scholarly journals Using a GIS-based order weight Average (OWA) Methods to Predict Suitable Location for Artificial Recharge of Groundwater

2021 ◽  
Author(s):  
Marzieh Mokarram ◽  
Saeed Negahban ◽  
Ali Abdolali ◽  
Mohammad Mehdi Ghasemi
Keyword(s):  
Artificial Recharge ◽  
Regression Method ◽  
Land Suitability ◽  
Effective Parameters ◽  
Ahp Method ◽  
Inference System ◽  
Trade Off ◽  
Subset Regression ◽  
Anfis Method ◽  
Method Show

Abstract The purpose of this study is to use the GIS-based analytic hierarchy process (AHP) and order weight average (OWA) to determine suitable locations for the artificial recharge of groundwater (ARG). Therefore, after preparing the fuzzy maps for each parameter, AHP method is used to pari comparison and determine the weight of each parameter. Then, using the OWA-AHP method based on different levels of confidence (different α values ​​), the weighting is done for each parameter to prepare the final land suitability maps with different risk levels. Also, the adaptive network-based fuzzy inference system (ANFIS) method is used to predict land suitability classes using input parameters. Then, using the Best subset regression method, the most important effective parameters for ARG are identified. The results of the Fuzzy-AHP method show that 27% of the area (in different parts) has good and very good conditions for ARG. The results of the combined OWA-AHP method show that, in case of low-level risk and no trade-off, more area is in very low class (80 %) while in case of the high level of risk and average trade-off, the highest area is in the very low class (27 %). The results of the ANFIS method show that fuzzy c–means (FCM) and sub-clustering methods have high accuracy to predict suitable places for ARG. The results of the best subset regression method show that slope, lithology, land use, and altitude with the lowest Cp values ​ (5.2) are effective parameters to determine ARG.

Penerapan Metode ANFIS untuk Prediksi Nilai Akhir dan Kelulusan Uji Kompetensi

2018 ◽  
Vol 4 (1) ◽  
pp. 21-28
Author(s):  
Rayendra
Keyword(s):  
Word Processing ◽  
Fuzzy Inference ◽  
Percentage Error ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Information And Communication ◽  
Anfis Method ◽  
Final Score ◽  
Computer Literate ◽  
Competence Test

To improve the graduation of Computer Literate Certified Professional (CLCP) competence test conducted by Competence Test of Information and Communication Technology (TUK-TIK) needs to be done continuous improvement by increasing try out competency test. Past values of the competency test can be used as modeling to predict the final score and the passing of the competency test. With the modeling can be predicted the passing of competency test participants through try out-try out done so that can be known weakness of candidate competency test from three units of CLCP competence. The modeling used to predict the final score and the passing of this competency test is the Adaptive Neuro Fuzzy Inference System (ANFIS) method. Used 20 past data of competency test participants with 6 criteria as input value from three CLCP competence units namely Word Processing, Spreadsheet, and Presentation. The resulting prediction is accurate enough with MAPE (Mean Absolute Percentage Error) value for each competency unit of 0.31492%, 0.284202%, and 0.267167%

Using a fuzzy logic decision system to optimize the land suitability evaluation for a sprinkler irrigation method

2018 ◽  
Vol 47 (4) ◽  
pp. 298-307 ◽  
Author(s):  
Yaser Hoseini ◽  
Morteza Kamrani
Keyword(s):  
Parametric Method ◽  
Sprinkler Irrigation ◽  
Land Suitability ◽  
Land Evaluation ◽  
Inference System ◽  
Fuzzy Method ◽  
Suitability Evaluation ◽  
Land Suitability Evaluation ◽  
Study Results ◽  
Significant Difference

