Soil infiltration based on bp neural network and grey relational analysis

Soil infiltration is a key link of the natural water cycle process. Studies on soil permeability are conducive for water resources assessment and estimation, runoff regulation and management, soil erosion modeling, nonpoint and point source pollution of farmland, among other aspects. The unequal influence of rainfall duration, rainfall intensity, antecedent soil moisture, vegetation cover, vegetation type, and slope gradient on soil cumulative infiltration was studied under simulated rainfall and different underlying surfaces. We established a six factor-model of soil cumulative infiltration by the improved back propagation (BP)-based artificial neural network algorithm with a momentum term and self-adjusting learning rate. Compared to the multiple nonlinear regression method, the stability and accuracy of the improved BP algorithm was better. Based on the improved BP model, the sensitive index of these six factors on soil cumulative infiltration was investigated. Secondly, the grey relational analysis method was used to individually study grey correlations among these six factors and soil cumulative infiltration. The results of the two methods were very similar. Rainfall duration was the most influential factor, followed by vegetation cover, vegetation type, rainfall intensity and antecedent soil moisture. The effect of slope gradient on soil cumulative infiltration was not significant.

Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping

Soil surveys are the main source of spatial information on soils and have a range of different applications, mainly in agriculture. The continuity of this activity has however been severely compromised, mainly due to a lack of governmental funding. The purpose of this study was to evaluate the feasibility of two different classifiers (artificial neural networks and a maximum likelihood algorithm) in the prediction of soil classes in the northwest of the state of Rio de Janeiro. Terrain attributes such as elevation, slope, aspect, plan curvature and compound topographic index (CTI) and indices of clay minerals, iron oxide and Normalized Difference Vegetation Index (NDVI), derived from Landsat 7 ETM+ sensor imagery, were used as discriminating variables. The two classifiers were trained and validated for each soil class using 300 and 150 samples respectively, representing the characteristics of these classes in terms of the discriminating variables. According to the statistical tests, the accuracy of the classifier based on artificial neural networks (ANNs) was greater than of the classic Maximum Likelihood Classifier (MLC). Comparing the results with 126 points of reference showed that the resulting ANN map (73.81 %) was superior to the MLC map (57.94 %). The main errors when using the two classifiers were caused by: a) the geological heterogeneity of the area coupled with problems related to the geological map; b) the depth of lithic contact and/or rock exposure, and c) problems with the environmental correlation model used due to the polygenetic nature of the soils. This study confirms that the use of terrain attributes together with remote sensing data by an ANN approach can be a tool to facilitate soil mapping in Brazil, primarily due to the availability of low-cost remote sensing data and the ease by which terrain attributes can be obtained.

Artificial neural networks applied for soil class prediction in mountainous landscape of the Serra do Mar¹

Soil information is needed for managing the agricultural environment. The aim of this study was to apply artificial neural networks (ANNs) for the prediction of soil classes using orbital remote sensing products, terrain attributes derived from a digital elevation model and local geology information as data sources. This approach to digital soil mapping was evaluated in an area with a high degree of lithologic diversity in the Serra do Mar. The neural network simulator used in this study was JavaNNS and the backpropagation learning algorithm. For soil class prediction, different combinations of the selected discriminant variables were tested: elevation, declivity, aspect, curvature, curvature plan, curvature profile, topographic index, solar radiation, LS topographic factor, local geology information, and clay mineral indices, iron oxides and the normalized difference vegetation index (NDVI) derived from an image of a Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor. With the tested sets, best results were obtained when all discriminant variables were associated with geological information (overall accuracy 93.2 - 95.6 %, Kappa index 0.924 - 0.951, for set 13). Excluding the variable profile curvature (set 12), overall accuracy ranged from 93.9 to 95.4 % and the Kappa index from 0.932 to 0.948. The maps based on the neural network classifier were consistent and similar to conventional soil maps drawn for the study area, although with more spatial details. The results show the potential of ANNs for soil class prediction in mountainous areas with lithological diversity.

