scholarly journals PENERAPAN FUZZY LOGIC DALAM PEMBUATAN PETA ELEMENT AT RISK BENCANA LUAPAN BANJIR SANGAI AIR BENGKULU KOTA BENGKULU

2019 ◽  
Vol 11 (2) ◽  
pp. 135-139
Author(s):  
Farouki Dinda Rassarandi ◽  
Bungaran Roy Satria Tambunan
Keyword(s):  
At Risk ◽  
Fuzzy Logic ◽  
Input Data ◽  
Element At Risk

Banjir merupakan suatu bencana yang dapat menimbulkan kerugian dan kerusakan di berbagai bidang, khususnya infrastruktur. Salah satu upaya untuk mencegah dan mengurangi dampak dari bencana banjir yaitu dengan pembuatan simulasi melalui pemodelan spasial dalam bentuk peta element at risk. Pada pembuatan peta element at risk, input data berupa peta yang diunduh dari Open Street Map yang berisikan kenampakan alam maupun infrastruktur dari simulasi bencana yang dibuat menggunakan logika Fuzzy. Penerapan logika Fuzzy digunakan untuk menginterpretasikan statemen yang samar dari persentase area bangunan yang terdampak pada setiap klasifikasi area luapan banjir menjadi sebuah pengertian yang logis dalam pengklasifikasian kerusakan “Berat’, “Sedang” dan “Ringan”. Berdasarkan hasil simulasi bencana banjir yang telah dilakukan, didapati bahwa jumlah bangunan yang terkena dampak bencana banjir luapan Sungai Air Bengkulu adalah sebanyak 37 bangunan “Rusak Berat’, 216 “Rusak Sedang’ dan 329 “Rusak Ringan’, dengan jumlah korban jiwa terdampak sebanyak 2.328 jiwa.

scholarly journals Definitions and Concepts for Quantitative Rockfall Hazard and Risk Analysis

Geosciences ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 158
Author(s):  
Didier Hantz ◽  
Jordi Corominas ◽  
Giovanni B. Crosta ◽  
Michel Jaboyedoff
Keyword(s):  
Risk Assessment ◽  
At Risk ◽  
Failure Probability ◽  
Rockfall Hazard ◽  
Process Failure ◽  
Failure Propagation ◽  
Element At Risk ◽  
Hazard And Risk ◽  
Hazard And Risk Assessment ◽  
Definition Of

There is an increasing need for quantitative rockfall hazard and risk assessment that requires a precise definition of the terms and concepts used for this particular type of landslide. This paper suggests using terms that appear to be the most logic and explicit as possible and describes methods to derive some of the main hazards and risk descriptors. The terms and concepts presented concern the rockfall process (failure, propagation, fragmentation, modelling) and the hazard and risk descriptors, distinguishing the cases of localized and diffuse hazards. For a localized hazard, the failure probability of the considered rock compartment in a given period of time has to be assessed, and the probability for a given element at risk to be impacted with a given energy must be derived combining the failure probability, the reach probability, and the exposure of the element. For a diffuse hazard that is characterized by a failure frequency, the number of rockfalls reaching the element at risk per unit of time and with a given energy (passage frequency) can be derived. This frequency is relevant for risk assessment when the element at risk can be damaged several times. If it is not replaced, the probability that it is impacted by at least one rockfall is more relevant.

scholarly journals Calculation of a Health Index of Oil-Paper Transformers Insulation with Binary Logistic Regression

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Weijie Zuo ◽  
Haiwen Yuan ◽  
Yuwei Shang ◽  
Yingyi Liu ◽  
Tao Chen
Keyword(s):  
Fuzzy Logic ◽  
Logistic Regression ◽  
Input Data ◽  
Binary Logistic Regression ◽  
Health Condition ◽  
Gas Analysis ◽  
Total Acidity ◽  
Health Index ◽  
Dissolved Gas ◽  
Comparison Results

This paper presents a new method for calculating the insulation health index (HI) of oil-paper transformers rated under 110 kV to provide a snapshot of health condition using binary logistic regression. Oil breakdown voltage (BDV), total acidity of oil, 2-Furfuraldehyde content, and dissolved gas analysis (DGA) are singled out in this method as the input data for determining HI. A sample of transformers is used to test the proposed method. The results are compared with the results calculated for the same set of transformers using fuzzy logic. The comparison results show that the proposed method is reliable and effective in evaluating transformer health condition.

scholarly journals ANALISIS KERAWANAN BANJIR MENGGUNAKAN PENDEKATAN GEOMORFOLOGI DI DKI JAKARTA

