Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models contributed to risk reduction, policy suggestion, minimization of the loss of human life, and reduction the property damage associated with floods. To mimic the complex mathematical expressions of physical processes of floods, during the past two decades, machine learning (ML) methods contributed highly in the advancement of prediction systems providing better performance and cost-effective solutions. Due to the vast benefits and potential of ML, its popularity dramatically increased among hydrologists. Researchers through introducing novel ML methods and hybridizing of the existing ones aim at discovering more accurate and efficient prediction models. The main contribution of this paper is to demonstrate the state of the art of ML models in flood prediction and to give insight into the most suitable models. In this paper, the literature where ML models were benchmarked through a qualitative analysis of robustness, accuracy, effectiveness, and speed are particularly investigated to provide an extensive overview on the various ML algorithms used in the field. The performance comparison of ML models presents an in-depth understanding of the different techniques within the framework of a comprehensive evaluation and discussion. As a result, this paper introduces the most promising prediction methods for both long-term and short-term floods. Furthermore, the major trends in improving the quality of the flood prediction models are investigated. Among them, hybridization, data decomposition, algorithm ensemble, and model optimization are reported as the most effective strategies for the improvement of ML methods.

AI has the power to boost the economy, improve environmental sustainability and create a more equitable society -- but there are dangers associated with its rise, the panel of experts has told. The report was developed to give Australians a reference point to understand AI, and what living in a future dominated by the technology will really mean. AI refers to a collection of technologies which give machines the ability to perform tasks and solve problems that would otherwise require the human brain to carry out. While the U.S. and China are undoubtedly leaders in AI technology, Australia is punching well above its weight in terms of establishing systems for mining, agriculture, and manufacturing. Australia is also the five-time winner of the world robot soccer competition, the Robocup.

Stunning footage has revealed the first evidence that leopard seals share food -- with the marine mammals caught divvying up a penguin as they feast on their kill. The ground-breaking footage was captured by a drone flying off the coast of the island of South Georgia, in the southern Atlantic. Researchers said that leopard seals are normally regarded as being solitary creatures. The Antarctic predators are largely'intolerant' of each other, but can be forced to hunt alongside each another when congregating in areas of plentiful prey, the experts added. The leopard seal is named for its black-spotted coat, whose pattern is similar to that of the big cat, though the seal's coat is grey rather than golden in colour.

Kespry, a drone-based aerial intelligence solution provider, and DroneBase, a drone services company, have announced a partnership to enable insurance, mining, and aggregates enterprises across North America to expand aerial analytics implementation across their worksites. Kespry's customers will now be able to leverage DroneBase to manage their Kespry deployments as part of Kespry's new Bring Your Own Drone (BYOD) program. BYOD includes a new platform pricing model designed to meet the expanding enterprise aerial intelligence requirements of multi-site mining and aggregates companies, as well as large-scale residential and commercial property insurers. The combination of Kespry and DroneBase brings the best of the platform and services worlds together, offering a cost-effective, productive way of using drone-based analytics across the largest insurance, mining, and aggregates businesses. "We're very pleased to work with DroneBase and its team of dedicated, aerial intelligence professionals to further expand Kespry insurance, mining and aggregates deployments across the country," said George Mathew, CEO, Kespry.

A company's most valuable asset is its human capital. In fact, people skills were ranked as the third most important force that will affect enterprises in the next two years by more than 12,800 C-level executives who participated in the most recent IBM Global C-suite Study. In addition, only half of the 2,100 CHRO participants said they currently have the people skills and resources to execute their business strategies. As Gina Dellabarca, General Manager of Human Resources for Westpac New Zealand, says in the C-suite Study report: "Our most important priority in HR is finding talent for the future, not just for now. We're focused on the formidable challenge of attracting, developing, and retaining employees with skills we haven't yet determined."

