scholarly journals Optical flow models as an open benchmark for radar-based precipitation nowcasting (rainymotion v0.1)

2018 ◽  
Author(s):  
Georgy Ayzel ◽  
Maik Heistermann ◽  
Tanja Winterrath
Keyword(s):  
Optical Flow ◽  
State Of The Art ◽  
Model Development ◽  
Software Library ◽  
Flow Models ◽  
Urban Sewage ◽  
Radar Images ◽  
Software Implementations ◽  
Python Programming ◽  
Prediction Approach

Abstract. Quantitative precipitation nowcasting (QPN) has become an essential technique in various application contexts, such as early warning or urban sewage control. A common heuristic prediction approach is to track the motion of precipitation features from a sequence of weather radar images, and then to extrapolate that motion to the imminent future (minutes to hours), assuming that the intensity of the features remains constant (Lagrangian persistence). In that context, optical flow has become one of the most popular tracking techniques. Yet, the present landscape of computational QPN models still struggles with producing open software implementations. Focusing on this gap, we have developed and extensively benchmarked a stack of models based on different optical flow algorithms for the tracking step, and a set of parsimonious extrapolation procedures based on image warping and advection. We demonstrate that these models provide skillful predictions comparable with or even superior to state-of-the-art operational software. Our software library (rainymotion) for precipitation nowcasting is written in Python programming language, and openly available at GitHub (https://github.com/hydrogo/rainymotion). That way, the library may serve as a tool for providing fast, free and transparent solutions that could serve as a benchmark for further model development and hypothesis testing – a benchmark that is far more advanced than the conventional benchmark of Eulerian persistence commonly used in QPN verification experiments.

scholarly journals Optical flow models as an open benchmark for radar-based precipitation nowcasting (rainymotion v0.1)

2019 ◽  
Vol 12 (4) ◽  
pp. 1387-1402 ◽  
Author(s):  
Georgy Ayzel ◽  
Maik Heistermann ◽  
Tanja Winterrath
Keyword(s):  
Optical Flow ◽  
State Of The Art ◽  
Model Development ◽  
Precipitation Field ◽  
Flow Models ◽  
Urban Sewage ◽  
Radar Images ◽  
Software Implementations ◽  
Python Programming ◽  
Prediction Approach

Abstract. Quantitative precipitation nowcasting (QPN) has become an essential technique in various application contexts, such as early warning or urban sewage control. A common heuristic prediction approach is to track the motion of precipitation features from a sequence of weather radar images and then to displace the precipitation field to the imminent future (minutes to hours) based on that motion, assuming that the intensity of the features remains constant (“Lagrangian persistence”). In that context, “optical flow” has become one of the most popular tracking techniques. Yet the present landscape of computational QPN models still struggles with producing open software implementations. Focusing on this gap, we have developed and extensively benchmarked a stack of models based on different optical flow algorithms for the tracking step and a set of parsimonious extrapolation procedures based on image warping and advection. We demonstrate that these models provide skillful predictions comparable with or even superior to state-of-the-art operational software. Our software library (“rainymotion”) for precipitation nowcasting is written in the Python programming language and openly available at GitHub (https://github.com/hydrogo/rainymotion, Ayzel et al., 2019). That way, the library may serve as a tool for providing fast, free, and transparent solutions that could serve as a benchmark for further model development and hypothesis testing – a benchmark that is far more advanced than the conventional benchmark of Eulerian persistence commonly used in QPN verification experiments.

scholarly journals Performance Comparison between Deep Learning and Optical Flow-Based Techniques for Nowcast Precipitation from Radar Images

Forecasting ◽  
2020 ◽  
Vol 2 (2) ◽  
pp. 194-210 ◽  
Author(s):  
Marino Marrocu ◽  
Luca Massidda
Keyword(s):  
Neural Network ◽  
Optical Flow ◽  
State Of The Art ◽  
Performance Comparison ◽  
Data Set ◽  
Radar Images ◽  
The Neural Network ◽  
Meteorological Radar ◽  
One Year ◽  
Better Than

In this article, a nowcasting technique for meteorological radar images based on a generative neural network is presented. This technique’s performance is compared with state-of-the-art optical flow procedures. Both methods have been validated using a public domain data set of radar images, covering an area of about 104 km2 over Japan, and a period of five years with a sampling frequency of five minutes. The performance of the neural network, trained with three of the five years of data, forecasts with a time horizon of up to one hour, evaluated over one year of the data, proved to be significantly better than those obtained with the techniques currently in use.

scholarly journals Clinically applicable artificial intelligence system for dental diagnosis with CBCT