2018 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Siti Dahlia ◽  
Tricahyono Nurharsono ◽  
Wira Fazri Rosyidin
Keyword(s):  
At Risk ◽  
Central Government ◽  
Flood Hazard ◽  
Research Area ◽  
Capital City ◽  
Capital Region ◽  
Low Area ◽  
Flood Susceptibility ◽  
Element At Risk ◽  
Result Analysis

ABSTRACT  Special Capital Region of Jakarta Province is Capital City of Indonesia, which has various strategic functions, such as central government and economic and business center. Geographically DKI Jakarta Province is lowland, it caused Jakarta has high of flood hazard. This condition potentially result of high risk. Based on it, the aims of research is: 1). Making map of flood susceptibility in Special Capital Region of Jakarta area, based on geomorphology approach, and 2). Data inventory element at risk of flood. The method of data analysis used qualitative, based on interpretation satellite imagery data using elements of interpretation. Indicators used to result map of flood susceptibility are elevation, slope, and landform, using scoring and overlay technique. The result of research is flood susceptibility of low area is 13.613,40 ha, medium is 23.238,67 ha, and higt is 27.216,72 ha. Based on it, the majority of research area have hight of flood susceptibility. Based on spatial pattern, it showed that areas with high flood susceptibility are mostly located in the northern part of research area, and areas with the lowest flood susceptibility are majority in the southern part of researh area. The result analysis of element at risk, it showed that element at risk  affected by flood for high, medium, or low level is settlement. Key Words: Flood Susceptibility of Map, Exposure, Geomorphology, and Special Capital Region of Jakarta ABSTRAK Provinsi DKI Jakarta merupakan Ibu Kota negara Indonesia yang memiliki beragam fungsi startegis, seperti pusat pemerintahan, dan pusat ekonomi dan bisnis. Akan tetapi, kondisi geografis Provinsi DKI Jakarta yang merupakan dataran rendah, mengakibatkan wilayah Jakarta memiliki ancaman tinggi terhadap bahaya banjir. Hal ini dapat berpotensi menghasilkan tingginya risiko kerugian terhadap bencana. Berdasarkan hal tersebut, tujuan dalam penelitian ini yaitu: 1) Membuat peta kerawanan banjir Provinsi DKI Jakarta berdasarkan pendekatan geomorfologi, dan 2). Melakukan inventarisasi elemen berisiko yang berpotensi terpapar banjir. Metode analisis data dalam penelitian ini menggunakan analisis kualitatif, karena berdasarkan teknik interpretasi data citra secara kualitatif yaitu menggunakan unsur-unsur interpretasi. Parameter- parameter yang digunakan untuk menghasilkan peta kerawanan banjir yaitu elevasi, kemiringan lereng, dan bentuklahan, dengan menggunakan skoring dan tumpang susun. Hasil penelitian menunjukkan bahwa tingkat kerawanan banjir rendah seluas 13.613,40 ha, sedang seluas 23.238,67 ha, dan tinggi seluas 27.216,72 ha. Mayoritas wilayah penelitian terletak pada tingkat kerawanan banjir tinggi. Berdasarkan pola spasial menunjukkan bahwa daerah dengan tingkat kerawanan banjir tinggi mayoritas terletak di bagian utara wilayah penelitian, dan daerah dengan tingkat kerawanan banjir rendah mayoritas dibagian selatan wilayah penelitian. Hasil analisis keterpaparan elemen berisiko wilayah penelitian menunjukkan bahwa elemen berisiko yang berpotensi tertinggi terkena banjir baik tingkat tinggi, sedang, atau rendah yaitu pemukiman. Kata Kunci: Pemetaan Kerawanan Banjir, Keterpaparan, Gemorfologi, dan DKI Jakarta

scholarly journals A Philosophical Treatise and Educational Aspects on Fuzzy Control

2020 ◽  
Vol 19 ◽  
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Control ◽  
Control Systems ◽  
Human Activity ◽  
Input Data ◽  
Feedback Controller ◽  
Central Heating ◽  
Almost All ◽  
Heating Boiler

Fuzzy Logic has found nowadays many applications to almost all sectors of human activity, withfuzzy control being one of the most important such applications. A control system regulates the behavior of adevice or another system with the help of a feedback controller. A fuzzy control system is a control system thatanalyses the input data in terms of variables which take continuous values in the interval [0, 1]. The presentarticle studies in detail the operation of fuzzy control systems and illustrates it by presenting an exampleof controlling a building’s central heating boiler.

