Use of artificial neural networks for prognosis of charcoal prices in Minas Gerais

Energy is an important factor of economic growth and is critical to the stability of a nation. Charcoal is a renewable energy resource and is a fundamental input to the development of the Brazilian forest-based industry. The objective of this study is to provide a prognosis of the charcoal price series for the year 2007 by using Artificial Neural Networks. A feedforward multilayer perceptron ANN was used, the results of which are close to reality. The main findings are that: real prices of charcoal dropped between 1975 and 2000 and rose from the early 21st century; the ANN with two hidden layers was the architecture making the best prediction; the most effective learning rate was 0.99 and 600 cycles, representing the most satisfactory and accurate ANN training. Prediction using ANN was found to be more accurate when compared by the mean squared error to other studies modeling charcoal price series in Minas Gerais state.

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Artificial Neural Networks Estimation for Thicknesses of Multilayer Nano-Scale Films

Materials Science Forum ◽

10.4028/www.scientific.net/msf.962.41 ◽

2019 ◽

Vol 962 ◽

pp. 41-48

Author(s):

Tzong Daw Wu ◽

Jiun Shen Chen ◽

Ching Pei Tseng ◽

Cheng Chang Hsieh

Keyword(s):

Thin Films ◽

Neural Networks ◽

Artificial Neural Networks ◽

Real Time ◽

Film Growth ◽

Mean Squared Error ◽

Squared Error ◽

Thin Film Optics ◽

The Mean ◽

Artificial Neural

This study presents a real-time method for determining the thickness of each layer in multilayer thin films. Artificial neural networks (ANNs) were introduced to estimate thicknesses from a transmittance spectrum. After training via theoretical spectra which were generated by thin-film optics and modified by noise, ANNs were applied to estimate the thicknesses of four-layer nanoscale films which were TiO2, Ag, Ti, and TiO2 thin films assembled sequentially on polyethylene terephthalate (PET) substrates. The results reveal that the mean squared error of the estimation is 2.6 nm2, and is accurate enough to monitor film growth in real time.

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Factors Affecting the Stability of Nanoemulsions—Use of Artificial Neural Networks

Pharmaceutical Research ◽

10.1007/s11095-009-0004-2 ◽

2009 ◽

Vol 27 (1) ◽

pp. 37-45 ◽

Cited By ~ 44

Author(s):

Amir Amani ◽

Peter York ◽

Henry Chrystyn ◽

Brian J. Clark

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Factors Affecting ◽

Artificial Neural ◽

The Stability

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DESENVOLVIMENTO DE REDES NEURAIS ARTIFICIAIS PARA ESTIMATIVA DAS VAZÕES DIÁRIAS NA BACIA DO RIO PIRACICABA5

Irriga ◽

10.15809/irriga.2018v23n4p756-772 ◽

2018 ◽

Vol 23 (4) ◽

pp. 756-772

Author(s):

Eduardo Morgan Uliana ◽

Demetrius David da Silva ◽

Michel Castro Moreira ◽

Donizete Dos Reis Pereira ◽

Silvio Bueno Pereira ◽

...

