“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change.

Tackling Climate Change with Machine Learning

A Rainfall-Runoff Model With LSTM-Based Sequence-to-Sequence Learning

https://eartharxiv.org/repository/object/149/download/296/

A comprehensive review of deep learning applications in hydrology and water resources

With the development of 5G and Internet of Things (IoT), the era of big data‐driven product design is booming. In addition, artificial intelligence (AI) is also emerging and evolving by recent breakthroughs in computing power and software architectures. In this regard, the digital twin, analyzing various sensor data with the help of AI algorithms, has become a cutting‐edge technology that connects the physical and virtual worlds, in which the various sensors are highly desirable to collect environmental information. However, although existing sensor technologies, including cameras, microphones, inertial measurement units, etc., are widely used as sensing elements for various applications, high‐power consumption and battery replacement of them is still a problem. Triboelectric nanogenerators (TENGs) as self‐powered sensors supply a feasible platform for realizing self‐sustainable and low‐power systems. Herein, the recent progress on TENG‐based intelligent systems, that is, wearable electronics, robot‐related systems, and smart homes, followed by prospective future development enabled by sensor fusion technology, is focused on. Finally, how to apply artificial intelligence to the design of intelligent sensor systems for the 5G and IoT era is discussed.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/aisy.202100228

Artificial Intelligence‐Enabled Sensing Technologies in the 5G/Internet of Things Era: From Virtual Reality/Augmented Reality to the Digital Twin

We introduce an analytical framework for analyzing tweets to (1) identify and categorize fine-grained details about a disaster such as affected individuals, damaged infrastructure and disrupted ser...

Identifying disaster-related tweets and their semantic, spatial and temporal context using deep learning, natural language processing and spatial analysis: a case study of Hurricane Irma

Predicting flood for any location at times of extreme storms is a longstanding problem that has utmost importance in emergency management. Conventional methods that aim to predict water levels in streams use advanced hydrological models still lack of giving accurate forecasts everywhere. This study aims to explore artificial deep neural networks performance on flood prediction. While providing models that can be used in forecasting stream stage, this paper presents a dataset that focuses on the connectivity of data points on river networks. It also shows that neural networks can be very helpful in time-series forecasting as in flood events, and support improving existing models through data assimilation.

Decentralized Flood Forecasting Using Deep Neural Networks

The advancements and new techniques in information technologies are making it possible to acquire large-scale spatial data through satellites, radars and sensor networks. The collection of vast amounts of environmental data increased the demand for applications which can manage and process large-scale and high-resolution data sets in real-time. One of the important tasks for organizing and customizing hydrological data sets is the delineation of watersheds on demand. Watershed delineation is a process for creating a boundary that represents the contributing area for a specific control point or water outlet, with the intent of characterization and analysis of portions of a study area. Although many GIS tools and software are available for watershed analysis on desktop systems, there is a need for optimized libraries for client-side and server-side web applications for creating a dynamic and interactive environment for exploring hydrological data. In this project, we developed and demonstrated several watershed delineation techniques on the web, with seven different use cases implemented on the client-side using JavaScript, WebAssembly, and WebGL and on the server-side using Python, Go, C, and Node.js. We also developed a client-side GPGPU (General Purpose Graphical Processing Unit) algorithm to analyze high-resolution terrain data for watershed delineation by benefiting from the parallelizable nature of GPUs. The web-based real-time analysis of watershed segmentation can be helpful for decision-makers and stakeholders while eliminating the need of installing complex software packages and dealing with large-scale data sets.

/pdf/optimized-watershed-delineation-library-for-server-side-and-51o4ar0nz8.pdf

Optimized watershed delineation library for server-side and client-side web applications

This article presents the vision, implementation, and case studies of the Iowa Flood Information System (IFIS) towards the vision for next-generation decision support systems for flooding. The IFIS...

Muhammed Ali Sit

Papers

A comprehensive review of deep learning applications in hydrology and water resources

Identifying disaster-related tweets and their semantic, spatial and temporal context using deep learning, natural language processing and spatial analysis: a case study of Hurricane Irma

Decentralized Flood Forecasting Using Deep Neural Networks

Optimized watershed delineation library for server-side and client-side web applications

FLOODSS: Iowa flood information system as a generalized flood cyberinfrastructure