scholarly journals Resolution of Probabilistic Weather Forecasts with Application in Disease Management

2017 ◽  
Vol 107 (2) ◽  
pp. 158-162 ◽  
Author(s):  
G. Hughes ◽  
N. McRoberts ◽  
F. J. Burnett
Keyword(s):  
Disease Management ◽  
Mutual Information ◽  
Disease Risk ◽  
Information Gain ◽  
Weather Forecast ◽  
Weather Data ◽  
Specific Information ◽  
Information Theoretic ◽  
Weather Forecasts ◽  
Measuring Change

Predictive systems in disease management often incorporate weather data among the disease risk factors, and sometimes this comes in the form of forecast weather data rather than observed weather data. In such cases, it is useful to have an evaluation of the operational weather forecast, in addition to the evaluation of the disease forecasts provided by the predictive system. Typically, weather forecasts and disease forecasts are evaluated using different methodologies. However, the information theoretic quantity expected mutual information provides a basis for evaluating both kinds of forecast. Expected mutual information is an appropriate metric for the average performance of a predictive system over a set of forecasts. Both relative entropy (a divergence, measuring information gain) and specific information (an entropy difference, measuring change in uncertainty) provide a basis for the assessment of individual forecasts.

scholarly journals Information Graphs for Binary Predictors

2015 ◽  
Vol 105 (1) ◽  
pp. 9-17 ◽  
Author(s):  
G. Hughes ◽  
N. McRoberts ◽  
F. J. Burnett
Keyword(s):  
Mutual Information ◽  
Crop Protection ◽  
Diagnostic Information ◽  
Specific Information ◽  
Data Set ◽  
Information Theoretic ◽  
Information Updating ◽  
Entropy Information ◽  
Wide Range ◽  
Crop Disease

Binary predictors are used in a wide range of crop protection decision-making applications. Such predictors provide a simple analytical apparatus for the formulation of evidence related to risk factors, for use in the process of Bayesian updating of probabilities of crop disease. For diagrammatic interpretation of diagnostic probabilities, the receiver operating characteristic is available. Here, we view binary predictors from the perspective of diagnostic information. After a brief introduction to the basic information theoretic concepts of entropy and expected mutual information, we use an example data set to provide diagrammatic interpretations of expected mutual information, relative entropy, information inaccuracy, information updating, and specific information. Our information graphs also illustrate correspondences between diagnostic information and diagnostic probabilities.

Some Interim Results of the Study

1982 ◽  
Vol 35 (3) ◽  
pp. 502-516
Author(s):  
R. Monk
Keyword(s):  
North Atlantic ◽  
Weather Forecast ◽  
Air Traffic ◽  
Weather Data ◽  
Weather Forecasts ◽  
Additional Information ◽  
European Cities ◽  
The North ◽  
The North Atlantic ◽  
To Receive

We are still in the process of collecting and developing ways of studying and analysing air traffic routes across the North Atlantic. The results presented in this paper must therefore be recognized as provisional. The data comprise some twelve examples of North Atlantic weather forecasts issued from Bracknell; they are sent to us regularly for the 2nd and 15th day of each month. We have also made arrangements to receive notification from the Heathrow Meteorological Office of any days in which there were significant changes in the weather forecast, so that we can request the additional information from Bracknell. Each set of weather data contains the ‘analysis weather’, that is the best estimate of the actual weather at 1200 GMT, and therefore applicable to the time when aircraft are making westerly departures across the North Atlantic from European cities, and also the weather forecasts issued for 12 and 24 hours before this time.

scholarly journals Next-Day Prediction of Hourly Solar Irradiance Using Local Weather Forecasts and LSTM Trained with Non-Local Data

Energies ◽  
2020 ◽  
Vol 13 (20) ◽  
pp. 5258 ◽  
Author(s):  
Byung-ki Jeon ◽  
Eui-Jong Kim
Keyword(s):  
Solar Irradiance ◽  
Short Term Memory ◽  
Predictive Performance ◽  
Weather Forecast ◽  
Weather Data ◽  
Local Data ◽  
Weather Forecasts ◽  
Proposed Model ◽  
International Data ◽  
Limited Input Data