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

River Basin ◽

Hydrologic Modeling ◽

Minas Gerais ◽

Mato Grosso ◽

Artificial Neural ◽

E Mail ◽

Daily Flows ◽

Piracicaba River

DESENVOLVIMENTO DE REDES NEURAIS ARTIFICIAIS PARA ESTIMATIVA DAS VAZÕES DIÁRIAS NA BACIA DO RIO PIRACICABA5 EDUARDO MORGAN ULIANA1; DEMETRIUS DAVID DA SILVA2; MICHEL CASTRO MOREIRA3; DONIZETE DOS REIS PEREIRA4; SILVIO BUENO PEREIRA2 E FREDERICO TERRA DE ALMEIDA1 1Universidade Federal de Mato Grosso (UFMT), Instituto de Ciências Agrárias e Ambientais (ICAA), Avenida Alexandre Ferronato, CEP.: 78557-267, Sinop – MT, Brasil, e-mail: [email protected], [email protected]. 2Universidade Federal de Viçosa (UFV), Departamento de Engenharia Agrícola, Avenida Peter Henry Rolfs, CEP.: 36570-900, Viçosa – MG, Brasil, e-mail: [email protected]. 3Universidade Federal do Oeste da Bahia, Centro das Ciências Exatas e das Tecnologias, Rua Professor José Seabra de Lemos, CEP.: 47808-021, Barreiras – BA, Brasil, e-mail: [email protected]. 4Universidade Federal de Viçosa (UFV), Instituto de Ciências Agrárias, Rodovia LMG 818, km 06, Florestal – MG, Brasil, e-mail: [email protected]. 5O artigo é referente ao capítulo 3 da tese de doutorado do primeiro autor. 1 RESUMO As Redes Neurais Artificiais (RNAs) são uma alternativa na modelagem hidrológica para a estimativa das vazões dos cursos de água a partir de dados hidrometeorológicos. O objetivo do trabalho foi desenvolver Redes Neurais Artificiais para estimar as vazões diárias na bacia hidrográfica do rio Piracicaba, Minas Gerais. O estudo foi realizado em três seções de monitoramento de vazão da bacia do rio Piracicaba, localizada no Estado de Minas Gerais - Brasil. No desenvolvimento das RNAs foram realizados a coleta e seleção dos dados; a definição da arquitetura da rede; e o treinamento e validação das redes desenvolvidas. A maior parte das RNAs desenvolvidas apresentou coeficiente de Nash-Sutcliffe maior que 0,80 o que permitiu classificar os modelos como bons para a estimativa das vazões. Com base nos resultados, pode-se concluir que as RNAs são adequadas para a estimativa das vazões diárias na bacia do rio Piracicaba e podem ser empregadas na estimativa de eventos extremos e no gerenciamento dos recursos hídricos. Palavras-Chave: modelo empírico inteligência artificial, modelagem hidrológica. ULIANA, E. M.; SILVA, D. D.; MOREIRA, M. C.; PEREIRA, D. R.; PEREIRA, S. B.; ALMEIDA, F. T. ARTIFICIAL NEURAL NETWORKS FOR DAILY FLOW ESTIMATES IN THE PIRACICABA RIVER BASIN 2 ABSTRACT Artificial neural networks (ANNs) have been used alternatively in hydrologic modeling to estimate accurately watercourse flows based on hydrometeorological data. This study developed artificial neural networks to estimate daily flows in Piracicaba river basin, in Minas Gerais state (Brazil). For this, we used three runoff-monitoring sections of the Piracicaba river basin, with an area of 5,304.0 km2, and located in the State of Minas Gerais – Brazil. For designing the ANNs to estimate daily flows, we adopted the following steps: data collection and selection, network architecture definition, training and validation of results. The results showed that ANNs are adequate to estimate daily flows in Piracicaba river basin. Keywords: empirical model, artificial intelligence, hydrologic modeling.

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Approximation of the process of changes in deformation of land surface using artificial neural networks

E3S Web of Conferences ◽

10.1051/e3sconf/20185500009 ◽

2018 ◽

Vol 55 ◽

pp. 00009

Author(s):

Maria Mrówczyńska ◽

Jacek Sztubecki

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Land Surface ◽

Reference System ◽

Surface Deformation ◽

Copper Mining ◽

Vertical Displacements ◽

Artificial Neural ◽

The Stability ◽

The Impact

Artificial neural networks are an interesting method for modelling phenomena, including spatial phenomena, which are difficult to describe with known mathematical models. The properties of neural networks enable their practical application for solving such problems as: approximation, interpolation, identification and classification of patterns, compression, prediction, etc. The article presents the use of multilayer feedforward artificial neural networks for describing the process of changes in land surface deformation in the area of the Legnica-Głogów Copper Mining Centre, located in the southern part of the Fore Sudetic Monocline. Results provided by geodesic monitoring, which consists of land surveying and interpreting data obtained in this way, are undoubtedly significant in terms of identifying the impact of mining on the land surface the results of measurements carried out by precise levelling in the years 19672014 were used to determine changes in land deformation in the Legnica-Głogów Copper Mining Centre. The concept of a flexible reference system was used to assess the stability of points in the measurement and control network stabilized in order to determine vertical displacements. However, the reference system itself was identified on the basis of the critical value of the increment of the square of the norm of corrections to the observations.

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Use of Artificial Neural Networks and Arima to Forecasting Consumption Sawnwood of Pinus sp. in Brazil

International Forestry Review ◽

10.1505/146554819825863735 ◽

2019 ◽

Vol 21 (1) ◽

pp. 51-61 ◽

Cited By ~ 1

Author(s):

D.A. Buratto ◽

R. Timofeiczyk Junior ◽

J.C.G.L. Silva ◽

J.R. Frega ◽

M.S.S.A. Wiecheteck ◽

...

