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With the aid of Twitter and AI, researchers to develop flood warning system

In a study, published in the journal Computers & Geosciences, the researchers showed how AI can be used to extract data from Twitter and crowdsourced information from mobile phone apps to build up hyper-resolution monitoring of urban flooding.

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AI can play a key role in future flood warning and monitoring systems
AI can play a key role in future flood warning and monitoring systems

London, Dec 26: Researchers are combining Twitter, citizen science and artificial intelligence (AI) techniques to develop an early-warning system for flood-prone communities in urban areas.

In a study, published in the journal Computers & Geosciences, the researchers showed how AI can be used to extract data from Twitter and crowdsourced information from mobile phone apps to build up hyper-resolution monitoring of urban flooding.

“By combining social media, citizen science and artificial intelligence in urban flooding research, we hope to generate accurate predictions and provide warnings days in advance,” said Roger Wang from University of Dundee in Britain.

Urban flooding is difficult to monitor due to complexities in data collection and processing.

This prevents detailed risk analysis, flooding control and the validation of numerical models.

The research team set about trying to solve this problem by exploring how the latest AI technology can be used to mine social media and apps for the data that users provide.

They found that social media and crowdsourcing can be used to complement datasets based on traditional remote sensing and witness reports.

Applying these methods in case studies, they found them to be genuinely informative and that AI can play a key role in future flood warning and monitoring systems.

“The present recording systems — remote satellite sensors, a local sensor network, witness statements and insurance reports — all have their disadvantages. Therefore, we were forced to think outside the box and one of the things that occurred to us was how Twitter users provide real-time commentary on floods,” Wang said.

“A tweet can be very informative in terms of flooding data. Key words were our first filter, then we used natural language processing to find out more about severity, location and other information,” Wang said.

The researchers applied computer vision techniques to the data collected from MyCoast, a crowdsourcing app, to automatically identify scenes of flooding from the images that users post.

“We found these big data-based flood monitoring approaches can definitely complement the existing means of data collection and demonstrate great promise for improving monitoring and warnings in future,” Wang said.

Twitter data was streamed over a one-month period in 2015, with the filtering keywords of “flood”, “inundation”, “dam”, “dike”, and “levee”. More than 7,500 tweets were analysed over this time.

“We have reached the point of 70 per cent accuracy and we are using the thousands of images available on MyCoast to further improve this,” Wang said.

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Google Claims Eye Doctors Can Turn More Effective Using AI

Without assistance, general ophthalmologists are significantly less accurate than the algorithm, while retina specialists are not significantly more accurate than the algorithm. 

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The research team at Google AI believes that some of these pitfalls may be avoided if the computer can "explain" its predictions. Pixabay

As Artificial Intelligence (AI) continues to evolve, diagnosing diseases has become faster with greater accuracy. A new study from the Google AI research group shows that physicians and algorithms working together are more effective than either one alone.

In the study, to be published in the journal Ophthalmology, the researchers created a system which not only improved the ophthalmologists’ diagnostic accuracy but also improved the algorithm’s accuracy.

The study expands on previous work from Google AI showing that its algorithm works roughly as well as human experts in screening patients for a common diabetic eye disease called diabetic retinopathy.

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To test this theory, ten ophthalmologists (four general ophthalmologists, one trained outside the US, four retina specialists, and one retina specialist in training) were asked to read images with and without algorithm assistance. Pixabay

“What we found is that AI can do more than simply automate eye screening, it can assist physicians in more accurately diagnosing diabetic retinopathy. AI and physicians working together can be more accurate than either one alone,” said lead researcher Rory Sayres.

Recent advances in AI promise to improve access to diabetic retinopathy screening and to improve its accuracy. But it’s less clear how AI will work in the physician’s office or other clinical settings, the team said.

According to the team, previous attempts to use computer-assisted diagnosis shows that some screeners rely on the machine too much, which leads to repeating the machine’s errors, or under-rely on it and ignore accurate predictions.

The research team at Google AI believes that some of these pitfalls may be avoided if the computer can “explain” its predictions.

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Recent advances in AI promise to improve access to diabetic retinopathy screening and to improve its accuracy. But it’s less clear how AI will work in the physician’s office or other clinical settings, the team said. Pixabay

To test this theory, ten ophthalmologists (four general ophthalmologists, one trained outside the US, four retina specialists, and one retina specialist in training) were asked to read images with and without algorithm assistance.

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Without assistance, general ophthalmologists are significantly less accurate than the algorithm, while retina specialists are not significantly more accurate than the algorithm.

With assistance, general ophthalmologists match but do not exceed the model’s accuracy, while retina specialists start to exceed the model’s performance. (IANS)