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نتیجه جستجو - Weather stations

تعداد مقالات یافته شده: 4
ردیف عنوان نوع
1 The importance of accounting-integrated information systems for realising productivity and sustainability in the agricultural sector
اهمیت سیستم های اطلاعاتی حسابداری یکپارچه برای تحقق بهره وری و پایداری در بخش کشاورزی-2021
Agricultural information systems are an integral part of modern farming and are helping to make a significant contribution to improved farm productivity and profitability. To date, however, there has been a failure to integrate accounting information systems with onfarm data, despite today’s farmers facing unprecedented and interconnected economic and resource pressures. This study explores this problem in more detail, defines the objectives of the solution and develops a model of integrated accounting and agricultural information systems, drawing on a ‘fads and fashions’ framework and advancing our understanding of bundled innovations. Using data from a participatory case study in Australian potato farming, the study integrates accounting data with soil moisture and climate data to track, alert and inform irrigation decisions. Development of preliminary digital software based on the model demonstrates how cost-informed tracking, alerts and forecasting can be supported by bundling accounting information systems and sensing technology. In doing so, the model extends the fads and fashions framework for agricultural information systems and demonstrates how accounting information can be the key for improved water productivity, profitability and agricultural sustainability.
keywords: تصمیم گیری کشاورزی | سیستم های حسابداری یکپارچه | نوآوری های همراه | سنسور | اطلاعات دیجیتال | ایستگاه های آب و هوا | تصویربرداری ماهواره ای | Agricultural decision-making | Integrated accounting systems | Bundled innovations | Sensors | Digital information | Weather stations | Satellite imagery
مقاله انگلیسی
2 Estimating crime scene temperatures from nearby meteorological station data
تخمین دمای صحنه جرم از داده های ایستگاه هواشناسی مجاور-2020
The importance of temperature data in minimum postmortem interval (minPMI) estimations in criminal investigations is well known. To maximise the accuracy of minPMI estimations, it is imperative to investigate the different components involved in temperature modelling, such as the duration of temperature data logger placement at the crime scene and choice of nearest weather station to compare the crime scene data to. Currently, there is no standardised practice on how long to leave the temperature data logger at the crime scene and the effects of varying logger duration are little known. The choice of the nearest weather station is usually made based on availability and accessibility of data from weather stations in the crime scene vicinity. However, there are no guidelines on what to look for to maximise the comparability of weather station and crime scene temperatures. Linear regression analysis of scene data with data from weather stations with varying time intervals, distances, altitudes and microclimates showed the greatest goodness of fit (R2), i.e. the highest compatibility between datasets, after 4–10 days. However, there was no significant improvement in estimation of crime scene temperatures beyond a 5-day regression period. The smaller the distance between scene and weather station and the higher the similarity in environment, such as altitude and geographical area, resulted in greater compatibility between datasets. Overall, the study demonstrated the complexity of choosing the most comparable weather station to the crime scene, especially because of a high variation in seasonal temperature and numerous influencing factors such as geographical location, urban ‘heat island effect’ and microclimates. Despite subtle differences, for both urban and rural areas an optimal data fit was generally reached after about five consecutive days within a radius of up to 30 km of the ‘crime scene’. With increasing distance and differing altitudes, a lower overall data fit was observed, and a diminishing increase in R2 values was reached after 4–10 consecutive days. These results demonstrate the need for caution regarding distances and climate differences when using weather station data for retrospective regression analyses for estimating temperatures at crime scenes. However, the estimates of scene temperatures from regression analysis were better than simply using the temperatures from the nearest weather station. This study provides recommendations for data logging duration of operation, and a baseline for further research into producing standard guidelines for increasing the accuracy of minPMI estimations and, ultimately, greater robustness of forensic entomology evidence in court.
Keywords: Temperature modelling | Minimum post-mortem interval | Micro-climate | Forensic ecology | Temperature datalogger
مقاله انگلیسی
3 Prediction of the apple scab using machine learning and simple weather stations
پیش بینی apple scabبا استفاده از یادگیری ماشین و ایستگاه های ساده آب و هوا-2019
Apple scab is an economically important pest for apple production. It is controlled by applying fungicides when conditions are ripe for the development of its spores. This occurs when leaves are wet for a long enough time at a given temperature. However, leaf wetness is not a sufficiently well-defined agro-meteorological variable. Moreover, the readings of leaf wetness sensors depend to a large extent on their location within the tree canopy. Here we show that virtual wetness sensors, which are based on the easily obtained meteorological parameters such as temperature, relative humidity and wind speed, can be used in place of physical sensors. To this end, we have first collected data for two growing seasons from two types of wetness sensors planted in four locations in the tree canopy. Then, for each sensor we have built a machine-learning model of leaf wetness using the aforementioned meteorological variables. These models were further used as virtual sensors. Finally, Mills models of apple scab infection were built using both real and virtual sensors and their results were compared. The comparison of apple scab models based on real sensors shows significant variability. In particular, the results of a model depend on the location of the sensor within the canopy. The models based on data obtained from virtual sensors, are similar to the models based on physical sensors. Both types of models generate results within the same range of variability. The outcome of the study shows that the control of apple scab can be based on machine learning models based on standard meteorological variables. These variables can be readily obtained using inexpensive meteorological stations equipped with basic sensors. These results open the way to a widespread application of precise control of apple scab and consequently significant reduction of the use of pesticides in apple production with benefits for environment, human health and economics of production.
Keywords: Apple scab | Machine learning | Random fores
مقاله انگلیسی
4 Deep shared representation learning for weather elements forecasting
یادگیری نمایندگی اشتراکی عمیق برای پیش بینی عناصر آب و هوا-2019
The accuracy and reliability of weather forecasting are of importance for many economic, business and management activities. This paper introduces novel data-driven predictive models based on deep convolutional neural networks (CNN) architecture for temperature and wind speed prediction in weather data. In particular, the proposed deep learning framework employs different upgrading versions of the convolutional neural networks i.e. 1d-, 2d- and 3d-CNN. The introduced models exploit the spatio-temporal multivariate weather data for learning shared representations using historical data and forecasting weather elements for a number of user defined weather stations simultaneously in an end-to-end fashion. The embedded feature learning component of the models as well as coupling the learned features of different input layers have shown to have a significant impact on the prediction task. The proposed models show promising results compared to the classical neural networks architecture used for modeling nonlinear systems. Two experimental setups have been considered based on a dataset collected from the Weather Underground website at six stations located in Netherlands and Belgium as well as a larger dataset with higher temporal resolution from the National Climatic Data Center (NCDC) at five stations located in Denmark. First, we focus on simultaneously predicting the temperature of two main stations of Amsterdam and Brussels for 1–10 days ahead. The second experiment concerns wind speed prediction at three weather stations located in Denmark for 6 and 12 h ahead. The obtained numerical results show that learning new shared representations of the weather data by means of convolutional operations improves the prediction performance.
Keywords: Deep learning | Weather forecasting | Convolutional neural networks | Dimensionality reduction | Representation learning
مقاله انگلیسی
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