[PDF] Top 20 Study on Pollution Forecasting using 2Phase Neural Network
Has 10000 "Study on Pollution Forecasting using 2Phase Neural Network" found on our website. Below are the top 20 most common "Study on Pollution Forecasting using 2Phase Neural Network".
Study on Pollution Forecasting using 2Phase Neural Network
... monitoring network is described in Table 1 and included meteorological parameters from a private monitoring network (EnteZonaIndustrialedi Porto Marghera), data from the airquality network of the ... See full document
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Review on Pollution Forecasting using 2Phase Neural Network
... which pollution is developed in urban and rural India the air contamination developed crosswise over India is ...Air Pollution crosswise over key areas of New ...the forecasting methodology or system ... See full document
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Groundwater level forecasting using Artificial Neural Network
... present study, factor analysis was introduced to identify the factors that have maximum influence on the groundwater level of the area and time series analysis to forecast the influencing factors prior to ANN ... See full document
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Forecasting of heavy metals concentration in groundwater resources of Asadabad plain using artificial neural network approach
... 22. Fazel Tavasol S, Vusuq BP, Manshuri M. The investigation of heavy metal (Sn-Pb) concentration in ground water resources and their environmental effects, Case study: North Chardoly Plain. Proceedings of the 1 ... See full document
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A Study On Short Term Load Forecasting Using Back Propagation Neural Network
... LIST OF FIGURES FIGURE TITLE PAGE Black box device 5 Simple summation fimction to determine the output 7 Summation functions to the threshold to determine output 8 The types of transfer [r] ... See full document
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Evolutionary Load Forecasting using Artificial Neural Network
... Data collection is a vital task of any kind of research study. Insufficient or inaccurate data can influence the results of a study and eventually lead to skewed results.In this, the actual load data was ... See full document
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Daily SO2 Air Pollution Prediction with the use of Artificial Neural Network Models
... and network parameters were changed to find the most suitable ...the network are information from other neurons or from the outside ...the network. In this study, the input from the neuron is ... See full document
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APPLICATION OF CELLULAR AUTOMATA FOR MODELING AND REVIEW OF METHODS OF MOVEMENT OF A GROUP OF PEOPLE
... market forecasting of price/index has always been an important financial ...artificial neural networks that proved to be effective ...This study uses the following: ten technical indicators as ... See full document
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ANN based short-term traffic flow forecasting in undivided two lane highway
... traffic forecasting is one of the important fields of study in the transporta- tion ...traffic forecasting is very useful to develop a more advanced transportation system to control traffic signals ... See full document
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Demand forecasting Using Artificial Neural Network Based on Different Learning Methods: Comparative Analysis
... to study the effectiveness of forecasting the demand signals in the supply chain with ANN method and identify the best training ...This study has developed a comparative forecasting mechanism ... See full document
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Simulation of Forecasting Performance Comparison of a Hybrid Model Integrated By Binomial Smoothing and Bayesian Model Averaging Techniques
... Inthis study, a hybrid JPSN-AR model is proposed based on binomial smoothing (BS) and Bayesian model averaging ...best forecasting performance for the proposed hybrid model. The forecasting ... See full document
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Electrical Load Forecasting in Power Distribution Network by Using Artificial Neural Network
... the network. The network setting is original back propagation 4 inputs and 1 ...of study is for eight month from May 2008 till December ...of using purelin and logsig is shown by ... See full document
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Price forecasting in e-commerce using neural network approach
... that neural network method of sales forecasting is far better than the traditional way of ...Detailed study of this research paper leads to the conclusion that neural network ... See full document
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A New Approach for Rainfall Prediction using Artificial Neural Network
... rainfall forecasting model is very important ...rainfall forecasting, were used an Artificial Neural Network (ANN) by the Cascade-forward Back propagation Neural Network (CFBPNN) ... See full document
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FORECASTING USING NEURAL NETWORK AND FUZZY TIME SERIES: A RELATIVE STUDY USING SUGAR PRODUCTION DATA
... current study is gathered from Food Corporation of ...and neural network ...in neural network elucidation is done by ANN,thus neural network is considered as objective in ... See full document
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Rainfall Forecasting Using Backpropagation Neural Network
... Rainfall forecasting based on time-series has been researched by Iriany et ...al.(2015) using 10 days rainfall period and forecast it with GSTAR_SUR Model but this model can only explained about ...a ... See full document
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Suitability of neural network for weather forecasting: a comprehensive literature review
... for forecasting, there is certainly the potential for problems with big data given services like TIHS Global Insight that provide access to over 30,000,000-time ...for forecasting approaches which leads us ... See full document
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Forecasting nitrate concentration in groundwater using artificial neural network and linear regression models
... groundwater using regression and neural ...artificial neural networks to predict the pesticide and nitrate contamination in rural private ...and neural networks to model nitrate concentration ... See full document
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Short-Term Load Forecasting Using Artificial Neural Network
... load forecasting methodologies as reported in [1] have their own ...Load forecasting can be performed using many techniques such as regression analysis, statistical methods, artificial neural ... See full document
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Review on Financial Forecasting Using Neural Network and Data Mining Technique
... Is the process of identifying a set of common features and models that describe and distinguish data classes or concepts. The models are used to predict the class of objects whose class label is unknown. A bank, for ... See full document
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