Neuro Fuzzy Application in Capacity Prediction and Forecasting Model for Ukai Reservoir

2017 ◽  
pp. 103-111 ◽  
Author(s):  
Surabhi Saxena ◽  
S. M. Yadav
Keyword(s):  
Forecasting Model ◽  
Neuro Fuzzy ◽  
Prediction And Forecasting ◽  
Capacity Prediction

Transferability of a neuro-fuzzy river ice jam flood forecasting model

2007 ◽  
Vol 48 (3) ◽  
pp. 188-201 ◽  
Author(s):  
C. Mahabir ◽  
F.E. Hicks ◽  
A. Robinson Fayek
Keyword(s):  
Flood Forecasting ◽  
River Ice ◽  
Forecasting Model ◽  
Ice Jam ◽  
Neuro Fuzzy

Air Pollutants and Meteorological Parameters Influence on PM2.5 Forecasting and Performance Assessment of the Developed Artificial Intelligence-Based Forecasting Model

2017 ◽  
Vol 68 (4) ◽  
pp. 864-868
Author(s):  
Marian Popescu ◽  
Sanda Florentina Mihalache ◽  
Mihaela Oprea
Keyword(s):  
Artificial Intelligence ◽  
Air Pollutants ◽  
Meteorological Parameters ◽  
Fuzzy Inference ◽  
Computational Effort ◽  
Forecasting Model ◽  
Inference System ◽  
Neuro Fuzzy ◽  
And Performance ◽  
The Comparative Study

Particulate matter with an aerodynamic diameter lower than 2.5 �m (PM2.5) is one of the most important air pollutants. Current regulations impose measuring and limiting its concentrations. Thus, it is necessary to develop forecasting models programs that can inform the population about possible pollution episodes. This paper emphasizes the correlations between PM2.5 and other pollutants, and meteorological parameters. From these, nitrogen dioxide and temperature showed have the best correlations with PM2.5 and have been selected as inputs for the proposed forecasting model besides four PM2.5 concentrations (the values from current hour to three hours ago), the output of the model being the prediction of the next hour PM2.5 concentration. Two methods from artificial intelligence were used to build the forecasting model, namely adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (ANN). The comparative study between these methods showed that the model which uses ANN have better results in terms of statistical indicators and computational effort.

A Self-Organized Neuro-Fuzzy System for Air Cargo and Airline Passenger Dynamics Modeling and Forecasting

2012 ◽  
Vol 2 (2) ◽  
pp. 36-49 ◽  
Author(s):  
Chuen-Jyh Chen
Keyword(s):  
Consumer Price Index ◽  
Fuzzy Model ◽  
Air Cargo ◽  
Dynamics Modeling ◽  
Passenger Traffic ◽  
Self Organized ◽  
Neuro Fuzzy ◽  
Airline Passenger ◽  
Modeling Prediction ◽  
Prediction And Forecasting

A self-organized, five-layer neuro-fuzzy model is developed to model the dynamics and to forecast air cargo and airline passenger by using the input of previous years’ consumer price index, exchange rate, gross national product, and number of cargo volume/passenger traffic. Simulation results show that the neuro-fuzzy model is more effective than neural network in prediction and accurate in forecasting. The effectiveness in modeling, prediction and forecasting is validated, and the input error from the series-parallel identification method is attenuated by the neuro-fuzzy model to yield better forecasting results.

scholarly journals A Temporal Mining Framework for Classifying Un-Evenly Spaced Clinical Data

2016 ◽  
Vol 07 (01) ◽  
pp. 1-21 ◽  
Author(s):  
Nancy Jane ◽  
Kannan Arputharaj ◽  
Khanna Nehemiah
Keyword(s):  
Time Series ◽  
Rough Set ◽  
Classification Accuracy ◽  
Temporal Pattern ◽  
Clinical Decision Making ◽  
Classification Model ◽  
Experimental Result ◽  
Series Data ◽  
Forecasting Model ◽  
Neuro Fuzzy

SummaryClinical time-series data acquired from electronic health records (EHR) are liable to temporal complexities such as irregular observations, missing values and time constrained attributes that make the knowledge discovery process challenging.This paper presents a temporal rough set induced neuro-fuzzy (TRiNF) mining framework that handles these complexities and builds an effective clinical decision-making system. TRiNF provides two functionalities namely temporal data acquisition (TDA) and temporal classification.In TDA, a time-series forecasting model is constructed by adopting an improved double exponential smoothing method. The forecasting model is used in missing value imputation and temporal pattern extraction. The relevant attributes are selected using a temporal pattern based rough set approach. In temporal classification, a classification model is built with the selected attributes using a temporal pattern induced neuro-fuzzy classifier.For experimentation, this work uses two clinical time series dataset of hepatitis and thrombosis patients. The experimental result shows that with the proposed TRiNF framework, there is a significant reduction in the error rate, thereby obtaining the classification accuracy on an average of 92.59% for hepatitis and 91.69% for thrombosis dataset.The obtained classification results prove the efficiency of the proposed framework in terms of its improved classification accuracy.

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