Solar irradiance prediction is significant for maximizing energy-saving effects in the predictive control of buildings. Several models for solar irradiance prediction have been developed; however, they require the collection of weather data over a long period in the predicted target region or evaluation of various weather data in real time. In this study, a long short-term memory algorithm–based model is proposed using limited input data and data from other regions. The proposed model can predict solar irradiance using next-day weather forecasts by the Korea Meteorological Administration and daily solar irradiance, and it is possible to build a model with one-time learning using national and international data. The model developed in this study showed excellent predictive performance with a coefficient of variation of the root mean square error of 12% per year even if the learning and forecast regions were different, assuming that the weather forecast was correct.

scholarly journals Weather forecasts - the islands of calmness in the rough TV sea

2021 ◽  
Author(s):  
Stanislava Tsalova
Keyword(s):  
Positive Emotions ◽  
Scientific Information ◽  
Weather Forecast ◽  
Weather Data ◽  
Stress Release ◽  
Weather Forecasts ◽  
The Past ◽  
The People ◽  
Tv Ratings ◽  
Tv Shows

<p>People who are not involved in doing Weather forecast presentations, think that it is something easy to prepare. But it needs experience to present the weather data and forecast, which is scientific information in a way understandable for the TV viewers. Weather forecasts have always been islands of positive emotions in TV programs. </p><p>The past year was very challeging for all TV stations around the world. In all the news and TV shows the main topic was Coronavirus disease. Now, more than ever TV weather forecast's role became to provide some positive emotions to the people who are so much got tired of the bad and scary news on their TVs. The fact is that during the pandemic the TV ratings are higher made our responsibility even bigger.<br><br>While preparing my weather presentations, even in cases of severe weather my top priority was not to scare people, who were scared enough. When showing weather videos, I avoided such with disasters. Instead I showed more wildlife and educational weather videos. Unlike before, in 2020/2021 years I definitely avoided climate change topic. <br><br>While chatting about weather on air with the news and morning shows anchors, the chat had sometimes escalated to bursting into laughter. Unlike before, our viewers approved that highly, because everybody is under pressure now and such stress release things were more than welcome. The weather forecast now became more than ever an island of calmness and hope for a better tomorrow in the rough TV sea.<br><br>I want to share my experience and to exchange opinion on that topic with collegues from other countries and TV stations.<br><br></p>

scholarly journals Methodology for the Quantification of the Impact of Weather Forecasts in Predictive Simulation Models

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1309 ◽  
Author(s):  
Eva Lucas Segarra ◽  
Hu Du ◽  
Germán Ramos Ruiz ◽  
Carlos Fernández Bandera
Keyword(s):  
Energy Demand ◽  
Forecast Error ◽  
Weather Forecast ◽  
Building Energy ◽  
Weather Data ◽  
Weather Forecasts ◽  
Hourly Data ◽  
The Future ◽  
The Impact ◽  
Near Future

The use of Building Energy Models (BEM) has become widespread to reduce building energy consumption. Projection of the model in the future to know how different consumption strategies can be evaluated is one of the main applications of BEM. Many energy management optimization strategies can be used and, among others, model predictive control (MPC) has become very popular nowadays. When using models for predicting the future, we have to assume certain errors that come from uncertainty parameters. One of these uncertainties is the weather forecast needed to predict the building behavior in the near future. This paper proposes a methodology for quantifying the impact of the error generated by the weather forecast in the building’s indoor climate conditions and energy demand. The objective is to estimate the error introduced by the weather forecast in the load forecasting to have more precise predicted data. The methodology employed site-specific, near-future forecast weather data obtained through online open access Application Programming Interfaces (APIs). The weather forecast providers supply forecasts up to 10 days ahead of key weather parameters such as outdoor temperature, relative humidity, wind speed and wind direction. This approach uses calibrated EnergyPlus models to foresee the errors in the indoor thermal behavior and energy demand caused by the increasing day-ahead weather forecasts. A case study investigated the impact of using up to 7-day weather forecasts on mean indoor temperature and energy demand predictions in a building located in Pamplona, Spain. The main novel concepts in this paper are: first, the characterization of the weather forecast error for a specific weather data provider and location and its effect in the building’s load prediction. The error is calculated based on recorded hourly data so the results are provided on an hourly basis, avoiding the cancel out effect when a wider period of time is analyzed. The second is the classification and analysis of the data hour-by-hour to provide an estimate error for each hour of the day generating a map of hourly errors. This application becomes necessary when the building takes part in the day-ahead programs such as demand response or flexibility strategies, where the predicted hourly load must be provided to the grid in advance. The methodology developed in this paper can be extrapolated to any weather forecast provider, location or building.