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Matvey Ezhov ◽  
Maxim Gusarev ◽  
Maria Golitsyna ◽  
Julian M. Yates ◽  
Evgeny Kushnerev ◽  
...  
Keyword(s):  
State Of The Art ◽  
Model Development ◽  
Clinical Effectiveness ◽  
Anatomical Landmarks ◽  
Periapical Lesion ◽  
Artificial Intelligence System ◽  
Intelligence System ◽  
Significant Difference ◽  
Diagnostic Capabilities ◽  
Lesion Localization

AbstractIn this study, a novel AI system based on deep learning methods was evaluated to determine its real-time performance of CBCT imaging diagnosis of anatomical landmarks, pathologies, clinical effectiveness, and safety when used by dentists in a clinical setting. The system consists of 5 modules: ROI-localization-module (segmentation of teeth and jaws), tooth-localization and numeration-module, periodontitis-module, caries-localization-module, and periapical-lesion-localization-module. These modules use CNN based on state-of-the-art architectures. In total, 1346 CBCT scans were used to train the modules. After annotation and model development, the AI system was tested for diagnostic capabilities of the Diagnocat AI system. 24 dentists participated in the clinical evaluation of the system. 30 CBCT scans were examined by two groups of dentists, where one group was aided by Diagnocat and the other was unaided. The results for the overall sensitivity and specificity for aided and unaided groups were calculated as an aggregate of all conditions. The sensitivity values for aided and unaided groups were 0.8537 and 0.7672 while specificity was 0.9672 and 0.9616 respectively. There was a statistically significant difference between the groups (p = 0.032). This study showed that the proposed AI system significantly improved the diagnostic capabilities of dentists.

Fusion of rain radar images and wind forecasts in adeep learning model applied to rain nowcasting

2021 ◽  
Author(s):  
Anastase Charantonis ◽  
Vincent Bouget ◽  
Dominique Béréziat ◽  
Julien Brajard ◽  
Arthur Filoche
Keyword(s):  
Deep Learning ◽  
Optical Flow ◽  
Weather Forecast ◽  
Forecast Model ◽  
Radar Data ◽  
Learning Model ◽  
Rainfall Forecasting ◽  
Radar Images ◽  
Deep Learning Model ◽  
Rain Radar

<p>Short or mid-term rainfall forecasting is a major task with several environmental applications such as agricultural management or flood risks monitoring. Existing data-driven approaches, especially deep learning models, have shown significant skill at this task, using only rainfall radar images as inputs. In order to determine whether using other meteorological parameters such as wind would improve forecasts, we trained a deep learning model on a fusion of rainfall radar images and wind velocity produced by a weather forecast model. The network was compared to a similar architecture trained only on radar data, to a basic persistence model and to an approach based on optical flow. Our network outperforms by 8% the F1-score calculated for the optical flow on moderate and higher rain events for forecasts at a horizon time of 30 minutes. Furthermore, it outperforms by 7% the same architecture trained using only rainfall radar images. Merging rain and wind data has also proven to stabilize the training process and enabled significant improvement especially on the difficult-to-predict high precipitation rainfalls. These results can also be found in Bouget, V., Béréziat, D., Brajard, J., Charantonis, A., & Filoche, A. (2020). Fusion of rain radar images and wind forecasts in a deep learning model applied to rain nowcasting. arXiv preprint arXiv:2012.05015</p>

scholarly journals Using a virtual machine environment for developing, testing, and training for the UM-UKCA composition-climate model, using Unified Model version 10.9 and above

2018 ◽  
Vol 11 (9) ◽  
pp. 3647-3657 ◽  
Author(s):  
Nathan Luke Abraham ◽  
Alexander T. Archibald ◽  
Paul Cresswell ◽  
Sam Cusworth ◽  
Mohit Dalvi ◽  
...  
Keyword(s):  
Virtual Machine ◽  
Climate Model ◽  
State Of The Art ◽  
Model Development ◽  
Unified Model ◽  
Model Version ◽  
Weather And Climate ◽  
Interactive Composition ◽  
Comprehensive Test ◽  
And Training

Abstract. The Met Office Unified Model (UM) is a state-of-the-art weather and climate model that is used operationally worldwide. UKCA is the chemistry and aerosol sub model of the UM that enables interactive composition and physical atmosphere interactions, but which adds an additional 120 000 lines of code to the model. Ensuring that the UM code and UM-UKCA (the UM running with interactive chemistry and aerosols) is well tested is thus essential. While a comprehensive test harness is in place at the Met Office and partner sites to aid in development, this is not available to many UM users. Recently, the Met Office have made available a virtual machine environment that can be used to run the UM on a desktop or laptop PC. Here we describe the development of a UM-UKCA configuration that is able to run within this virtual machine while only needing 6 GB of memory, before discussing the applications of this system for model development, testing, and training.

