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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Water Stress Around 2000 A.D. By WaterGAP.jpg|280px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Bispo-Silva Geosciences23 13-11.png|240px]]</div>
'''[[Hydroinformatics]]''' is the multidisciplinary application of information and decision support systems to address the equitable and efficient management and use of water for many different purposes. Hydroinformatics draws on and integrates hydraulics, hydrology, environmental engineering, and many other disciplines. It sees application at all points in the water cycle, from atmosphere to ocean, and in artificial interventions in that cycle such as urban drainage and water supply systems. It provides support for decision making at all levels, from governance and policy through to management and operations.
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


Hydroinformatics also recogniszs the inherently social nature of the problems of water management and of decision making processes, and it includes mechanisms towards understanding the social processes by which technologies are brought into use and how they change the water system. Since the resources to obtain and develop technological solutions affecting water collection, purification, and distribution continue to be concentrated in the hands of the minority, the need to examine these social processes are particularly acute. Hydroinformatics can help tackle problems and tasks such as improving shallow-water flow models, optimizing damn breaks, and constructing bridges across bodies of water. ('''[[Hydroinformatics|Full article...]]''')<br />
[[Chromatography|Chromatographic]] oil analysis is an important step for the identification of biodegraded petroleum via peak visualization and interpretation of phenomena that explain the oil geochemistry. However, analyses of chromatogram components by geochemists are comparative, visual, and consequently slow. This article aims to improve the chromatogram analysis process performed during geochemical interpretation by proposing the use of [[convolutional neural network]]s (CNN), which are deep learning techniques widely used by big tech companies. Two hundred and twenty-one (221) chromatographic oil images from different worldwide basins (Brazil, USA, Portugal, Angola, and Venezuela) were used. The [[open-source software]] Orange Data Mining was used to process images by CNN. The CNN algorithm extracts, pixel by pixel, recurring features from the images through convolutional operations ... ('''[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Full article...]]''')<br />
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''Recently featured'':
''Recently featured'': [[Software as a service]], [[Health informatics]], [[Content delivery network]]
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}}

Revision as of 13:37, 13 May 2024

Fig1 Bispo-Silva Geosciences23 13-11.png

"Geochemical biodegraded oil classification using a machine learning approach"

Chromatographic oil analysis is an important step for the identification of biodegraded petroleum via peak visualization and interpretation of phenomena that explain the oil geochemistry. However, analyses of chromatogram components by geochemists are comparative, visual, and consequently slow. This article aims to improve the chromatogram analysis process performed during geochemical interpretation by proposing the use of convolutional neural networks (CNN), which are deep learning techniques widely used by big tech companies. Two hundred and twenty-one (221) chromatographic oil images from different worldwide basins (Brazil, USA, Portugal, Angola, and Venezuela) were used. The open-source software Orange Data Mining was used to process images by CNN. The CNN algorithm extracts, pixel by pixel, recurring features from the images through convolutional operations ... (Full article...)
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