Quantifying Stimulus Discriminability: A Comparison of Information Theory and Ideal Observer Analysis

2005 ◽  
Vol 17 (4) ◽  
pp. 741-778 ◽  
Author(s):  
Eric E. Thomson ◽  
William B. Kristan
Keyword(s):  
Information Theory ◽  
Mutual Information ◽  
Conditional Entropy ◽  
Ideal Observer ◽  
Upper And Lower Bounds ◽  
Specific Information ◽  
Information Theoretic ◽  
Information Measures ◽  
Improved Performance ◽  
Ideal Observer Analysis

Performance in sensory discrimination tasks is commonly quantified using either information theory or ideal observer analysis. These two quantitative frameworks are often assumed to be equivalent. For example, higher mutual information is said to correspond to improved performance of an ideal observer in a stimulus estimation task. To the contrary, drawing on and extending previous results, we show that five information-theoretic quantities (entropy, response-conditional entropy, specific information, equivocation, and mutual information) violate this assumption. More positively, we show how these information measures can be used to calculate upper and lower bounds on ideal observer performance, and vice versa. The results show that the mathematical resources of ideal observer analysis are preferable to information theory for evaluating performance in a stimulus discrimination task. We also discuss the applicability of information theory to questions that ideal observer analysis cannot address.

scholarly journals Strategy for Rice Disease Management in Bangladesh

2021 ◽  
Vol 25 (1) ◽  
pp. 23-36
Author(s):  
MT Khatun ◽  
B Nessa ◽  
MU Salam ◽  
MS Kabir
Keyword(s):  
Disease Management ◽  
Yield Loss ◽  
Disease Risk ◽  
Economic Loss ◽  
Disease Outbreak ◽  
Rice Production ◽  
Weather Data ◽  
Management Options ◽  
Rice Disease ◽  
Disease Specific

Disease is one of the most limiting biotic factors that affects rice production worldwide. In Bangladesh, there are 10 rice diseases considered as major, which cause economic loss in farmers’ fields. Therefore, the aim of this article is to explore all the feasible avenues of technology deployment on rice disease management to restrict the disease infection at minimum level and thus minimize economic loss. The article is generated using data and/or infromation from published and unpublished works and incorporating authors’ experience. It is evident that periodically (odd year) a disease outbreak or epidemic occurred in Bangladesh such as blast. Under epidemic situation, research findings estimated a yield loss of up to 98% at the highest disease severity level of infection of blast. On the other hand, field survey indicated the highest of 65.4% yield loss from severly infected field with the disease. To overcome the epidemics in odd years and to keep the loss under economic threshold level, it is necessary to undertake preventive measures such as planting of resistant or tolerant varieties, use of disease-free seeds from healthy plants, use of balanced fertilizer where applicable, and following feasible crop rotations. Currently, it is urgent need for developing strong and precise weather-based disease-risk forecasting system at least one week’s lead time based on real-time weather data. Subsequent quick management options such as disease-specific fungicidal treatment should be communicated to all stakeholders using fast-delivery media such as TV channels and SMS could be efficient and effective ways to address the disease outbreak under epidemic situation. To address annualized yield loss, it is suggested to execute interventions like effective training to the root level (both for farmers and extension personnel) and conducting demonstration in farmers fields, regular field monitoring, digitalization in disease management sector, revive indigenous technologies as appropriate, and improving rice production system. To continously improve rice disease management sector, this paper has proposed an innovative action for three decades through to 2050 under the banner ‘Location, Variety and Disease Specific Smart Management’ on research, development and extension. Bangladesh Rice J. 25 (1) : 23-36, 2021

scholarly journals An Information-Theoretic Framework for Optimal Design: Analysis of Protocols for Estimating Soft Tissue Parameters in Biaxial Experiments

Axioms ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 79
Author(s):  
Ankush Aggarwal ◽  
Damiano Lombardi ◽  
Sanjay Pant
Keyword(s):  
Optimal Design ◽  
Mutual Information ◽  
Soft Tissues ◽  
Information Gain ◽  
Model Parameters ◽  
Conditional Mutual Information ◽  
Information Theoretic ◽  
Information Theoretic Measures ◽  
New Framework ◽  
Biaxial Experiments

A new framework for optimal design based on the information-theoretic measures of mutual information, conditional mutual information and their combination is proposed. The framework is tested on the analysis of protocols—a combination of angles along which strain measurements can be acquired—in a biaxial experiment of soft tissues for the estimation of hyperelastic constitutive model parameters. The proposed framework considers the information gain about the parameters from the experiment as the key criterion to be maximised, which can be directly used for optimal design. Information gain is computed through k-nearest neighbour algorithms applied to the joint samples of the parameters and measurements produced by the forward and observation models. For biaxial experiments, the results show that low angles have a relatively low information content compared to high angles. The results also show that a smaller number of angles with suitably chosen combinations can result in higher information gains when compared to a larger number of angles which are poorly combined. Finally, it is shown that the proposed framework is consistent with classical approaches, particularly D-optimal design.

