scholarly journals GIS-based comparative assessment of flood susceptibility mapping using hybrid multi-criteria decision-making approach, naïve Bayes tree, bivariate statistics and logistic regression: A case of Topľa basin, Slovakia

2020 ◽  
Vol 117 ◽  
pp. 106620 ◽  
Author(s):  
Sk Ajim Ali ◽  
Farhana Parvin ◽  
Quoc Bao Pham ◽  
Matej Vojtek ◽  
Jana Vojteková ◽  
...  
Keyword(s):  
Decision Making ◽  
Logistic Regression ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Comparative Assessment ◽  
Susceptibility Mapping ◽  
Multi Criteria Decision Making ◽  
Bivariate Statistics ◽  
Flood Susceptibility ◽  
Flood Susceptibility Mapping

scholarly journals A Hybrid GIS Multi-Criteria Decision-Making Method for Flood Susceptibility Mapping at Shangyou, China

2018 ◽  
Vol 11 (1) ◽  
pp. 62 ◽  
Author(s):  
Yi Wang ◽  
Haoyuan Hong ◽  
Wei Chen ◽  
Shaojun Li ◽  
Dragan Pamučar ◽  
...  
Keyword(s):  
Decision Making ◽  
Area Under The Curve ◽  
Analytic Network Process ◽  
Susceptibility Mapping ◽  
Collective Decision ◽  
Susceptibility Map ◽  
Hybrid Technique ◽  
Collective Decision Making ◽  
Flood Susceptibility ◽  
Flood Susceptibility Mapping

Floods are considered one of the most disastrous hazards all over the world and cause serious casualties and property damage. Therefore, the assessment and regionalization of flood disasters are becoming increasingly important and urgent. To predict the probability of a flood, an essential step is to map flood susceptibility. The main objective of this work is to investigate the use a novel hybrid technique by integrating multi-criteria decision analysis and geographic information system to evaluate flood susceptibility mapping (FSM), which is constructed by ensemble of decision making trial and evaluation laboratory (DEMATEL), analytic network process, weighted linear combinations (WLC) and interval rough numbers (IRN) techniques in the case study at Shangyou County, China. Specifically, we improve the DEMATEL method by applying IRN to determine connections in the network structure based on criteria and to accept imprecisions during collective decision making. The application of IRN can eliminate the necessity of additional information to define uncertain number intervals. Therefore, the quality of the existing data during collective decision making and experts’ perceptions that are expressed through an aggregation matrix can be retained. In this work, eleven conditioning factors associated with flooding were considered and historical flood locations were randomly divided into the training (70% of the total) and validation (30%) sets. The flood susceptibility map validates a satisfactory consistency between the flood-susceptible areas and the spatial distribution of the previous flood events. The accuracy of the map was evaluated by using objective measures of receiver operating characteristic (ROC) curve and area under the curve (AUC). The AUC values of the proposed method coupling with the WLC fuzzy technique for aggregation and flood susceptibility index are 0.988 and 0.964, respectively, which proves that the WLC fuzzy method is more effective for FSM in the study area. The proposed method can be helpful in predicting accurate flood occurrence locations with similar geographic environments and can be effectively used for flood management and prevention.

Sistem Rekomendasi Pertemanan berdasarkan Hobi menggunakan Metode Multicriteria Decision Making

2020 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Alviana Dina Putri ◽  
Ajib Susanto
Keyword(s):  
Decision Making ◽  
Naive Bayes ◽  
Multicriteria Decision Making ◽  
Naïve Bayes ◽  
Multi Criteria Decision Making ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Multicriteria Decision

Hobi pada tahun 1816 mulai dikenalkan hanya dalam kosakata di kalangan sejumlah orang inggris. Istilah hobi pada abad itu diartikan dengan waktu senggang. Namun pada saat ini, mempresentasikan hobi dapat dikategorikan menjadi untuk memenuhi hasrat semata , menambah pengetahuan dan mengembangkan ke dalam dunia bisnis. Media twitter adalah salah satu media pendukung yang sering digunakan banyak orang didalam mempresentasikan hobi seseorang, dengan melihat siapa yang diikuti dan berdasarkan tweet seseorang tersebut. Bisa diklasifikasikan bahwa orang tersebut dikategorikan memiliki hobi yang sama dengan orang lain. Rekomendasi hybrid filltering adalah metode pendukung didalam proses mendapatkan kelas kategori yang dimiliki oleh seseorang tersebut . Karena sebagian besar sebuah aplikasi yang sudah ada hanya mempresentasikan hobi kedalam aplikasi berupa isian form inputan saja. Dengan menggunakan algoritma naive bayes classifier menjadi solusi baru bagi penulis didalam mengklasifikasikan tweet yang dimiliki user masuk kedalam kategori kelas hobi. Dengan tambahan algoritma Multi-criteria decision making sebagi proses akhir didalam pengelolahan data lanjutan untuk mendapatkan hasil dan hasil tersebut rekomendasikan kepada user lain dengan kategori hobi yang sama. Sehingga user mendapatkan rekomendasi teman sesuai dengan hobi yang sama.

scholarly journals Comparison of Support Vector Machine, Naïve Bayes and Logistic Regression for Assessing the Necessity for Coronary Angiography

2020 ◽  
Vol 17 (18) ◽  
pp. 6449
Author(s):  
Parastoo Golpour ◽  
Majid Ghayour-Mobarhan ◽  
Azadeh Saki ◽  
Habibollah Esmaily ◽  
Ali Taghipour ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Coronary Angiography ◽  
Naive Bayes ◽  
Area Under The Curve ◽  
Naïve Bayes ◽  
Support Vector ◽  
Bayes Model

(1) Background: Coronary angiography is considered to be the most reliable method for the diagnosis of cardiovascular disease. However, angiography is an invasive procedure that carries a risk of complications; hence, it would be preferable for an appropriate method to be applied to determine the necessity for angiography. The objective of this study was to compare support vector machine, naïve Bayes and logistic regressions to determine the diagnostic factors that can predict the need for coronary angiography. These models are machine learning algorithms. Machine learning is considered to be a branch of artificial intelligence. Its aims are to design and develop algorithms that allow computers to improve their performance on data analysis and decision making. The process involves the analysis of past experiences to find practical and helpful regularities and patterns, which may also be overlooked by a human. (2) Materials and Methods: This cross-sectional study was performed on 1187 candidates for angiography referred to Ghaem Hospital, Mashhad, Iran from 2011 to 2012. A logistic regression, naive Bayes and support vector machine were applied to determine whether they could predict the results of angiography. Afterwards, the sensitivity, specificity, positive and negative predictive values, AUC (area under the curve) and accuracy of all three models were computed in order to compare them. All analyses were performed using R 3.4.3 software (R Core Team; Auckland, New Zealand) with the help of other software packages including receiver operating characteristic (ROC), caret, e1071 and rminer. (3) Results: The area under the curve for logistic regression, naïve Bayes and support vector machine were similar—0.76, 0.74 and 0.75, respectively. Thus, in terms of the model parsimony and simplicity of application, the naïve Bayes model with three variables had the best performance in comparison with the logistic regression model with seven variables and support vector machine with six variables. (4) Conclusions: Gender, age and fasting blood glucose (FBG) were found to be the most important factors to predict the result of coronary angiography. The naïve Bayes model performed well using these three variables alone, and they are considered important variables for the other two models as well. According to an acceptable prediction of the models, they can be used as pragmatic, cost-effective and valuable methods that support physicians in decision making.

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