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Mean Squared Error ◽

Arima Model ◽

Secondary Data ◽

Information Criterion ◽

Absolute Error ◽

Percentage Error ◽

Explanatory Variables ◽

Artificial Neural

The objective of this study was to analyze the application of an artificial neural networks model and an ARIMA model to predict the consumption of sawnwood of pine. For this, we use real and secondary data collected and obtained from a historical data source, corresponding to the period from 1997 to 2016, which were later tested to generate the forecast models. Based on economic and statistical criteria, six explanatory variables were used to fit the best model. The choice of the model was made based on Mean Squared Error, Mean Absolute Error, Theil U metric, Percentage Error of Forecast and Akaike value information criterion. The results indicated that the models generated through the ARIMA model presented better performance when compared to the artificial neural network. The best adjusted model estimated a reduction of 1.33% in consumption of sawnwood of pine in Brazil for the period between 2017 and 2020.

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Artificial Neural Networks for Filling Missing Streamflow Data in Rio do Carmo Basin, Minas Gerais, Brazil

Brazilian Archives of Biology and Technology ◽

10.1590/1678-4324-2020180522 ◽

2020 ◽

Vol 63 ◽

Author(s):

Gabriela Rezende de Souza ◽

Italoema Pinheiro Bello ◽

Flávia Vilela Corrêa ◽

Luiz Fernando Coutinho de Oliveira

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Minas Gerais ◽

Streamflow Data ◽

Artificial Neural

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A Comparison of Performance of Artificial Neural Networks for Prediction of Heavy Metals Concentration in Groundwater Resources of Toyserkan Plain

Avicenna Journal of Environmental Health Engineering ◽

10.5812/ajehe.11792 ◽

2017 ◽

Vol 4 (1) ◽

pp. 11792-11792 ◽

Cited By ~ 5

Author(s):

Meysam Alizamir ◽

Soheil Sobhanardakani

Keyword(s):

Heavy Metals ◽

Neural Networks ◽

Artificial Neural Networks ◽

Mean Squared Error ◽

Groundwater Resources ◽

Coefficient Of Determination ◽

Multi Layer Perceptron ◽

Squared Error ◽

Artificial Neural ◽

Statistical Indicators

Nowadays, about 50% the world’s population is living in dry and semi dry regions and has utilized groundwater as a source of drinking water. Therefore, forecasting of pollutant content in these regions is vital. This study was conducted to compare the performance of artificial neural networks (ANNs) for prediction of As, Zn, and Pb content in groundwater resources of Toyserkan Plain. In this study, two types of artificial neural networks (ANNs), namely multi-layer perceptron (MLP) and Radial Basis Function (RBF) approaches, were examined using the observations of As, Zn, and Pb concentrations in groundwater resources of Toyserkan plain, Western Iran. Two statistical indicators, the coefficient of determination (R2) and root mean squared error (RMSE) were employed to evaluate the performances of various models. The results indicated that the best performance could be obtained by MLP, in terms of different statistical indicators during training and validation periods.

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Application of Artificial Neural Networks in Analysis of Time-Variable Optical Reflectance Spectra in Digital Light Projection Spectroscopy

Coatings ◽

10.3390/coatings12010037 ◽

2021 ◽

Vol 12 (1) ◽

pp. 37

Author(s):

Marek Gąsiorowski ◽

Piotr Szymak ◽

Leszek Bychto ◽

Aleksy Patryn

Keyword(s):

Neural Networks ◽

Optical Properties ◽

Artificial Neural Networks ◽

Mean Squared Error ◽

Reflectance Spectra ◽

Training Methods ◽

Optical Reflectance ◽

Research Cycle ◽

Evaluation Module ◽

Artificial Neural

This article undertakes the subject matter of applying artificial neural networks to analyze optical reflectance spectra of objects exhibiting a change of optical properties in the domain of time. A compact Digital Light Projection NIRscan Nano Evaluation Module spectrometer was used to record spectra. Due to the miniature spectrometer’s size and its simplicity of measurement, it can be used to conduct tests outside of a laboratory. A series of plant-derived objects were used as test subjects with rapidly changing optical properties in the presented research cycle. The application of artificial neural networks made it possible to determine the aging time of plants with a relatively low mean squared error, reaching 0.56 h for the Levenberg–Marquardt backpropagation training method. The results of the other ten training methods for artificial neural networks have been included in the paper.

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