Alleviating the independence assumptions of averaged one-dependence estimators by model weighting

2021 ◽  
Vol 25 (6) ◽  
pp. 1431-1451
Author(s):  
Li-Min Wang ◽  
Peng Chen ◽  
Musa Mammadov ◽  
Yang Liu ◽  
Si-Yuan Wu
Keyword(s):  
Mutual Information ◽  
Information Gain ◽  
Directed Acyclic Graphs ◽  
Independence Assumption ◽  
Information Theoretic ◽  
Probability Estimates ◽  
Acyclic Graphs ◽  
Benchmark Datasets ◽  
Moderate Cost ◽  
Model Weighting

Of numerous proposals to refine naive Bayes by weakening its attribute independence assumption, averaged one-dependence estimators (AODE) has been shown to be able to achieve significantly higher classification accuracy at a moderate cost in classification efficiency. However, all one-dependence estimators (ODEs) in AODE have the same weights and are treated equally. To address this issue, model weighting, which assigns discriminate weights to ODEs and then linearly combine their probability estimates, has been proved to be an efficient and effective approach. Most information-theoretic weighting metrics, including mutual information, Kullback-Leibler measure and the information gain, place more emphasis on the correlation between root attribute (value) and class variable. We argue that the topology of each ODE can be divided into a set of local directed acyclic graphs (DAGs) based on the independence assumption, and multivariate mutual information is introduced to measure the extent to which the DAGs fit data. Based on this premise, in this study we propose a novel weighted AODE algorithm, called AWODE, that adaptively selects weights to alleviate the independence assumption and make the learned probability distribution fit the instance. The proposed approach is validated on 40 benchmark datasets from UCI machine learning repository. The experimental results reveal that, AWODE achieves bias-variance trade-off and is a competitive alternative to single-model Bayesian learners (such as TAN and KDB) and other weighted AODEs (such as WAODE).

WEATHER FORECAST PERCEPTIONS OF SAUDI ARABIAN CITIZENS: INITIAL STEPS TOWARDS BUILDING EXTREME WEATHER FORECASTING COMMUNICATION TECHNOLOGY

2021 ◽  
Author(s):  
Abdulrahman Khamaj ◽  
Amin G. Alhashim ◽  
Vincent T. Ybarra ◽  
Azham Hussain
Keyword(s):  
Weather Forecasting ◽  
Middle Eastern ◽  
Protective Action ◽  
Weather Forecast ◽  
Weather Data ◽  
Cross Sectional ◽  
Daily Lives ◽  
Weather Forecasts ◽  
The Public ◽  
Public Perspective

AbstractCommunicating weather forecasts from the public perspective is essential for meeting people’s needs and enhancing their overall experiences. Due to the lack of cited work on the public’s behavior and perception of weather data and delivery sources in Middle Eastern countries such as Saudi Arabia (KSA), this study employs a cross-sectional questionnaire to fill the gap and apply the Protective Action Decision Model to non-Western individuals. The questionnaire examined respondents’ opinions about 1) the importance of weather forecast accessibility, 2) crucial weather features, and 3) available features on existing smartphone weather applications (apps) in KSA. The results showed that nearly all participants reported that their decisions of daily lives and activities were highly dependent on weather forecasts. Most participants thought weather forecast features are necessary. Though the most commonly used source for weather forecasts in KSA was smartphone apps, many participants responded that these apps were lacking specific weather functionalities (e.g., giving weather alerts to their exact location). Regression analyses found that KSA individuals who do not believe that weather forecasts are important are predicted by 1) not wanting any new features added to weather applications and 2) that weather forecasts do not impact lives nor property. This study’s findings can guide governmental and private weather agencies in KSA and other Middle Eastern or developing countries to better understand how to meet and communicate people’s weather needs.

Sign in / Sign up

Export Citation Format

Share Document