scholarly journals An ecohydraulic-based expert system for optimal management of environmental flow at the downstream of reservoirs

2021 ◽  
Author(s):  
Mahdi Sedighkia ◽  
Bithin Datta ◽  
Asghar Abdoli ◽  
Zahra Moradian
Keyword(s):  
Expert System ◽  
Water Supply ◽  
Fuzzy Inference ◽  
Differential Evolution Algorithm ◽  
Water Quality Assessment ◽  
Environmental Flow ◽  
Physical Habitat ◽  
Inference System ◽  
Suitability Assessment ◽  
Inference Systems

Abstract Linking ecohydraulic modeling and reservoir operation optimization is a requirement for robust management of the environmental degradations at the downstream of the reservoirs. The present study proposes and evaluates an ecohydraulic-based expert system to optimize environmental flow at the downstream of the reservoirs. Three fuzzy inference systems including physical habitat assessment, water quality assessment and combined suitability assessment were developed based on the expert panel method. Moreover, water temperature and dissolved oxygen were simulated by the coupled particle swarm optimization (PSO)–adaptive neuro-fuzzy inference system. Three evolutionary algorithms including PSO, differential evolution algorithm (DE) and biogeography-based optimization were applied to optimize the environmental flow regime. A fuzzy technique for order of preference by similarity to ideal solution was applied to select the best evolutionary algorithm to assess environmental flow. Based on the results in the case study, the proposed method provides a robust framework for simultaneous management of environmental flow and water supply. DE was selected as the best algorithm to optimize environmental flow. The optimization system was able to balance habitat losses, storage loss and water supply loss that might reduce negotiations between the stakeholders and environmental managers in the reservoir management.

scholarly journals Utilization of Current Data for Geospatial Analysis of the Appropriateness of Apple Plantation Land Based on Fuzzy Inference Systems

2019 ◽  
Vol 4 (1) ◽  
pp. 64
Author(s):  
Prayudi Lestantyo
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Geospatial Analysis ◽  
Current Data ◽  
Inference System ◽  
Apple Industry ◽  
Accuracy Test ◽  
Spatial Factor ◽  
Apple Production ◽  
Inference Systems

Apple is a high-value import fruit in Indonesia. One of the Apple production centers in Indonesia is Batu City, but the results tend to be declining in every year. To fulfill the demand of domestic apple industry, it is than a must to open new plantation land by observing the spatial factor. Expert and direct field review are needed to perform the analysis of land suitability, so that it will takes a lot of time and effort. Therefore, a smart system that can conduct geospatial analysis by using fuzzy inference system is developed. The data was obtained by using satellite imagery, data interpolation, and digitized and then analyzed into information. The analysis was performed on each pixel with six variable inputs including altitude, rainfall, humidity, air temperature, soil type and sun shine intensity. Besides that, the five-clustering output makes the results more accurate. From the results of the accuracy test, it is obtained a 92,86% accuracy, by comparing the results of the spatial analysis using fuzzy inference system with direct review on the field.

Fuzzy Logic-Based Inference Systems

2015 ◽  
pp. 209-239
Keyword(s):  
Fuzzy Logic ◽  
Knowledge Representation ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Mathematical Formulation ◽  
Cognitive Map ◽  
Fuzzy Cognitive Map ◽  
Adaptive Networks ◽  
Inference System ◽  
Inference Systems

This chapter presents the mathematical formulation of the fuzzy logic-based inference systems, used as means to infer about the response of ill-conditioned systems, based on the field knowledge representation in the fuzzy world. Particular approaches are explored, e.g., Fuzzy Inference System (FIS), Adaptive Networks-based FIS (ANFIS), Intuitionistic FIS (IFIS) and Fuzzy Cognitive Map (FCM), surfacing their potentialities in modeling applications, such as those in the field of learning, examined in the chapters of Part III that follow.

scholarly journals Water Supply Pipeline Risk Index Assessment Based on Cohesive Hierarchical Fuzzy Inference System

Processes ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 182 ◽  
Author(s):  
Muhammad Fayaz ◽  
Shabir Ahmad ◽  
Lei Hang ◽  
DoHyeun Kim
Keyword(s):  
Water Supply ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Residential Buildings ◽  
Risk Index ◽  
Inference System ◽  
Full Structure ◽  
Implementation Time ◽  
Novel Method ◽  
Water Supply Pipeline

As populations grow, facilities such as roads, bridges, railways lines, commercial and residential buildings, etc., must be expanded and maintained. There are extensive networks of underground facilities to fulfil the demand, such as water supply pipelines, sewage pipelines, metro structures, etc. Hence, a method to regularly assesses the risk of the underground facility failures is needed to decrease the chance of accidental loss of service or accidents that endanger people and facilities. In the proposed work, a cohesive hierarchical fuzzy inference system (CHFIS) was developed. A novel method is proposed for membership function (MF) determination called the heuristic based membership functions determination (HBMFD) method to determine an appropriate MF set for each fuzzy logic method in CHFIS. The proposed model was developed to decrease the number of rules for the full structure fuzzy inference system with all rule implementation. Four very crucial parameters were considered in the proposed work that are inputs to the proposed CHFIS model in order to calculate the risk of water supply pipelines. In order to fully implement the proposed CHFIS just 85 rules are needed while using the traditional Mamdani fuzzy inference system, 900 rules are required. The novel method greatly reduces implementation time and rule design sets that are extremely time consuming to develop and difficult to maintain.