Comparing the artificial neural network with parcial least squares for prediction of soil organic carbon and pH at different moisture content levels using visible and near-infrared spectroscopy

Visible and near infrared (vis-NIR) spectroscopy is widely used to detect soil properties. The objective of this study is to evaluate the combined effect of moisture content (MC) and the modeling algorithm on prediction of soil organic carbon (SOC) and pH. Partial least squares (PLS) and the Artificial neural network (ANN) for modeling of SOC and pH at different MC levels were compared in terms of efficiency in prediction of regression. A total of 270 soil samples were used. Before spectral measurement, dry soil samples were weighed to determine the amount of water to be added by weight to achieve the specified gravimetric MC levels of 5, 10, 15, 20, and 25 %. A fiber-optic vis-NIR spectrophotometer (350-2500 nm) was used to measure spectra of soil samples in the diffuse reflectance mode. Spectra preprocessing and PLS regression were carried using Unscrambler® software. Statistica® software was used for ANN modeling. The best prediction result for SOC was obtained using the ANN (RMSEP = 0.82 % and RPD = 4.23) for soil samples with 25 % MC. The best prediction results for pH were obtained with PLS for dry soil samples (RMSEP = 0.65 % and RPD = 1.68) and soil samples with 10 % MC (RMSEP = 0.61 % and RPD = 1.71). Whereas the ANN showed better performance for SOC prediction at all MC levels, PLS showed better predictive accuracy of pH at all MC levels except for 25 % MC. Therefore, based on the data set used in the current study, the ANN is recommended for the analyses of SOC at all MC levels, whereas PLS is recommended for the analysis of pH at MC levels below 20 %.

Efeito da freqüência de sangria e estimulação no desempenho produtivo e econômico de clones de seringueira

Este trabalho teve como objetivo avaliar o desempenho produtivo e os aspectos econômicos de dez clones de seringueira [Hevea brasiliensis (Willd. ex Adr. de Juss.) Muell. Arg.] em diferentes freqüências de sangria e com estimulação à base de ethefon. O experimento foi instalado na Fazenda Indiana, no município de Indiana, SP, sob o delineamento de blocos ao acaso com parcelas subdivididas. Os tratamentos principais foram os clones GT 1, RRIM 701, RRIM 600, PB 235, PR 261, PB 252, Fx 4098, Fx 2261, Fx 3864 e IAN 873, submetidos a três sistemas de sangria: 1/2S d/2 6d/7 (testemunha), 1/2S d/4 6d/7.10m/y. ET 5,0% Ba 10y e 1/2S d/6 6d/7.10m/y. ET 5,0% Ba 10/y. As variáveis estudadas foram: perímetro do caule, produção, secamento do painel e os aspectos econômicos. Os resultados mostraram superioridade no sistema 1/2S d/2 6d/7 na maioria dos clones, exceto o GT 1 e o PB 235, que no sistema 1/2S d/4 ET 5,0% apresentaram ganhos líquidos de 12,0% e 54,0%, respectivamente, acima do obtido no sistema testemunha. Somente os clones Fx 3864 e PB 235 apresentaram ganhos líquidos de 18,0% e 28,0% no sistema 1/2S d/6 ET 5% superiores em relação ao obtido no sistema testemunha. A maior porcentagem de secamento do painel foi observada no clone PB 235 no sistema 1/2S d/4 ET 5,0%.

Development and evaluation of neural network models to estimate daily solar radiation at Córdoba, Argentina

The objective of this work was to develop neural network models of backpropagation type to estimate solar radiation based on extraterrestrial radiation data, daily temperature range, precipitation, cloudiness and relative sunshine duration. Data from Córdoba, Argentina, were used for development and validation. The behaviour and adjustment between values observed and estimates obtained by neural networks for different combinations of input were assessed. These estimations showed root mean square error between 3.15 and 3.88 MJ m-2 d-1 . The latter corresponds to the model that calculates radiation using only precipitation and daily temperature range. In all models, results show good adjustment to seasonal solar radiation. These results allow inferring the adequate performance and pertinence of this methodology to estimate complex phenomena, such as solar radiation.

HETEROTIC GROUP FORMATION IN PSIDIUM GUAJAVA L. BY ARTIFICIAL NEURAL NETWORK AND DISCRIMINANT ANALYSIS

ABSTRACT The present study aimed at evaluating the heterotic group formation in guava based on quantitative descriptors and using artificial neural network (ANN). For such, we evaluated eight quantitative descriptors. Large genetic variability was found for the eight quantitative traits in the 138 genotypes of guava. The artificial neural network technique determined that the optimal number of groups was three. The grouping consistency was determined by linear discriminant analysis, which obtained classification percentage of the groups, with a value of 86 %. It was concluded that the artificial neural network method is effective to detect genetic divergence and heterotic group formation.

Time-series forecasting of pollutant concentration levels using particle swarm optimization and artificial neural networks

This study evaluates the application of an intelligent hybrid system for time-series forecasting of atmospheric pollutant concentration levels. The proposed method consists of an artificial neural network combined with a particle swarm optimization algorithm. The method not only searches relevant time lags for the correct characterization of the time series, but also determines the best neural network architecture. An experimental analysis is performed using four real time series and the results are shown in terms of six performance measures. The experimental results demonstrate that the proposed methodology achieves a fair prediction of the presented pollutant time series by using compact networks.