scholarly journals Vulnerability curves vs. vulnerability indicators: application of an indicator-based methodology for debris-flow hazards

2016 ◽  
Vol 16 (8) ◽  
pp. 1771-1790 ◽  
Author(s):  
Maria Papathoma-Köhle
Keyword(s):  
At Risk ◽  
Debris Flow ◽  
Vulnerability Assessment ◽  
Structural Characteristics ◽  
Physical Vulnerability ◽  
Vulnerability Indicators ◽  
Element At Risk ◽  
Mountain Hazards ◽  
Methodological Approaches

Abstract. The assessment of the physical vulnerability of elements at risk as part of the risk analysis is an essential aspect for the development of strategies and structural measures for risk reduction. Understanding, analysing and, if possible, quantifying physical vulnerability is a prerequisite for designing strategies and adopting tools for its reduction. The most common methods for assessing physical vulnerability are vulnerability matrices, vulnerability curves and vulnerability indicators; however, in most of the cases, these methods are used in a conflicting way rather than in combination. The article focuses on two of these methods: vulnerability curves and vulnerability indicators. Vulnerability curves express physical vulnerability as a function of the intensity of the process and the degree of loss, considering, in individual cases only, some structural characteristics of the affected buildings. However, a considerable amount of studies argue that vulnerability assessment should focus on the identification of these variables that influence the vulnerability of an element at risk (vulnerability indicators). In this study, an indicator-based methodology (IBM) for mountain hazards including debris flow (Kappes et al., 2012) is applied to a case study for debris flows in South Tyrol, where in the past a vulnerability curve has been developed. The relatively "new" indicator-based method is being scrutinised and recommendations for its improvement are outlined. The comparison of the two methodological approaches and their results is challenging since both methodological approaches deal with vulnerability in a different way. However, it is still possible to highlight their weaknesses and strengths, show clearly that both methodologies are necessary for the assessment of physical vulnerability and provide a preliminary "holistic methodological framework" for physical vulnerability assessment showing how the two approaches may be used in combination in the future.

Designing Unsupervised Hierarchical Fuzzy Logic Systems

2011 ◽  
pp. 456-463
Author(s):  
M. Mohammadian
Keyword(s):  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Unsupervised Learning ◽  
Input Data ◽  
Fuzzy Logic System ◽  
Fuzzy Rules ◽  
Fuzzy Logic Systems ◽  
Logic Systems ◽  
The Individual ◽  
Logic System

Systems such as robotic systems and systems with large input-output data tend to be difficult to model using mathematical techniques. These systems have typically high dimensionality and have degrees of uncertainty in many parameters. Artificial intelligence techniques such as neural networks, fuzzy logic, genetic algorithms and evolutionary algorithms have created new opportunities to solve complex systems. Application of fuzzy logic [Bai, Y., Zhuang H. and Wang, D. (2006)] in particular, to model and solve industrial problems is now wide spread and has universal acceptance. Fuzzy modelling or fuzzy identification has numerous practical applications in control, prediction and inference. It has been found useful when the system is either difficult to predict and or difficult to model by conventional methods. Fuzzy set theory provides a means for representing uncertainties. The underlying power of fuzzy logic is its ability to represent imprecise values in an understandable form. The majority of fuzzy logic systems to date have been static and based upon knowledge derived from imprecise heuristic knowledge of experienced operators, and where applicable also upon physical laws that governs the dynamics of the process. Although its application to industrial problems has often produced results superior to classical control, the design procedures are limited by the heuristic rules of the system. It is simply assumed that the rules for the system are readily available or can be obtained. This implicit assumption limits the application of fuzzy logic to the cases of the system with a few parameters. The number of parameters of a system could be large. The number of fuzzy rules of a system is directly dependent on these parameters. As the number of parameters increase, the number of fuzzy rules of the system grows exponentially. Genetic Algorithms can be used as a tool for the generation of fuzzy rules for a fuzzy logic system. This automatic generation of fuzzy rules, via genetic algorithms, can be categorised into two learning techniques, supervised and unsupervised. In this paper unsupervised learning of fuzzy rules of hierarchical and multi-layer fuzzy logic control systems are considered. In unsupervised learning there is no external teacher or critic to oversee the learning process. In other words, there are no specific examples of the function to be learned by the system. Rather, provision is made for a task-independent measure of the quality or representation that the system is required to learn. That is the system learns statistical regularities of the input data and it develops the ability to learn the feature of the input data and thereby create new classes automatically [Mohammadian, M., Nainar, I. and Kingham, M. (1997)]. To perform unsupervised learning, a competitive learning strategy may be used. The individual strings of genetic algorithms compete with each other for the “opportunity” to respond to features contained in the input data. In its simplest form, the system operates in accordance with the strategy that ‘the fittest wins and survives’. That is the individual chromosome in a population with greatest fitness ‘wins’ the competition and gets selected for the genetic algorithms operations (cross-over and mutation). The other individuals in the population then have to compete with fit individual to survive. The diversity of the learning tasks shown in this paper indicates genetic algorithm’s universality for concept learning in unsupervised manner. A hybrid integrated architecture incorporating fuzzy logic and genetic algorithm can generate fuzzy rules for problems requiring supervised or unsupervised learning. In this paper only unsupervised learning of fuzzy logic systems is considered. The learning of fuzzy rules and internal parameters in an unsupervised manner is performed using genetic algorithms. Simulations results have shown that the proposed system is capable of learning the control rules for hierarchical and multi-layer fuzzy logic systems. Application areas considered are, hierarchical control of a network of traffic light control and robotic systems. A first step in the construction of a fuzzy logic system is to determine which variables are fundamentally important. Any number of these decision variables may appear, but the more that are used, the larger the rule set that must be found. It is known [Raju, S., Zhou J. and Kisner, R. A. (1990), Raju G. V. S. and Zhou, J. (1993), Kingham, M., Mohammadian, M, and Stonier, R. J. (1998)], that the total number of rules in a system is an exponential function of the number of system variables. In order to design a fuzzy system with the required accuracy, the number of rules increases exponentially with the number of input variables and its associated fuzzy sets for the fuzzy logic system. A way to avoid the explosion of fuzzy rule bases in fuzzy logic systems is to consider Hierarchical Fuzzy Logic Control (HFLC) [Raju G. V. S. and Zhou, J. (1993)]. A learning approach based on genetic algorithms [Goldberg, D. (1989)] is discussed in this paper for the determination of the rule bases of hierarchical fuzzy logic systems.

