Artificial neural network forecast application for fine particulate matter concentration using meteorological data

estimation of atmospheric particulate matter (pm10) concentration based on remote sensing measurements and meteorological parameters: application of artificial neural network

suspended aerosols in the atmosphere have strong impact on the global climate. they influence the earth’s radiation budget by scattering or absorbing both incoming and outgoing radiation. aerosols in troposphere are caused by natural sources, such as dust, sea-spray and volcanoes and also by anthropogenic sources, such as combustion of fossil fuels and biomass burning activities and from gas-to...

Neural network forecast of daily pollution concentration using optimal meteorological data at synoptic and local scales

We present a simple neural network and data pre–selection framework, discriminating the most essential input data for accurately forecasting the concentrations of PM10, based on observations for the years between 2002 and 2006 in the metropolitan region of Lisbon, Portugal. Starting from a broad panoply of different data sets collected at several air quality and meteorological stations, a forw...

Forecasting Natural Gas Demand Using Meteorological Data: Neural Network Method

The need for prediction and patterns of gas consumption especially in the cold seasons is essential for consumption management and policy planning decision making. In residential and commercial uses which account for the bulk of gas consumption in the country the effects of meteorological variables have the highest impact on consumption.  In the present research four variables include daily ave...

scour modeling piles of kambuzia industrial city bridge using hec-ras and artificial neural network

today, scouring is one of the important topics in the river and coastal engineering so that the most destruction in the bridges is occurred due to this phenomenon. whereas the bridges are assumed as the most important connecting structures in the communications roads in the country and their importance is doubled while floodwater, thus exact design and maintenance thereof is very crucial. f...

Development of An Artificial Neural Network Model for Asphalt Pavement Deterioration Using LTPP Data

Deterioration models are important and essential part of any Pavement Management System (PMS). These models are used to predict future pavement situation based on existence condition, parameters causing deterioration and implications of various maintenance and rehabilitation policies on pavement. The majority of these models are based on roughness which is one of the most important indices in p...

Representation of Adsorption Data for the Case of Energetically Heterogeneous Solid Surfaces Using Artificial Neural Network