scholarly journals Prediction of Municipal Solid Waste Generation for Developing Countries in Temporal Scale: A Fuzzy Inference System Approach

2017 ◽  
Vol 19 (3) ◽  
pp. 511-520 ◽  
Keyword(s):  
Municipal Solid Waste ◽  
Solid Waste ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Temporal Scale ◽  
Age Group ◽  
Consumer Expenditure ◽  
Waste Generation ◽  
Inference System ◽  
Input Variables

Fuzzy Inference System (FIS) based prediction models for the Municipal Solid Waste (MSW) generation has been developed in the present work to study the influences of total population, percapita annual income, literacy rate, age group and monthly consumer expenditure on temporal variability of MSW generation for Kolhapur city, India. Ten models were developed considering two input variables at a time to study the effect of the socioeconomic and demographic parameters on MSW generation. Finally, all five input variables were considered in a single model to predict MSW generation in a temporal scale. Result shows that, the model with input variables consumer expenditure and age group was best fitted with highest coefficient of determination (0.985) value and lowest standard error of the estimate (1.562) value for the modelling period. For the design period, models related to consumer expenditure show higher waste generation. Models related to population and age show prediction similar to ‘Kolhapur Municipal Corporations’ prediction. However model with input literacy and income shows very low waste generation prediction. The proposed modelling technique is very useful in MSW generation prediction for a temporal scale in uncertain and random environment globally.

Various Models Used in Analysing Municipal Solid Waste Generation–A Review

2021 ◽  
Vol 47 (3) ◽  
pp. 569-578
Author(s):  
Rashmi Srinivasaiah ◽  
Devappa Renuka Swamy ◽  
Aswin S. Krishna ◽  
Chandrashekar Vinayak Airsang ◽  
Dinesh C. Reddy ◽  
...  
Keyword(s):  
Municipal Solid Waste ◽  
Solid Waste ◽  
Fuzzy Inference ◽  
Sample Survey ◽  
Waste Generation ◽  
Linear Regression Models ◽  
Inference System ◽  
Artificial Intelligence Models ◽  
Solid Waste Generation ◽  
Conventional Models

At present, factors such as growth in population, economic development, urbanization and improved standard of living increase the quantity and complexity of generated Municipal Solid Waste. The different approaches for developing models for forecasting municipal solid waste generation have been classified into conventional and non-conventional or artificial intelligence models. While the conventional models include sample survey, system dynamics, econometric models, time series analysis, factor driven models and multiple linear regression models, the non-conventional models include artificial neural networks, Fuzzy logic models and Adaptive Neuro Fuzzy Inference System models. In this review, various factors considered for modelling, locations of study, sources of data and various studies conducted by researchers have been tabulated in detail for identifying the major factors and models used in developed and developing countries. Non-conventional models are being preferred because of their capacity to analyse dynamic data and for their prediction accuracy.

scholarly journals Prediction of dissolved oxygen in reservoirs using adaptive network-based fuzzy inference system

2011 ◽  
Vol 14 (1) ◽  
pp. 167-179 ◽  
Author(s):  
Vesna Ranković ◽  
Jasna Radulović ◽  
Ivana Radojević ◽  
Aleksandar Ostojić ◽  
Ljiljana Čomić
Keyword(s):  
Experimental Data ◽  
Water Quality ◽  
Dissolved Oxygen ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Adaptive Network ◽  
Inference System ◽  
Fuzzy Models ◽  
Highly Nonlinear ◽  
Input Variables

Predicting water quality is the key factor in the water quality management of reservoirs. Since a large number of factors affect the water quality, traditional data processing methods are no longer good enough for solving the problem. The dissolved oxygen (DO) level is a measure of the health of the aquatic system and its prediction is very important. DO dynamics are highly nonlinear and artificial intelligence techniques are capable of modelling this complex system. The objective of this study was to develop an adaptive network-based fuzzy inference system (ANFIS) to predict the DO in the Gruža Reservoir, Serbia. The fuzzy model was developed using experimental data which were collected during a 3-year period. The input variables analysed in this paper are: water pH, water temperature, total phosphate, nitrites, ammonia, iron, manganese and electrical conductivity. The selection of an appropriate set of input variables is based on the building of ANFIS models for each possible combination of input variables. Results of fuzzy models are compared with measured data on the basis of correlation coefficient, mean absolute error and mean square error. Comparing the predicted values by ANFIS with the experimental data indicates that fuzzy models provide accurate results.