APPLICABILITY OF FUZZY LOGIC TO THE DEFINITION OF THE BUCKLING LENGTH OF STEEL PLANE FRAMES

2009 ◽  
Vol 1 (3) ◽  
pp. 135-140
Author(s):  
Zdeněk Kala ◽  
Abayomi Omishore ◽  
Libor Puklický
Keyword(s):  
Fuzzy Logic ◽  
Input Data ◽  
Joint Stiffness ◽  
Order Theory ◽  
Extension Principle ◽  
Shape Functions ◽  
Plane Frames ◽  
Modelling Uncertainties ◽  
Definition Of ◽  
Buckling Length

This paper presents a fuzzy-logic-based approach to the definition of buckling length evoked by the uncertainty of joint stiffness and boundary conditions for the member support. The example demonstrates a lucid and specific application of fuzzy sets in modelling uncertainties in design. To carry out analysis, the extension principle in the form of α-cuts was used. Buckling lengths were analysed utilizing stability solution according to the second-order theory. The beam finite element method with the shape functions of sin and sinh was utilized in the analysis taking into account valuable information on the uncertainties of input data.

scholarly journals LOGIKA FUZZY DALAM TEKNIK PERAMALAN SECARA STATISTIK

2015 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Deddy Barnabas Lasfeto
Keyword(s):  
Fuzzy Logic ◽  
Input Data ◽  
Independent Variable

Selama ini, metode peramalan secara konvensional yang digunakan adalah analisis regresi. Oleh karena itu, dicoba untuk dibandingkan kinerja metode konvensionil dalam hal ini analisis regresi dengan metode sistem cerdas, dalam hal ini adalah logika fuzzy. Dengan dapat  dianalisisnya kinerja kedua sistem peramalan tersebut, maka user dapat memilih metode yang  mana yang sebaiknya digunakan jika melakukan suatu proses peramalan. Tujuan dalam penelitian ini adalah membandingkan kinerja dari fuzzy logic dan regresi dalam analisis peramalan suatu variabel forcastum dari satu atau lebih variabel bebas (independent variable). Input yang diperlukan yang sesuai yaitu mengisi ukuran range dan memilih type fungsi keanggotaan input X1, X2, X3, …., Xn serta parameter yang diperlukan. Hal serupa juga dilakukan untuk variabel tak bebas Y. Hal yang sama input X1, X2 dan Y untuk menentukan peramalan dengan regresi berganda. Input X1, X2 dan Y secara simulasi akan ditampilkan. Untuk Melihat kinerja secara keseluruhan dalam teknik peramalan Untuk regresi berganda ini, baik secara fuzzy maupun dengan teknik konvensional maka, dihitung nilai kesalahan rata-rata yang berdsarkan kesalahan relative masing-masing y Untuk setiap input data. Dari hasil analisis ini, diperoleh bahwa pada regresi berganda, nilai kesalahan relative rata-rata pada metode Fuzzy sedikit lebih besar dibandingkan dengan metode regresi konvensional, yakni sebesar 3%. Dapat dikatakan bahwa Metode regresi konvensional lebih baik dibandingkan dengan metode fuzzy dalam tekni peramalan ini.