Land evaluation for irrigation is the process of predicting land use potential on the basis of soil attributes. The Food and Agriculture Organization (FAO) framework for land suitability evaluation is the most commonly used and is based on the biophysical properties of lands. The FAO framework method for land suitability application Boolean approach that has been criticized by some researchers. Because the Boolean representations ignore the continuous nature of soil and uncertainties in measurement and also its inability for overcoming problems related to vagueness in definition and other uncertainties, fuzzy set methodologies have been proposed. In the present study, the qualitative land suitability evaluation for sprinkler irrigation using parametric-based FAO learning and fuzzy inference system was carried out in an area of 5175 ha in Northwest Iran. By overlaying the layers (soil texture, soil depth, lime, electric conductivity, drainage, and slope) and use of spatial data modeler in ArcGIS 9.3, land evaluation maps for sprinkler irrigation were provided for the area under study. Results showed that based on the parametric approach, 1598 ha of the study area were classified as highly suitable (S1 class) for sprinkler irrigation; the area of highly suitable lands in the parametric method was about five times the area of highly suitable lands obtained through the fuzzy method. In addition, the two methods were completely different in determining moderately suitable lands (S2). Accordingly, 787 ha in the area was moderately suitable using the parametric method, which was about two times that obtained through the fuzzy method. This showed the significant difference between two methods applied to evaluate the lands. Moreover, fuzzy approaches accommodate the continuous nature of some soil properties and produce more intuitive distributions of land suitability indexes.

TODA

2018 ◽  
Vol 10 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Cida Sanches ◽  
Samuel Ferreira Jr ◽  
Givaldo Santos ◽  
Marisa Regina Paixão ◽  
Manuel Meireles
Keyword(s):  
Decision Analysis ◽  
Objective Function ◽  
Analysis Method ◽  
Case Study Method ◽  
Application Process ◽  
Ahp Method ◽  
Trade Off ◽  
Software Application

This paper describes the use and application of the TODA (Trade-off Decision Analysis) method through a case study. The method uses the concept of trade-off applied to a prioritization matrix and, to define the weights, it takes the concept of causality into account. Studies have shown that the TODA achieves the same results as the competing AHP method. However, it is easier to operate. The methodology used is a case study concerning the choice of the type of car for a fleet of vehicles to be driven by salespeople. Together with the software application process, the methods that aided the weighting of the criteria are described and how the values of the alternatives are converted into coefficients of the objective function. The results clearly show that the method is easily applied, but the limitations of the case study method preclude forming generalizations.

Gas-Liquid Phase Recirculation in Bubble Column Reactors: Development of a Hybrid Model Based on Local CFD – Adaptive Neuro-Fuzzy Inference System (ANFIS)

2019 ◽  
Vol 44 (1) ◽  
pp. 29-42 ◽  
Author(s):  
Mashallah Rezakazemi ◽  
Saeed Shirazian
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Bubble Column ◽  
Scale Up ◽  
Flow Simulation ◽  
Continuous Phase ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Anfis Method ◽  
Different Dimensions

Abstract The Euler–Euler method and soft computing methods are recently utilized for the purpose of bubbly flow simulation and evolution of the dispersed and continuous phase in a two-phase reactor. Joining computational fluid dynamics (CFD) to the adaptive neuro-fuzzy inference system (ANFIS) method can enable the researchers to avoid several runs for heavy numerical methods (multidimensional Euler–Euler) to optimize fluid conditions. This overview can also help the researchers to carefully analyze fluid conditions and categorize their huge number of data in their artificial neural network nodes and avoid a complex non-structure CFD mesh. In addition, it can provide a neural geometry without limitation of an increasing mesh number in the fluid domain. In this study, gas and liquid circulation were considered as one of the main CFD factors in the scale-up of reactors used as an output parameter for prediction tool (ANFIS method) in different dimensions. This study shows that a combination of ANFIS and CFD methods provides the non-discrete domain in various dimensions and makes a smart tool to locally predict multiphase flow. The integration of numerical calculation and smart methods also shows that there is a great agreement between CFD results and ANFIS output depending on different dimensions.

scholarly journals Colored object detection using 5 dof robot arm based adaptive neuro-fuzzy method

2019 ◽  
Vol 13 (1) ◽  
pp. 293
Author(s):  
Mujiarto Mujiarto ◽  
Asari Djohar ◽  
Mumu Komaro ◽  
Mohamad Afendee Mohamed ◽  
Darmawan Setia Rahayu ◽  
...  
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Kinematic Model ◽  
Inverse Kinematic ◽  
Robot Arm ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Color Detection ◽  
Anfis Method ◽  
Arduino Microcontroller

<p>In this paper, an Adaptive Neuro Fuzzy Inference System (ANFIS) based on Arduino microcontroller is applied to the dynamic model of 5 DoF Robot Arm presented. MATLAB is used to detect colored objects based on image processing. Adaptive Neuro Fuzzy Inference System (ANFIS) method is a method for controlling robotic arm based on color detection of camera object and inverse kinematic model of trained data. Finally, the ANFIS algorithm is implemented in the robot arm to select objects and pick up red objects with good accuracy.</p>

scholarly journals IMPLEMENTATION OF ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS) METHOD ON RICE PRICE PREDICTION IN LUBUKLINGGAU CITY