scholarly journals A Fuzzy Inference System for Seagrass Distribution Modeling in the Mediterranean Sea: A First Approach

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2949
Author(s):  
Dimitra Papaki ◽  
Nikolaos Kokkos ◽  
Georgios Sylaios
Keyword(s):  
Chlorophyll A ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Climate Change Scenarios ◽  
Public Authorities ◽  
Sea Temperature ◽  
Inference System ◽  
Chl A ◽  
Seagrass Ecosystems ◽  
Input Variables

A Mamdani-type fuzzy-logic model was developed to link Mediterranean seagrass presence to the prevailing environmental conditions. UNEP-WCMC (seagrass presence), CMEMS, and EMODnet (oceanographic/environmental) datasets, along with human-impact parameters were utilized for this expert system. The model structure and input parameters were tested according to their capacity to accurately predict the presence of seagrass families at specific locations. The optimum Fuzzy Inference System (FIS) comprised four input variables: water depth, sea surface temperature, nitrates, and bottom chlorophyll-a concentration, exhibiting reasonable precision (76%). Results illustrated that Posidoniaceae prefers cooler water (16–18 °C) with low chlorophyll-a levels (<0.2 mg/m3); Zosteraceae favors similarly cooler (16–18 °C) and mesotrophic waters (Chl-a > 0.2 mg/m3), but also slightly warmer (18–19.5 °C) with lower Chl-a levels (<0.2 mg/m3); Cymodoceaceae lives in warm, oligotrophic (19.5–21.0 °C, Chl-a < 0.3 mg/m3) to moderately warm mesotrophic sites (18–21.3 °C, 0.3–0.4 mg/m3 Chl-a). Finally, Hydrocharitaceae thrives in the warm Mediterranean waters (21–23 °C) of low chlorophyll-a content (<0.25 mg/m3). Climate change scenarios show that Posidoniaceae and Zosteraceae tolerate bathymetric changes, and Posidoniaceae and Zosteraceae are mostly affected by sea temperature rise, while Hydrocharitaceae exhibits tolerance at higher sea temperatures. This FIS could aid the protection of vulnerable seagrass ecosystems by national and regional policy-makers and public authorities.

scholarly journals Examining child obesity risk level using fuzzy inference system

2021 ◽  
Vol 10 (3) ◽  
pp. 679
Author(s):  
Febrina Sari ◽  
Desyanti Desyanti ◽  
Teuku Radillah ◽  
Siti Nurjannah ◽  
Julimar Julimar ◽  
...  
Keyword(s):  
Childhood Obesity ◽  
Fuzzy Inference System ◽  
Fast Food ◽  
Fuzzy Inference ◽  
Risk Level ◽  
Food History ◽  
Inference System ◽  
Input Variables ◽  
Level Of Risk ◽  
Obesity In Children

The doctor will determine the risk level of childhood obesity by using standard calculations, namely measuring the child's weight and height, and many other factors. Then the doctor will calculate the child's body mass index (BMI). The results of calculations made by the doctor will be compared with standard/normal values set by FAO/WHO, to obtain the level of risk of obesity in children. This study aims to analyze the risk level of obesity in children using the Sugeno method of Fuzzy Inference system, using the trapezoidal membership function and involving six input variables such as exercise habits, consumption of fast food, history of obesity of parents, and others. The application of the fuzzy inference system Sugeno method can help doctors to analyze the risk level of childhood obesity quickly and accurately with an accuracy rate of 85%. The results of the implementation of the Sugeno method of Fuzzy Inference system showed that out of 140 children who were the object of the study, 119 children received a diagnosis of the level of risk of obesity which was the same as the diagnosis made by a doctor.

