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Registros recuperados: 15 | |
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Tavanti,Renan F. R.; Freddi,Onã da S.; Tavanti,Tauan R.; Rigotti,Adriel; Magalhães,Wellington de A.. |
ABSTRACT The least limiting water range (LLWR) is a soil physical quality indicator that receives much attention. It has been criticized and put to the test regarding mathematical models that compose it since they describe the behavior of soil physical attributes in a simplified way. This study aimed to assess the efficiency of some pedofunctions proposed in the literature and artificial neural networks on the accuracy in predicting soil water retention at potentials equivalent to field capacity (θFC) and permanent wilting point (θPWP). In other words, to apply the best models to LLWR of two soil types (Oxisol and Ultisol) and verify changes in their structure. The results indicated that pedofunctions using sand, silt, clay, bulk density, and soil organic... |
Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Soil physics; Soil physical quality indicator; Available water; Pedotransfer functions; Artificial neural networks. |
Ano: 2019 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162019000400444 |
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Pereira,Tonismar dos S.; Robaina,Adroaldo D.; Peiter,Marcia X.; Torres,Rogerio R.; Bruning,Jhosefe. |
ABSTRACT The aim of this study was to present and to evaluate methodologies for the estimation of soil resistance to penetration (RP) using artificial intelligence prediction techniques. In order to do so, a data base with values of physical-water characteristics of the soils available in the literature was used, and the performances of Artificial Neural Networks (ANN) and Support Vector Machines (SVM) were evaluated. The models generated from the ANNs were implemented through the multilayer perceptron with backpropagation algorithm of Matlab software, varying the number of neurons in the input and intermediate layers. For the procedure from SVM, the RapidMiner software was used, varying input variables, the kernel function and the coefficients of these... |
Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Soil compaction; Machine learning; Support vector machines; Artificial neural networks. |
Ano: 2018 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162018000100142 |
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Rochelle-newall, Emma. J.; Winter, Christian; Barrón, Cristina; Borges, Alberto V.; Duarte, Carlos M.; Elliott, Mike; Frankignoulle, Michel; Gazeau, Frederic; Middelburg, Jack J.; Pizay, Marie-dominique; Gattuso, Jean-pierre. |
Knowing the metabolic balance of an ecosystem is of utmost importance in determining whether the system is a net source or net sink of carbon dioxide to the atmosphere. However, obtaining these estimates often demands significant amounts of time and manpower. Here we present a simplified way to obtain an estimation of ecosystem metabolism. We used artificial neural networks (ANNs) to develop a mathematical model of the gross primary production to community respiration ratio (GPP:CR) based on input variables derived from three widely contrasting European coastal ecosystems (Scheldt Estuary, Randers Fjord, and Bay of Palma). Although very large gradients of nutrient concentration, light penetration, and organic-matter concentration exist across the sites,... |
Tipo: Text |
Palavras-chave: Artificial neural networks; Coastal ecosystems; Metabolic balance; Primary production; Respiration. |
Ano: 2007 |
URL: https://archimer.ifremer.fr/doc/00247/35857/34378.pdf |
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Cortese, G; Dolven, Jk; Bjorklund, Kr; Malmgren, Ba. |
Artificial Neural Networks (ANN) were trained by using an extensive radiolarian census dataset from the Nordic (Greenland, Norwegian, and Iceland) Seas. The regressions between observed and predicted Summer Sea Temperature (SST) indicate that lower error margins and better correlation coefficients are obtained for 100 m (SST100) compared to 10 m (SST10) water depth, and by using a subset of species instead of all species. The trained ANNs were subsequently applied to radiolarian data from two Norwegian Sea cores, HM 79-4 and MD95-2011, for reconstructions of SSTs through the last 15,000 years. The reconstructed SST is quite high during the Bolling-Allerod, when it reaches values only found later during the warmest phase of the Holocene. The climatic... |
Tipo: Text |
Palavras-chave: Artificial neural networks; Radiolarians; Nordic seas; Late Pleistocene; Holocene. |
Ano: 2005 |
URL: https://archimer.ifremer.fr/doc/00229/34074/32535.pdf |
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Calderano Filho,Braz; Polivanov,Helena; Chagas,César da Silva; Carvalho Júnior,Waldir de; Barroso,Emílio Velloso; Guerra,Antônio José Teixeira; Calderano,Sebastião Barreiros. |
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... |
Tipo: Info:eu-repo/semantics/other |
Palavras-chave: Artificial neural networks; Terrain attributes; Digital mapping. |
Ano: 2014 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832014000600003 |
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Bortolini,Diego; Albuquerque,Jackson Adriano. |
ABSTRACT: Soil water retention and availability are important properties for agricultural production, which can be measured directly or estimated by pedotransfer functions. Some studies on this topic were carried out in Santa Catarina, Brazil. To improve the estimates, it is necessary to evaluate other properties, to analyze more soil types, as well as to use other analysis techniques such as artificial neural networks and regression trees. Thus, the objective of the study was to estimate the field capacity (FC), permanent wilting point (PWP), and available water (AW) in soils of Santa Catarina (SC), through multiple linear regressions (MLR), artificial neural networks (ANN), and regression trees (RT), more efficiently than the current pedotransfer... |
Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Pedotransfer functions; Water retention curve; Artificial neural networks; Regression trees; Multiple linear regressions. |
Ano: 2018 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832018000100424 |
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Dornelles,Eldair F.; Kraisig,Adriana R.; Silva,José A. G. da; Sawicki,Sandro; Roos-Frantz,Fabricia; Carbonera,Roberto. |
ABSTRACT Artificial intelligence may represent an efficient strategy for simulation and optimization of important processes in agriculture. The main goal of the study is to propose the use of artificial intelligence, namely artificial neural networks and genetic algorithms, respectively, in the simulation of oat grain yield and optimization of seeding density, considering the main succession systems of southern Brazil. The study was conducted in a randomized complete block design with four replicates, following a 4 x 2 factorial scheme, for seeding densities (100, 300, 600 and 900 seeds m-2) and oat cultivars (Brisasul and URS Taura), in succession systems of corn/oats and soybean/oats. A multi-layered artificial neural network and a genetic algorithm were... |
Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Avena sativa; Artificial neural networks; Genetic algorithms; Innovation. |
Ano: 2018 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1415-43662018000300183 |
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Coelho,Fabrício Fernandes; Giasson,Elvio; Campos,Alcinei Ribeiro; Tiecher,Tales; Costa,José Janderson Ferreira; Coblinski,João Augusto. |
ABSTRACT: In Brazil several digital soil class mapping studies were carried out from 2006 onwards to maximize the use of existing maps and information and to provide estimates for wider areas. However, there is no consensus on which methods have produced superior results in the predictive value of soil maps. This study conducts a systematic review of digital soil class mapping in Brazil and aims to analyze the factors which can improve the accuracy of digital soil class maps. Data from 334 digital soil class mapping studies were grouped and analyzed by Student's t-test, Wilcoxon-Mann-Whitney test and Kruskal-Wallis test. When conventional maps were used for validation, the studies showed average values of 63 % and when field samples were used, 56 % for... |
Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Pedology; Mapping unit density; Artificial neural networks; Soil-forming factors; Overall accuracy. |
Ano: 2021 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162021000501401 |
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Registros recuperados: 15 | |
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