Scientists have developed an automated system that uses artificial intelligence (AI) to detect and capture fast radio bursts (FRBs) in real-time. FRBs are mysterious and powerful flashes of radio waves from space, thought to originate billions of light years from the Earth, said researchers from Swinburne University of Technology in Australia. They last for only a few milliseconds or a thousandth of a second and their cause is one of astronomy's biggest puzzles. Astronomical Society, has already identified five bursts -- including one of the most energetic ever detected, as well as the broadest. Wael Farah from Swinburne University of Technology trained the on-site computer at the Molonglo Radio Observatory in Australia to recognise the signs and signatures of FRBs, and trigger an immediate capture of the finest details seen to date.

We introduce the HSIC (Hilbert-Schmidt independence criterion) bottleneck for training deep neural networks. The HSIC bottleneck is an alternative to conventional backpropagation, that has a number of distinct advantages. The method facilitates parallel processing and requires significantly less operations. It does not suffer from exploding or vanishing gradients. It is biologically more plausible than backpropagation as there is no requirement for symmetric feedback. We find that the HSIC bottleneck provides a performance on the MNIST/FashionMNIST/CIFAR10 classification comparable to backpropagation with a cross-entropy target, even when the system is not encouraged to make the output resemble the classification labels. Appending a single layer trained with SGD (without backpropagation) results in state-of-the-art performance.

We present the first unsupervised deep learning method for pollen analysis using bright-field microscopy. Using a modest dataset of 650 images of pollen grains collected from honey, we achieve family level identification of pollen. We embed images of pollen grains into a low-dimensional latent space and compare Euclidean and Riemannian metrics on these spaces for clustering. We propose this system for automated analysis of pollen and other microscopic biological structures which have only small or unlabelled datasets available.

Artificial Intelligence (AI) has incredible potential to improve lives and create a better world, but the stakes are high and the consequences will be disastrous if the technology is misused. Those are the findings of a new "horizon scanning" report by the Australian Council of Learned Academies (ACOLA), titled The Effective and Ethical Development of Artificial Intelligence – An Opportunity to Improve our Wellbeing. "Horizon scanning" is a way for governments and decision-makers to "look at the future challenges and opportunities that the technologies pose", UNSW Professor of Artificial Intelligence Toby Walsh, co-chair of the report's expert working group, said. The report draws on research and expertise from a wide range of disciplines including science, medicine, economics, philosophy and law. At its best, AI has the power to enhance Australia's wellbeing, lift the economy, improve environmental sustainability and create a more equitable, inclusive and fair society, the report said.

It is a challenging and complex task to acquire information from different regions of a disaster-affected area in a timely fashion. The extensive spread and reach of social media and networks allow people to share information in real-time. However, the processing of social media data and gathering of valuable information require a series of operations such as (1) processing each specific tweet for a text classification, (2) possible location determination of people needing help based on tweets, and (3) priority calculations of rescue tasks based on the classification of tweets. These are three primary challenges in developing an effective rescue scheduling operation using social media data. In this paper, first, we propose a deep learning model combining attention based Bi-directional Long Short-Term Memory (BLSTM) and Convolutional Neural Network (CNN) to classify the tweets under different categories. We use pre-trained crisis word vectors and global vectors for word representation (GLoVe) for capturing semantic meaning from tweets. Next, we perform feature engineering to create an auxiliary feature map which dramatically increases the model accuracy. In our experiments using real data sets from Hurricanes Harvey and Irma, it is observed that our proposed approach performs better compared to other classification methods based on Precision, Recall, F1-score, and Accuracy, and is highly effective to determine the correct priority of a tweet. Furthermore, to evaluate the effectiveness and robustness of the proposed classification model a merged dataset comprises of 4 different datasets from CrisisNLP and another 15 different disasters data from CrisisLex are used. Finally, we develop an adaptive multitask hybrid scheduling algorithm considering resource constraints to perform an effective rescue scheduling operation considering different rescue priorities.