scholarly journals GIS and Remote Sensing for Malaria Risk Mapping, Ethiopia

2014 ◽  
Vol XL-8 ◽  
pp. 155-161 ◽  
Author(s):  
A. Ahmed
Keyword(s):  
At Risk ◽  
Land Use ◽  
Land Cover ◽  
Malaria Risk ◽  
Hazard Map ◽  
Land Use Land Cover ◽  
Risk Map ◽  
Risk Area ◽  
Element At Risk ◽  
Very High

Integrating malaria data into a decision support system (DSS) using Geographic Information System (GIS) and remote sensing tool can provide timely information and decision makers get prepared to make better and faster decisions which can reduce the damage and minimize the loss caused. This paper attempted to asses and produce maps of malaria prone areas including the most important natural factors. The input data were based on the geospatial factors including climatic, social and Topographic aspects from secondary data. The objective of study is to prepare malaria hazard, Vulnerability, and element at risk map which give the final output, malaria risk map. The malaria hazard analyses were computed using multi criteria evaluation (MCE) using environmental factors such as topographic factors (elevation, slope and flow distance to stream), land use/ land cover and Breeding site were developed and weighted, then weighted overlay technique were computed in ArcGIS software to generate malaria hazard map. The resulting malaria hazard map depicts that 19.2 %, 30.8 %, 25.1 %, 16.6 % and 8.3 % of the District were subjected to very high, high, moderate, low and very low malaria hazard areas respectively. For vulnerability analysis, health station location and speed constant in Spatial Analyst module were used to generate factor maps. For element at risk, land use land cover map were used to generate element at risk map. Finally malaria risk map of the District was generated. Land use land cover map which is the element at risk in the District, the vulnerability map and the hazard map were overlaid. The final output based on this approach is a malaria risk map, which is classified into 5 classes which is Very High-risk area, High-risk area, Moderate risk area, Low risk area and Very low risk area. The risk map produced from the overlay analysis showed that 20.5 %, 11.6 %, 23.8 %, 34.1 % and 26.4 % of the District were subjected to very high, high, moderate, low and very low malaria risk respectively. This help to plan valuable measures to be taken in early warning, monitor, control and prevent malaria epidemics.

scholarly journals A physical approach on flood risk vulnerability of buildings

2014 ◽  
Vol 11 (2) ◽  
pp. 1411-1460 ◽  
Author(s):  
B. Mazzorana ◽  
S. Simoni ◽  
C. Scherer ◽  
B. Gems ◽  
S. Fuchs ◽  
...  
Keyword(s):  
At Risk ◽  
Residential Buildings ◽  
Dynamic Assessment ◽  
Building Envelope ◽  
Economic Damage ◽  
Assessment Procedure ◽  
Element At Risk ◽  
Vulnerability Of Buildings ◽  
The Impact ◽  
Vulnerability Functions

Abstract. The design of efficient hydrological risk mitigation strategies and their subsequent implementation relies on a careful vulnerability analysis of the elements exposed. Recently, extensive research efforts were undertaken to develop and refine empirical relationships linking the structural vulnerability of buildings to the impact forces of the hazard processes. These empirical vulnerability functions allow estimating the expected direct losses as a result of the hazard scenario based on spatially explicit representation of the process patterns and the elements at risk classified into defined typological categories. However, due to the underlying empiricism of such vulnerability functions, the physics of the damage generating mechanisms for a well-defined element at risk with its peculiar geometry and structural characteristics remain unveiled, and, as such, the applicability of the empirical approach for planning hazard-proof residential buildings is limited. Therefore, we propose a conceptual assessment scheme to close this gap. This assessment scheme encompasses distinct analytical steps: modelling (a) the process intensity, (b) the impact on the element at risk exposed and (c) the physical response of the building envelope. Furthermore, these results provide the input data for the subsequent damage evaluation and economic damage valuation. This dynamic assessment supports all relevant planning activities with respect to a minimisation of losses, and can be implemented in the operational risk assessment procedure.

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