Sign up for new issue notifications. Aeration is improved in rivers by the turbulence created in the flow over macro and intermediate roughness conditions.
Computational intelligence in earth sciences and environmental applications: issues and challenges.
Macro and intermediate roughness flow conditions are generated by flows over block ramps or rock chutes. The measurements are taken in uniform flow region. Efficacy of soft computing methods in modeling hydraulic parameters are not common so far. In this study, modeling efficiencies of MPMR model and FFNN model are found for estimating the air concentration over block ramps under macro roughness conditions. The experimental data are used for training and testing phases.
Content from this work may be used under the terms of the Creative Commons Attribution 3. Any further distribution of this work must maintain attribution to the author s and the title of the work, journal citation and DOI. Techniques covered include artificial neural networks, support vector machines, fuzzy logic, decision-making algorithms, supervised and unsupervised classification algorithms, probabilistic computing, hybrid methods and morphic computing.
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Further topics given treatment in this volume include remote sensing, meteorology, atmospheric and oceanic modeling, climate change, environmental engineering and management, catastrophic natural hazards, air and environmental pollution and water quality. By linking computational intelligence techniques with earth and environmental science oriented problems, this book promotes synergistic activities among scientists and technicians working in areas such as data mining and machine learning.
We believe that a diverse group of academics, scientists, environmentalists, meteorologists and computing experts with a common interest in computational intelligence techniques within the earth and environmental sciences will find this book to be of great value. Prashant K. Currently, she is working as a research consultant for satellite based slum area delineation. It provides definitions of methods and approaches, and also includes examples where they have been applied to solve complex big data problems. Readers will find that this book helps them to understand the processes involved for environmental related complex problems, which is the purpose of computational intelligence from the outset.
In , there were weather and disaster events, triple the number that occurred in Twenty percent of species currently face extinction, and that number could rise to 50 percent by But we have a new tool to help us better manage the impacts of climate change and protect the planet: artificial intelligence AI. In India, AI has helped farmers get 30 percent higher groundnut yields per hectare by providing information on preparing the land, applying fertilizer and choosing sowing dates.
In Norway, AI helped create a flexible and autonomous electric grid, integrating more renewable energy.
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An atmospheric river over California. Photo: NOAA. And AI has helped researchers achieve 89 to 99 percent accuracy in identifying tropical cyclones, weather fronts and atmospheric rivers, the latter of which can cause heavy precipitation and are often hard for humans to identify on their own. By improving weather forecasts, these types of programs can help keep people safe. AI can now quickly discern patterns that humans cannot, make predictions more efficiently and recommend better policies.
The holy grail of artificial intelligence research is artificial general intelligence, when computers will be able to reason, abstract, understand and communicate like humans. But we are still far from that—it takes 83, processors 40 minutes to compute what one percent of the human brain can calculate in one second.
Enterprise Artificial Intelligence (EAI) < Northeastern University
What exists today is narrow AI , which is task-oriented and capable of doing some things, sometimes better than humans can do, such as recognizing speech or images and forecasting weather. Playing chess and classifying images, as in the tagging of people on Facebook, are examples of narrow AI.
AI considers its next move in chess. Photo: viegas.
Machine learning, which developed out of earlier AI, involves the use of algorithms sets of rules to follow to solve a problem that can learn from data. The more data the system analyzes, the more accurate it becomes as the system develops its own rules and the software evolves to achieve its goal. Deep learning , a subset of machine learning, involves neural networks made up of multiple layers of connections or neurons, much like the human brain.
Each layer has a separate task and as information passes through, the neurons give it a weight based on its accuracy vis a vis the assigned task. The final result is determined by the total of the weights. Art created by deep learning. Photo: Gene Kogan. Eventually it will help scale up and commercialize the most promising projects. Uriarte and her colleagues want to know how tropical storms, which may worsen with climate change, affect the distribution of tree species in Puerto Rico.
But how is it possible to tell one species from another by looking at a green mass from above over such a large area? The human eye could theoretically do it, but it would take forever to process the thousands of images.
Photo: Kevin Krajick. Using the ground information from these specific plots, AI can figure out what the various species of trees look like from above in the flyover images.
Understanding how the distribution and composition of forests change in response to hurricanes is important because when forests are damaged, vegetation decomposes and emits more CO2 into the atmosphere. As trees grow back, since they are smaller, they store less carbon.
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If climate change results in more extreme storms, some forests will not recover, less carbon will be stored, and more carbon will remain in the atmosphere, exacerbating global warming. Uriarte says her work could not be done without artificial intelligence. It allows us to ask questions at a scale that we could not ask from below.
How AI is Helping Us Better Understand the Environment
The flyovers and the AI tools are going to allow us to study hurricanes in a whole different way. With a grant from Microsoft, the organization will improve an ecosystem model that gathers data about salmon and steelhead growth, tracks fish and marine mammal movements, and monitors marine conditions. The model will help improve hatchery, harvest, and ecosystem management, and support habitat protection and restoration efforts.