Description of epidemics of coffee rust with neural networks

The objective of this paper was to evaluate the potential of neural networks (NN) as an alternative method to the basic epidemiological approach to describe epidemics of coffee rust. The NN was developed from the intensities of coffee (Coffea arabica) rust along with the climatic variables collected in Lavras-MG between 13 February 1998 and 20 April 2001. The NN was built with climatic variables that were either selected in a stepwise regression analysis or by the Braincel® system, software for NN building. Fifty-nine networks and 26 regression models were tested. The best models were selected based on small values of the mean square deviation (MSD) and of the mean prediction error (MPE). For the regression models, the highest coefficients of determination (R²) were used. The best model developed with neural networks had an MSD of 4.36 and an MPE of 2.43%. This model used the variables of minimum temperature, production, relative humidity of the air, and irradiance 30 days before the evaluation of disease. The best regression model was developed from 29 selected climatic variables in the network. The summary statistics for this model were: MPE=6.58%, MSE=4.36, and R²=0.80. The elaborated neural networks from a time series also were evaluated to describe the epidemic. The incidence of coffee rust at four previous fortnights resulted in a model with MPE=4.72% and an MSD=3.95.

Rainfall erosivity for the State of Rio de Janeiro estimated by artificial neural network

The Artificial Neural Networks (ANNs) are mathematical models method capable of estimating non-linear response plans. The advantage of these models is to present different responses of the statistical models. Thus, the objective of this study was to develop and to test ANNs for estimating rainfall erosivity index (EI30) as a function of the geographical location for the state of Rio de Janeiro, Brazil and generating a thematic visualization map. The characteristics of latitude, longitude e altitude using ANNs were acceptable to estimating EI30 and allowing visualization of the space variability of EI30. Thus, ANN is a potential option for the estimate of climatic variables in substitution to the traditional methods of interpolation.

Determination of the influence of the variation of reducing and non-reducing sugars on coffee quality with use of artificial neural network

The present study aimed at evaluating the use of Artificial Neural Network to correlate the values resulting from chemical analyses of samples of coffee with the values of their sensory analyses. The coffee samples used were from the Coffea arabica L., cultivars Acaiá do Cerrado, Topázio, Acaiá 474-19 and Bourbon, collected in the southern region of the state of Minas Gerais. The chemical analyses were carried out for reducing and non-reducing sugars. The quality of the beverage was evaluated by sensory analysis. The Artificial Neural Network method used values from chemical analyses as input variables and values from sensory analysis as output values. The multiple linear regression of sensory analysis values, according to the values from chemical analyses, presented a determination coefficient of 0.3106, while the Artificial Neural Network achieved a level of 80.00% of success in the classification of values from the sensory analysis.

Application of artificial neural networks as an alternative to volumetric water balance in drip irrigation management in watermelon crop

Precision irrigation seeks to establish strategies which achieve an efficient ratio between the volume of water used (reduction in input) and the productivity obtained (increase in production). There are several studies in the literature on strategies for achieving this efficiency, such as those dealing with the method of volumetric water balance (VWB). However, it is also of great practical and economic interest to set up versatile implementations of irrigation strategies that: (i) maintain the performance obtained with other implementations, (ii) rely on few computational resources, (iii) adapt well to field conditions, and (iv) allow easy modification of the irrigation strategy. In this study, such characteristics are achieved when using an Artificial Neural Network (ANN) to determine the period of irrigation for a watermelon crop in the Irrigation Perimeter of the Lower Acaraú, in the state of Ceará, Brazil. The Volumetric Water Balance was taken as the standard for comparing the management carried out with the proposed implementation of ANN. The statistical analysis demonstrates the effectiveness of the proposed management, which is able to replace VWB as a strategy in automation.