2021 ◽  
Vol 4 (2) ◽  
pp. 260-269
Author(s):  
Zulfauzi - ◽  
Budi Santoso ◽  
M. Agus Syamsul Arifin ◽  
Siti Nuraisyah
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
City Area ◽  
Price Prediction ◽  
Neuro Fuzzy ◽  
Rice Price ◽  
The Future ◽  
Anfis Method ◽  
Rice Prices

The problem behind this research is the imbalance between the capacity offered and the capacity demanded by the community, resulting in uncontrolled rice prices, so it is necessary to predict rice price in the future to monitor the stability of rice prices in the Lubuklinggau City area. In this study, the Adaptive Neuro-Fuzzy Inference System (ANFIS) method was used to predict future rice prices. The sample used in this study is data on rice price in Lubuklinggau City from January 2016 to December 2020. The result of the prediction of rice price in the Lubuklinggau City area for the next five years. With the accuracy value in rice price predictions based on MSE training, numely 99,9037% and based on the MSE test that is 99,8784%. While the accuracy values of rice price predictions based on MAPE training and testing are 93,2997% and 88,2782%, respectively. For the accuracy value of rice price prediction result based on the MSE and MAPE values respectively namely 99,8935% and 92,9212%. It can be concluded that the ANFIS method is very effectively used for the process of predicting a price or value in the future

scholarly journals Prototipe Penilaian Kinerja Tenaga Ahli PT. Inacon Luhur Pertiwi Dengan Pendekatan Adaptive Neuro Fuzzy Inference System (ANFIS)

2019 ◽  
Vol 5 (1) ◽  
pp. 108-122
Author(s):  
Handa Gustiawan
Keyword(s):  
Neural Networks ◽  
Data Analysis ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Primary Data ◽  
Inference System ◽  
Analysis Process ◽  
Neuro Fuzzy ◽  
Anfis Method ◽  
Data Reading

Inacon Luhur pertiwi PT. as amanagement consulting firm in carrying outits work on the project PNPM Urban withcontract number HK.02.03 / NMC / IBRD /SATKER-PK / 007/2012 dated 10 May 2012.By carrying out quantitative researchmethods, using primary and secondary dataas samples. Primary data retrieved byconducting an observation as anobservation instrument of expertsperformance assessment. Secondary datawas collected by observing the data,reading, studying and quoting from the bookof literature, as well as the resources thatare closely related to this study. The dataobtained will be used for purposes ofdescriptive data analysis process by usingAdaptive Neuro Fuzzy Inference System(ANFIS). ANFIS method is a method thatuses neural networks to implement fuzzyinference system. Fuzzy inference systemused is the fuzzy inference system modelsTagaki-Sugeno-Kang (TSK) withconsideration of simplicity and easycomputation. The result of this research isthe prototipe of expert performanceevaluation which can be implemented atInacon Luhur Pertiwi PT.

scholarly journals Land Valuation with Analytical Hierarchy Process and Regression Method in Grogol Sub-District, Sukoharjo District

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Arga Fondra Oksaping
Keyword(s):  
Analytical Hierarchy Process ◽  
Regression Method ◽  
Land Values ◽  
Educational Facilities ◽  
Ahp Method ◽  
Transaction Data ◽  
Best Value ◽  
Land Valuation ◽  
Value Model ◽  
Hierarchy Process

Land valuation is a set of processes to determine the value of land plot. In conducting land valuation, it is necessary to considering the factors that affect values of land which caused land values in each region to be different. In order to objective land valuation, it is necessary to analyze the magnitude of factors that influencing the value of land. The Analytical Hierarchy Process (AHP) method is used in this study to analyzing the magnitude value of land in Grogol Sub-district,  Sukoharjo District. Factors used are field distance to CBD, field distance to road, field distance to river, field distance to health facilities, field distance to educational facilities, and land use. The data used in this study were obtined from Sukoharjo Land Office, which is the data of sale and purchase transaction in Grogol Sub-district, Sukoharjo District, totaling 178 data from January to December 2016. Transaction data and factors influence land value are analyzed by Regression Method to obtain the best value model in Grogol Subdistrict, Sukoharjo District.

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