scholarly journals An Intelligent Method for Industrial Location Selection: Application to Markazi Province, Iran

2021 ◽  
Vol 32 (3) ◽  
pp. 267-289
Author(s):  
Hadi Aliverdilou ◽  
Mehran Hajilou ◽  
Hasanali Faraji Sabokbar ◽  
Amin Faraji
Keyword(s):  
Land Use Planning ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Industrial Location ◽  
Inference System ◽  
Location Selection ◽  
Knowledge Based ◽  
Neuro Fuzzy ◽  
Artificial Neural Network Ann ◽  
The Right

Decision-making and selection are important and sensitive aspects of planning. An important part of land-use planning is the location of human activities. Locating activities in the right places determines the future space of a region. Selection and definition of natural and human indices and criteria for location always face uncertainty. Thus, this study aimed to develop an intelligent method for industrial location. In this study a developmental-applied approach was used along with a descriptive-analytical method for data analysis. Through the review of related literature and a Delphi survey, 18 criteria were extracted and 6 main components were categorized. The data were analyzed and modeled by GIS, MATLAB software, and the Fuzzy Inference System (FIS) and Adaptive Neuro-Fuzzy Inference System (ANFIS) methods. For each modeling three industrial domains were extracted, i.e. weak, medium, and premium. A total of 42,968 hectares of premium industrial location with a score higher than 0.7 resulted from combining the produced maps. Other important findings were related to the architecture and methodology applied in the research based on computational intelligence and knowledge-based systems to analyze and understand the processes that influence the score of locations. The novelty of this method lies in the use of high computing power and information evaluation based on artificial intelligence (AI), making it possible to analyze and understand the processes influencing industrial location.   Abstrak. Pengambilan keputusan dan seleksi adalah aspek-aspek penting dan sensitive dalam perencanaan. Bagian yang penting dalam sebuah perencaan penggunaan lahan adalah terkait lokasi kegiatan manusia. Alokasi kegiatan manusia pada tempat yang benar adalah penentu ruang masa depan dari suatu wilayah. Dalam hal seleksi dan definisi index, juga kriteria lokasi selalu menghadapi ketidakpastian. Sehingga, studi ini dilakukan untuk mengembangkan metode yang berguna dalam alokasi industri. Pada artikel ini, digunakan pendekatan terapan-terkembangkan dengan metode analisis deskriptif dalam hal analisis data. Berdasarkan tinjauan pada literatur terkait dan survey Delphi, 18 kritersia diekstraksi yang dikategorikan pada 6 komponen utama. Data dianalisis dan dimodelkan menggunakan GIS, MATLAB, Fuzzy Inference System (FIS), dan metode Adaptive Neuro-Fuzzy Inference System (ANFIS). Untuk setiap model, tiga domain industry ditentukan, yakni: lemah, moderat, dan premium. Terdapat lokasi industry premium dengan total 42,968 ha dengan nilai lebih dari 0.7. Hasil penting lainnya berkaitan dengan arsitektur dan metode terapan dalam penelitian yang berdasar kepada ilmu komputasi untuk memahami proses yang memengaruhi nilai untuk suatu lokasi. Kebaruan dari metode ini ada pada penggunaan model komputasi tinggi dan evaluasi informasi berdasarkan kecerdasan buatan (AI) yang memungkinkan untuk melakukan analisis dan memahami proses yang memengaruhi lokasi industri.   Kata kunci. Fuzzy Inference System (FIS), Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN), lokasi industri, Provinsi Markazi.

scholarly journals Global Image Thresholding Adaptive Neuro-Fuzzy Inference System Trained with Fuzzy Inclusion and Entropy Measures

Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 286 ◽  
Author(s):  
Athanasios Bogiatzis ◽  
Basil Papadopoulos
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Initial Step ◽  
Local Area ◽  
Mathematical Functions ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Entropy Measures ◽  
Complex Procedure ◽  
Artificial Neural Network Ann

Thresholding algorithms segment an image into two parts (foreground and background) by producing a binary version of our initial input. It is a complex procedure (due to the distinctive characteristics of each image) which often constitutes the initial step of other image processing or computer vision applications. Global techniques calculate a single threshold for the whole image while local techniques calculate a different threshold for each pixel based on specific attributes of its local area. In some of our previous work, we introduced some specific fuzzy inclusion and entropy measures which we efficiently managed to use on both global and local thresholding. The general method which we presented was an open and adaptable procedure, it was free of sensitivity or bias parameters and it involved image classification, mathematical functions, a fuzzy symmetrical triangular number and some criteria of choosing between two possible thresholds. Here, we continue this research and try to avoid all these by automatically connecting our measures with the wanted threshold using some Artificial Neural Network (ANN). Using an ANN in image segmentation is not uncommon especially in the domain of medical images. However, our proposition involves the use of an Adaptive Neuro-Fuzzy Inference System (ANFIS) which means that all we need is a proper database. It is a simple and immediate method which could provide researchers with an alternative approach to the thresholding problem considering that they probably have at their disposal some appropriate and specialized data.

Estimating daily pan evaporation from climatic data of the State of Illinois, USA using adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN)

2011 ◽  
Vol 42 (6) ◽  
pp. 491-502 ◽  
Author(s):  
J. Shiri ◽  
W. Dierickx ◽  
A. Pour-Ali Baba ◽  
S. Neamati ◽  
M. A. Ghorbani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Pan Evaporation ◽  
Climatic Parameters ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Daily Pan Evaporation ◽  
Artificial Neural Network Ann

Evaporation is a major component of the hydrological cycle. It is an important aspect of water resource engineering and management, and in estimating the water budget of irrigation schemes. The current work presents the application of adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) approaches for modeling daily pan evaporation using daily climatic parameters. The neuro-fuzzy and neural network models are trained and tested using the data of three weather stations from different geographical positions in the U.S. State of Illinois. Daily meteorological variables such as air temperature, solar radiation, wind speed, relative humidity, surface soil temperature and total rainfall for three years (August 2005 to September 2008) were used for training and testing the employed models. Statistic parameters such as the coefficient of determination (R2), the root mean squared error (RMSE), the variance accounted for (VAF), the adjusted coefficient of efficiency (E1) and the adjusted index of agreement (d1) are used to evaluate the performance of the applied techniques. The results obtained show the feasibility of the ANFIS and ANN evaporation modeling from the available climatic parameters, especially when limited climatic parameters are used.

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