Dosagem de folatos maternos e fetais, séricos e eritrocitários em malformações por defeito de fechamento do tubo neural no feto

Objetivos: avaliar os níveis de folatos maternos e fetais gestações com malformações por defeitos de fechamento do tubo neural (DFTN). Métodos: o estudo foi do tipo caso-controle, no qual 14 casos de fetos com DFTN (grupo estudo) e 14 casos de fetos com outras malformações (grupo controle) foram estudados em gestantes de baixo risco para DFTN. Propusemo-nos a dosar o ácido fólico, na sua forma total e metilada, nos compartimentos fetal e materno, utilizando dosagens séricas e tissulares (eritrocitárias), assim como o volume corpuscular médio, o hematócrito e a hemoglobina. As coletas foram realizadas imediatamente antes da interrupção da gestação. Os resultados nos dois grupos foram comparados pelo teste t de Student, método de amostras pareados pela idade gestacional. Resultados: não se encontrou diferença nas taxas de folatos fetais e nos parâmetros hematológicos dos fetos, entre os dois grupos. Por outro lado, taxas anormalmente baixas de folatos foram encontradas nos eritrócitos das mães portadoras de fetos com DFTN, tanto para as formas totais(293,9 ng/mL contra 399,1 ng/mL no grupo controle, p=0,01) quanto para as formas metiladas (201,9 ng/mL contra 314,0 ng/mL para o grupo controle, p=0,02). Os folatos séricos maternos não se mostraram diferentes nos grupos estudo e controle. Conclusão: este estudo demonstrou que há uma menor taxa de folatos intratissulares, nas mães de fetos acometidos por DFTN, porém com taxas de folatos séricos semelhantes em relação ao grupo controle.

Folato, vitamina B12 e ferritina sérica e defeitos do tubo neural

Objetivo: verificar os níveis de folatos, vitamina B12 e ferritina em pacientes cujos fetos apresentaram defeitos de tubo neural (DTN). O folato sangüíneo e a vitamina B12 atuam como cofatores para as enzimas envolvidas na biossíntese do DNA. A interrupção deste processo pode impedir o fechamento do tubo neural. A suplementação vitamínica contendo folato pode reduzir as taxas de ocorrência de defeitos de tubo neural, embora exista a preocupação de que esta prevenção possa mascarar a deficiência de vitamina B12. Métodos: dosagens de vitamina B12 e ferritina pelo método de enzimaimunoensaio com micropartículas e a dosagens de ácido fólico pelo método de captura iônica (IMx ABBOTT). Resultados: a porcentagem de gestantes com deficiência de vitamina B12 (níveis séricos < 150 pg/ml) foi de 11,8%. Não houve nenhum caso de deficiência de folato (níveis séricos < 3,0 ng/ml). A prevalência de gestantes com deficiência nos estoques de ferro foi de 47,1% (níveis séricos < 12 ng/ml). Conclusões: com os resultados encontrados neste estudo (prevalência de 11,8% de deficientes em vitamina B12 e 0% de deficiência de folato), sugerimos que a suplementação se realize após a determinação da vitamina B12 sérica.

Efeitos da estimulação vibro-acústica na velocidade do sangue da artéria cerebral média e na freqüência cardíaca fetal: 1. entre a 35ª e 41ª semanas de gestação

RESUMO Objetivos: estudar, em fetos hígidos, quais são as respostas da freqüência cardíaca fetal basal e da resistência na artéria cerebral média à estimulação vibro-acústica padronizada. Métodos: em estado comportamental de hipo ou de inatividade fetal (apnéia e ausência de movimentos corpóreos), mediu-se o índice de pulsatilidade (IP) da artéria cerebral média (ACM), bem como calculou-se a freqüência cardíaca fetal basal (FCFB) pela análise da onda espectral, antes e após a aplicação de estímulo vibro-acústico (EVA) por 3 segundos. Foram empregados ecógrafos de alta resolução, com Doppler pulsado e mapeamento a cores. A fonte sonora emitia som com 400 a 40.000 Hz, sob forma de varredura, com pressão sonora de 65 a 110 dB. Resultados: a média da FCFB pré-estímulo foi 139 bpm, com desvio padrão de 3,14 bpm. A média da FCFB pós-estímulo foi 153 bpm, com desvio padrão de 7,23 bpm (p<0.0001). A média do IP da ACM pré-estímulo foi 1,84, com desvio padrão de 0,07. A média do IP da ACM foi 1,56, com desvio padrão de 0,04 (p<0.00001). Em todos os casos houve resposta do concepto, caracterizada pela evidência de movimento corpóreo vigoroso, aumento da FCFB e redução do IP na ACM. Em nenhum caso houve necessidade de repetir o estímulo vibro-acústico. Conclusões: a aplicação de estímulo vibro-acústico, com as características aqui descritas, em conceptos hígidos e de termo, por período de 3 segundos, determina incremento na FCFB e nos movimentos corpóreos, bem como redução na impedância da ACM. Em que pese haver tendência em se inferir que a EVA determina aumento no fluxo de sangue ao cérebro fetal, os dados aqui evidenciados não permitem interpretações clínicas.