A Classification Approach Using Fuzzy Inference System for Non-Proliferative Diabetic Retinopathy

2020 ◽  
Vol 10 (11) ◽  
pp. 2577-2587
Author(s):  
K. R. Remya ◽  
M. N. Giriprasad
Keyword(s):  
Diabetic Retinopathy ◽  
Proliferative Diabetic Retinopathy ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Classification Methods ◽  
Inference System ◽  
Classification Approach ◽  
Classification Technique ◽  
Input Variables

This paper proposes a classification technique using fuzzy inference system (FIS) for non-proliferative diabetic retinopathy (NPDR). The input variables to FIS are the extracted features from the pathologies of NPDR. Abnormalities like exudates and microaneuryms are segmented for feature extraction. The pathological aspects of NPDR leads to fuzzy if-then rules that effectively handles the fuzziness present in some of the features. The role of FIS in NPDR classification replaces the training phase in learning based classification methods. The performance of the proposed fuzzy approach is analyzed for stages of NPDR and diabetic retinopathy classification on various databases. The accuracy of 98.2% is observed for NPDR classification in Messidor database.

scholarly journals Model Fuzzy Mamdani Untuk Penilaian Tingkat Kepuasan Pelayanan Pengaduan Masyarakat

2018 ◽  
Vol 5 (2) ◽  
pp. 237-247 ◽  
Author(s):  
Martin Martin ◽  
Lala Nilawati
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Comfort Level ◽  
System Model ◽  
Service Satisfaction ◽  
Competitive Advantages ◽  
Inference System ◽  
Service Levels ◽  
Input Variables ◽  
The Government

AbstrakKualitas pelayanan adalah salah satu keunggulan kompetitif, karena pelayanan yang baik adalah salah satu faktor dasar yang mampu mempengaruhi tingkat kenyamanan penerima layanan. Pelayanan publik oleh aparatur pemerintah dewasa ini masih banyak dijumpai kelemahan, sehingga belum dapat memenuhi kualitas yang diharapkan masyarakat. Penelitian ini ditujukan untuk melihat seberapa besar kepuasan pelayanan, dan pengaruh tingkat pelayanan terhadap tingkat kepuasan berdasarkan Logika Fuzzy Inference System Model Mamdani. Ada empat variabel input yang digunakan yaitu kejelasan informasi, kejelasan persyaratan, kemampuan petugas dan ketersediaan sarana dan prasarana untuk menghasilkan output kepuasan pelayanan. Berdasarkan tahapan-tahapan menggunakan Logika Fuzzy Inference System Model Mamdani mulai dari pembentukan himpunan fuzzy, aplikasi fungsi impilkasi, komposisi aturan sampai proses penegasan (defuzzyfikasi), dapat dibuktikan adanya korelasi antara variabel-variabel input sehingga dapat menentukan output hasil kepuasan pelayanan. Hasil penelitian ini diharapkan dapat digunakan oleh pihak instansi, sebagai pendukung sistem keputusan terhadap hasil penilaian yang diberikan oleh masyarakat untuk pelayanan yang dirasakan. Pengembangan penelitian ini kedepan nya akan diuji coba kembali dengan menambahkan lebih banyak variabel dan akan dibuat sebuah interface untuk memudahkan pemprosesan hasil penilaian kualitas pelayanan pengaduan masyarakat.  Kata Kunci: Pelayanan, Fuzzy Mamdani, Logika Fuzzy.AbstractService quality is one of the competitive advantages, because good service is one of the basic factors that can affect the comfort level of service recipients. Public services by the government apparatus today are still often found to be weak, so that they cannot meet the quality expected by the community. This study is intended to see how much service satisfaction is, and the effect of service levels on satisfaction levels based on Mamdani Model Fuzzy Inference System Logic. There are four input variables used namely clarity of information, clarity of requirements, ability of officers and availability of facilities and infrastructure to produce service satisfaction output. Based on the stages using Mamdani Model Fuzzy Inference System Logic starting from the formation of fuzzy sets, application of the implementation function, composition of the rules until the confirmation process (defuzzyfication), it can be proved the correlation between input variables so that it can determine the output of service satisfaction. The results of this study are expected to be used by the agency, as a support system for the decision on the results of the assessment given by the community for perceived services. The future development of this research will be re-tested by adding more variables and an interface will be created to facilitate the processing of the results of the quality assessment of public complaints services. Keywords: Service, Fuzzy Mamdani, Fuzzy Logic.