Artificial neural networks and adaptive neuro-fuzzy inference systems for parameter identification of dynamic systems

2020 ◽  
Vol 39 (5) ◽  
pp. 6145-6155
Author(s):  
Ramin Vatankhah ◽  
Mohammad Ghanatian
Keyword(s):  
Differential Equations ◽  
Dynamic Systems ◽  
Fuzzy Inference ◽  
Dynamic Equations ◽  
Control Analysis ◽  
Unknown Parameters ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Artificial Neural ◽  
Inference Systems

There would always be some unknown geometric, inertial or any other kinds of parameters in governing differential equations of dynamic systems. These parameters are needed to be numerically specified in order to make these dynamic equations usable for dynamic and control analysis. In this study, two powerful techniques in the field of Artificial Intelligence (AI), namely Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are utilized to explain how unknown parameters in differential equations of dynamic systems can be identified. The data required for training and testing the ANN and the ANFIS are obtained by solving the direct problem i.e. solving the dynamic equations with different known parameters and input stimulations. The governing ordinary differential equations of the system is numerically solved and the output values in different time steps are obtained. The output values of the system and their derivatives, the time and the inputs are given to the ANN and the ANFIS as their inputs and the unknown parameters in the dynamic equations are estimated as the outputs. Finally, the performances of the ANN and the ANFIS for identifying parameters of the system are compared based on the test data Percent Root Mean Square Error (% RMSE) values.

Demand forecasting in the beauty industry using fuzzy inference systems

2020 ◽  
Vol 15 (4) ◽  
pp. 1389-1417
Author(s):  
Ricardo Felicio Souza ◽  
Peter Wanke ◽  
Henrique Correa
Keyword(s):  
Time Series ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Theory And Practice ◽  
Forecast Errors ◽  
Fuzzy Inference Systems ◽  
Inference System ◽  
Content Type ◽  
Forecasting Methods ◽  
Inference Systems

Purpose This study aims to analyze the performance of four different fuzzy inference system-based forecasting tools using a real case company. Design/methodology/approach The forecasting tools were tested using 27 products of the nail polish line of a multinational beauty company and the performance of said tools was compared to those of the company’s previous forecasting methods that were basically qualitative (informal and intuition-based). Findings The performance of the methods analyzed was compared by using mean absolute percentage error. It was possible to determine the characteristics and conditions that make each model the best for each situation. The main takeaways were that low kurtosis, negatively skewed demand time-series and longer horizon forecasts that favor the fuzzy inference system-based models. Besides, the results suggest that the fuzzy forecasting tools should be preferred for longer horizon forecasts over informal qualitative methods. Originality/value Notwithstanding the proposed hybrid modeling approach based on fuzzy inference systems, our research offers a relevant contribution to theory and practice by shedding light on the segmentation and selection of forecasting models, both in terms of time-series characteristics and forecasting horizon. The proposed fuzzy inference systems showed to be particularly useful not only when time-series distributions present no clear central tendency (that is, they are platykurtic or dispersed around a large plateau around the median, which is the characteristic of negative kurtosis), but also when mode values are greater than median values, which in turn are greater than mean values. This large tail to the left (negative skewness) is typical of successful products whose sales are ramping up in early stages of their life cycle. For these, fuzzy inference systems may help managers screen out forecast bias and, therefore, lower forecast errors. This behavior also occurs when managers deal with forecasts of longer horizons. The results suggest that further research on fuzzy inference systems hybrid approaches for forecasting should emphasize short-term forecasting by trying to better capture the “tribal” managerial knowledge instead of focusing on less dispersed and slower moving products, where the purely qualitative forecasting methods used by managers tend to perform better in terms of their accuracy.

scholarly journals Artificial Hydrocarbon Networks Fuzzy Inference System

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Hiram Ponce ◽  
Pedro Ponce ◽  
Arturo Molina
Keyword(s):  
Fuzzy Inference ◽  
Fuzzy Inference Systems ◽  
Inference Model ◽  
Inference System ◽  
Position Controller ◽  
Inference Systems ◽  
Linguistic Units ◽  
Output Space

This paper presents a novel fuzzy inference model based on artificial hydrocarbon networks, a computational algorithm for modeling problems based on chemical hydrocarbon compounds. In particular, the proposed fuzzy-molecular inference model (FIM-model) uses molecular units of information to partition the output space in the defuzzification step. Moreover, these molecules are linguistic units that can be partially understandable due to the organized structure of the topology and metadata parameters involved in artificial hydrocarbon networks. In addition, a position controller for a direct current (DC) motor was implemented using the proposed FIM-model in type-1 and type-2 fuzzy inference systems. Experimental results demonstrate that the fuzzy-molecular inference model can be applied as an alternative of type-2 Mamdani’s fuzzy control systems because the set of molecular units can deal with dynamic uncertainties mostly present in real-world control applications.

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