Suspended Sediment Forecasting by Artificial Neural Networks Using Hydro Meteorological Data

2003 ◽  
Author(s):  
H. Kerem Cigizoglu ◽  
Murat Alp
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Suspended Sediment ◽  
Meteorological Data ◽  
Artificial Neural

Ozone Concentration Prediction using Artificial Neural Networks

2017 ◽  
Vol 68 (10) ◽  
pp. 2224-2227 ◽  
Author(s):  
Camelia Gavrila
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Ozone Concentration ◽  
Meteorological Data ◽  
Global Solar Radiation ◽  
Time Data ◽  
Marquardt Algorithm ◽  
Sensitivity Tests ◽  
Artificial Neural ◽  
The Relationship

The aim of this paper is to determine a mathematical model which establishes the relationship between ozone levels together with other meteorological data and air quality. The model is valid for any season and for any area and is based on real-time data measured in Bucharest and its surroundings. This study is based on research using artificial neural networks to model nonlinear relationships between the concentration of immission of ozone and the meteorological factors: relative humidity (RH), global solar radiation (SR), air temperature (TEMP). The ozone concentration depends on following primary pollutants: nitrogen oxides (NO, NO2), carbon monoxide (CO). To achieve this, the Levenberg-Marquardt algorithm was implemented in Scilab, a numerical computation software. Performed sensitivity tests proved the robustness of the model and its applicability in predicting the ozone on short-term.

Prediction of short and medium term PM10 concentration using artificial neural networks

2019 ◽  
Vol 30 (2) ◽  
pp. 414-436 ◽  
Author(s):  
Elaine Schornobay-Lui ◽  
Eduardo Carlos Alexandrina ◽  
Mônica Lopes Aguiar ◽  
Werner Siegfried Hanisch ◽  
Edinalda Moreira Corrêa ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Predictive Models ◽  
Urban Areas ◽  
Meteorological Data ◽  
High Volume ◽  
Data Series ◽  
Content Type ◽  
Artificial Neural ◽  
Narx Model

Purpose There has been a growing concern about air quality because in recent years, industrial and vehicle emissions have resulted in unsatisfactory human health conditions. There is an urgent need for the measurements and estimations of particulate pollutants levels, especially in urban areas. As a contribution to this issue, the purpose of this paper is to use data from measured concentrations of particulate matter and meteorological conditions for the predictions of PM10. Design/methodology/approach The procedure included daily data collection of current PM10 concentrations for the city of São Carlos-SP, Brazil. These data series enabled to use an estimator based on artificial neural networks. Data sets were collected using the high-volume sampler equipment (VFA-MP10) in the period ranging from 1997 to 2006 and from 2014 to 2015. The predictive models were created using statistics from meteorological data. The models were developed using two neural network architectures, namely, perceptron multilayer (MLP) and non-linear autoregressive exogenous (NARX) inputs network. Findings It was observed that, over time, there was a decrease in the PM10 concentration rates. This is due to the implementation of more strict environmental laws and the development of less polluting technologies. The model NARX that used as input layer the climatic variables and the PM10 of the previous day presented the highest average absolute error. However, the NARX model presented the fastest convergence compared with the MLP network. Originality/value The presentation of a given PM10 concentration of the previous day improved the performance of the predictive models. This paper brings contributions with the NARX model applications.

scholarly journals Artificial neural networks employment in the prediction of evapotranspiration of greenhouse-grown sweet pepper

2016 ◽  
Vol 20 (6) ◽  
pp. 507-512 ◽  
Author(s):  
Héliton Pandorfi ◽  
Alan C. Bezerra ◽  
Roberto T. Atarassi ◽  
Frederico M. C. Vieira ◽  
José A. D. Barbosa Filho ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Network Architecture ◽  
Learning Algorithm ◽  
Meteorological Data ◽  
Back Propagation ◽  
Sweet Pepper ◽  
Coefficient Of Determination ◽  
Error Back Propagation ◽  
Artificial Neural

ABSTRACT This study aimed to investigate the applicability of artificial neural networks (ANNs) in the prediction of evapotranspiration of sweet pepper cultivated in a greenhouse. The used data encompass the second crop cycle, from September 2013 to February 2014, constituting 135 days of daily meteorological data, referring to the following variables: temperature and relative air humidity, wind speed and solar radiation (input variables), as well as evapotranspiration (output variable), determined using data obtained by load-cell weighing lysimeter. The recorded data were divided into three sets for training, testing and validation. The ANN learning model recognized the evapotranspiration patterns with acceptable accuracy, with mean square error of 0.005, in comparison to the data recorded in the lysimeter, with coefficient of determination of 0.87, demonstrating the best approximation for the 4-21-1 network architecture, with multilayers, error back-propagation learning algorithm and learning rate of 0.01.

scholarly journals Testing hourly reference evapotranspiration approaches using lysimeter measurements in a semiarid climate

2009 ◽  
Vol 41 (1) ◽  
pp. 38-49 ◽  
Author(s):  
Slavisa Trajkovic
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Reference Evapotranspiration ◽  
Meteorological Data ◽  
Irrigation Management ◽  
Weather Data ◽  
Canopy Resistance ◽  
Rbf Network ◽  
Artificial Neural ◽  
Two Parameters

Numerous approaches have been developed for estimating hourly reference evapotranspiration ET0, most of which require numerous meteorological data. In many areas, the necessary data are lacking and new techniques are required. The objectives of this study are: (1) to develop artificial neural networks for estimating hourly reference evapotranspiration from limited weather data; (2) to evaluate the reliability of obtained artificial neural networks (ANNs) and Food and Agricultural Organization—56 Penman Monteith (FAO-56 PM) equation compared to the lysimeter measurements; (3) to test the performance of the FAO-56 PM equation for hourly daytime periods using rc=70 s m−1 (PM70) and using a lower rc=50 s m−1 (PM50); and (4) to evaluate the reliability of obtained ANNs compared to the FAO-56 PM equation using an hourly dataset from a variety of locations. The accuracy of two reduced-set artificial neural networks (ANNTR and ANNTHR) and two FAO-56 Penman-Monteith equations with different canopy resistance values (PM50 and PM70) was assessed using hourly lysimeter data from Davis, California. The ANNTR required only two parameters (temperature and radiation) as inputs. Temperature, humidity and (Rn−G) term were used as inputs in the ANNTHR. The ANNTR and PM50 were best at estimating hourly grass ET0. The ANNTR approach was additionally tested using hourly FAO-56 PM ET0 data from California Irrigation Management Information System (CIMIS) dataset. The overall results recommended Radial Basis Function (RBF) network for estimating hourly ET0 from limited weather data. Also, the results support the introduction of new value for canopy resistance (rc=50 s m−1) in the hourly FAO-56 PM equation.

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