scholarly journals Combined particle swarm optimization and fuzzy inference system model for estimation of current-induced scour beneath marine pipelines

2010 ◽  
Vol 13 (3) ◽  
pp. 558-573 ◽  
Author(s):  
M. Zanganeh ◽  
A. Yeganeh-Bakhtiary ◽  
R. Bakhtyar
Keyword(s):  
Particle Swarm Optimization ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Subtractive Clustering ◽  
Scour Depth ◽  
Swarm Optimization ◽  
Inference System ◽  
Input Variables

In this paper the capability of Particle Swarm Optimization (PSO) is employed to deal with an Adaptive Network based Fuzzy Inference System (ANFIS) model's inherent shortcomings to extract optimum fuzzy if–then rules in noisy areas arising from the application of nondimensional variables to estimate scour depth. In the model, a PSO algorithm is employed to optimize the clustering parameters controlling fuzzy if–then rules in subtractive clustering while another PSO algorithm is employed to tune the fuzzy rule parameters associated with the fuzzy if–then rules. The PSO model's objective function is the Root Mean Square (RMSE), by which the model attempts to minimize the error in scour depth estimation with respect to its generalization capability. To evaluate the model's performance, the experimental datasets are used as training, checking and testing datasets. Two-dimensional and nondimensional models are developed such that in the dimensional model the mean current velocity, mean grain size, water depth, pipe diameter and shear boundary velocity are used as input variables while in the nondimensional model the pipe, boundary Reynolds numbers, Froude number and normalized depth of water are set as input variables. The results show that the model provides an alternative approach to the conventional empirical formulae. It is evident that the developed PSO–FIS–PSO is superior to the ANFIS model in the noisy area in which the input and output variables are slightly related to each other.

scholarly journals Adaptive-network-based fuzzy inference system (ANFIS) modelbased prediction of the surface ozone concentration

2014 ◽  
Vol 79 (10) ◽  
pp. 1323-1334 ◽  
Author(s):  
Marija Savic ◽  
Ivan Mihajlovic ◽  
Milica Arsic ◽  
Zivan Zivkovic
Keyword(s):  
Ozone Concentration ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Surface Ozone ◽  
Ambient Air ◽  
Adaptive Network ◽  
Inference System ◽  
Bivariate Correlation ◽  
Wide Range ◽  
Input Variables

This paper presents the results of the tropospheric ozone concentration modeling as the dependence on volatile organic compounds - VOCs (Benzene, Toluene, m,p-Xylene, o-Xylene, Ethylbenzene); nonorganic compounds - NOx (NO, NO2, NOx, CO, H2S, SO2 and PM10) in the ambient air in parallel with the meteorological parameters: temperature, solar radiation, relative humidity, wind speed and direction. Modeling is based on measured results obtained during the year 2009. The measurements were performed at the measuring station located within an agricultural area, in vicinity of city of Zrenjanin (Serbian Banat, Serbia). Statistical analysis of obtained data, based on bivariate correlation analysis indicated that accurate modeling cannot be performed using linear statistics approach. Also, considering that almost all input variables have wide range of relative change (ratio of variance compared to range), nonlinear statistic analysis method based on only one rule describing the behavior of input variable, most certainly wouldn?t present accurate enough results. From that reason, modeling approach was based on Adaptive-Network-Based Fuzzy Inference System (ANFIS). Model obtained using ANFIS methodology resulted with high accuracy, with prediction potential of above 80%, considering that obtained determination coefficient for the final model was R2=0.802.

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