scholarly journals Hallucinating Optical Flow Features for Video Classification

2019 ◽  
Author(s):  
Yongyi Tang ◽  
Lin Ma ◽  
Lianqiang Zhou
Keyword(s):  
Optical Flow ◽  
Data Storage ◽  
Large Scale ◽  
State Of The Art ◽  
Video Content ◽  
Video Classification ◽  
Temporal Relationships ◽  
Optical Flow Computation ◽  
Flow Features ◽  
Optical Flow Features

Appearance and motion are two key components to depict and characterize the video content. Currently, the two-stream models have achieved state-of-the-art performances on video classification. However, extracting motion information, specifically in the form of optical flow features, is extremely computationally expensive, especially for large-scale video classification. In this paper, we propose a motion hallucination network, namely MoNet, to imagine the optical flow features from the appearance features, with no reliance on the optical flow computation. Specifically, MoNet models the temporal relationships of the appearance features and exploits the contextual relationships of the optical flow features with concurrent connections. Extensive experimental results demonstrate that the proposed MoNet can effectively and efficiently hallucinate the optical flow features, which together with the appearance features consistently improve the video classification performances. Moreover, MoNet can help cutting down almost a half of computational and data-storage burdens for the two-stream video classification. Our code is available at: https://github.com/YongyiTang92/MoNet-Features

scholarly journals State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Ramin Keivani ◽  
Sina Faizollahzadeh Ardabili ◽  
Farshid Aram
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
State Of The Art ◽  
Smart Cities ◽  
Model Development ◽  
Urban Sustainability ◽  
Urban Transport ◽  
Support Vector ◽  
Neuro Fuzzy ◽  
Vector Machines

Deep learning (DL) and machine learning (ML) methods have recently contributed to the advancement of models in the various aspects of prediction, planning, and uncertainty analysis of smart cities and urban development. This paper presents the state of the art of DL and ML methods used in this realm. Through a novel taxonomy, the advances in model development and new application domains in urban sustainability and smart cities are presented. Findings reveal that five DL and ML methods have been most applied to address the different aspects of smart cities. These are artificial neural networks; support vector machines; decision trees; ensembles, Bayesians, hybrids, and neuro-fuzzy; and deep learning. It is also disclosed that energy, health, and urban transport are the main domains of smart cities that DL and ML methods contributed in to address their problems.

scholarly journals Graphical User Interface for Shuffling of Sections based on Student Profile

2021 ◽  
Vol 9 (VI) ◽  
pp. 2752-2757
Author(s):  
Kotawar Ashwitha
Keyword(s):  
Time Series ◽  
Data Structures ◽  
Graphical User Interface ◽  
Software Library ◽  
Male And Female ◽  
Data Manipulation ◽  
Student Profile ◽  
Python Language ◽  
Python Programming ◽  
Excel File

This project GUI for shuffling of sections is done to automate the hectic work of shuffling students into sections has been programed in python using open source module using pandas and tkinter the overall result achieved to this program is that students got shuffled into sections with same ratio of male and female in all section, and average of ranks of students of all sections are similar as to maintain equality and integrity. This program gives a GUI for the administrator to access the file with data of students stored to manipulate that data. In this project we will implement using python programming language .in python, we will use module pandas, TKinter. Pandas to manipulate data of students from an excel file through python program, TKinter is used to add GUI to the program to select the file to be manipulated pandas is a software library written for the python language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series In particular, it offers data structures and operations for manipulating numerical tables and time series. TKINTER is a software library for creating library for creating GUI using python language.

scholarly journals Artificial intelligence maturity model: a systematic literature review

2021 ◽  
Vol 7 ◽  
pp. e661
Author(s):  
Raghad Baker Sadiq ◽  
Nurhizam Safie ◽  
Abdul Hadi Abd Rahman ◽  
Shidrokh Goudarzi
Keyword(s):  
Artificial Intelligence ◽  
Systematic Approach ◽  
State Of The Art ◽  
Model Development ◽  
Significant Proportion ◽  
Maturity Model ◽  
Methodological Issues ◽  
Maturity Models ◽  
Essential Contribution ◽  
Review State

Organizations in various industries have widely developed the artificial intelligence (AI) maturity model as a systematic approach. This study aims to review state-of-the-art studies related to AI maturity models systematically. It allows a deeper understanding of the methodological issues relevant to maturity models, especially in terms of the objectives, methods employed to develop and validate the models, and the scope and characteristics of maturity model development. Our analysis reveals that most works concentrate on developing maturity models with or without their empirical validation. It shows that the most significant proportion of models were designed for specific domains and purposes. Maturity model development typically uses a bottom-up design approach, and most of the models have a descriptive characteristic. Besides that, maturity grid and continuous representation with five levels are currently trending in maturity model development. Six out of 13 studies (46%) on AI maturity pertain to assess the technology aspect, even in specific domains. It confirms that organizations still require an improvement in their AI capability and in strengthening AI maturity. This review provides an essential contribution to the evolution of organizations using AI to explain the concepts, approaches, and elements of maturity models.

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