing data streaming is also introduced where the three typical and open-source stream processing frameworks are introduced. This paper also discusses some design concerns for remote sensing data streaming processing systems, such as data model and transmission, system model, programming interfaces, storage management, availability, etc. Finally, this research specifically addresses some of the challenges of remote sensing data streaming processing, such as scalability, fault tolerance, consistency, load balancing and throughput.

scholarly journals Visual Analysis of Research Paper Collections Using Normalized Relative Compression

Entropy ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. 612
Author(s):  
Pere-Pau Vázquez
Keyword(s):  
Data Processing ◽  
Reference Lists ◽  
Visual Analysis ◽  
Research Paper ◽  
Research Area ◽  
Research Articles ◽  
Human Intervention ◽  
Document Collections ◽  
Automated Data Processing ◽  
Relative Compression

The analysis of research paper collections is an interesting topic that can give insights on whether a research area is stalled in the same problems, or there is a great amount of novelty every year. Previous research has addressed similar tasks by the analysis of keywords or reference lists, with different degrees of human intervention. In this paper, we demonstrate how, with the use of Normalized Relative Compression, together with a set of automated data-processing tasks, we can successfully visually compare research articles and document collections. We also achieve very similar results with Normalized Conditional Compression that can be applied with a regular compressor. With our approach, we can group papers of different disciplines, analyze how a conference evolves throughout the different editions, or how the profile of a researcher changes through the time. We provide a set of tests that validate our technique, and show that it behaves better for these tasks than other techniques previously proposed.

scholarly journals Identifikasi Lapisan Bawah Permukaan yang Terkontaminasi Lindi Sekitar Sistem Sanitary Landfill di TPA Cahaya Kencana, Kabupaten Banjar

2017 ◽  
Vol 14 (1) ◽  
pp. 46
Author(s):  
Meirani Agustina ◽  
Sri Cahyo Wahyono ◽  
Tetti Novalina Manik
Keyword(s):  
Waste Water ◽  
Heavy Metals ◽  
Water Quality ◽  
Laboratory Test ◽  
Data Processing ◽  
Chemical Elements ◽  
Quality Standard ◽  
Track Length ◽  
Sanitary Landfill ◽  
Water Quality Standard

The landfills (TPA) Cahaya Kencana is applying system Sanitary Landfill, the landfill system that is done by the garbage dumped and compacted, then covered with dirt as cover and carried out continuously each end of the operation. This research is very important investigate the distribution of leachate contaminated leachate approximately systems Sanitary Landfill based on the resistivity value and the content of chemical elements in the holding pond. Based on the results of data processing 2D geoelectric Wenner configuration on four tracks with each track length of 100 m were processed using Res2dinv, obtainable at a depth of between 0.75 – 11.6 m resistivity values in the leachate is 4.34 – 10.0 Ωm track 2, 3, and 4, but are not on track 1. The results of laboratory test on four samples consecutive for content. BOD leachate from 26.2 - 121.0 mg/l; and COD 132.5 – 276.2 mg/l is not exceeded the limit of waste water quality standard, except for sample 4 with a COD value of 472.9 mg/l while for pH 7.24 - 7.39 mg/l and the concentration of heavy metals in leachate outlet still on the threshold of normal, where Pb <0.0019 mg/l; Cd <0.0019 mg/l; and Cr <0,0035mg/l.

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