Internet of Things for Smart Agriculture monitoring and prediction system

Internet of Things for Smart Agriculture monitoring and prediction system

Differences in the Sub-seasonal Predictability of Extreme Stratospheric Events

Extreme stratospheric events such as sudden stratospheric warming and strong vortex events associated with an anomalously weak or strong polar vortex can have downward impacts on surface weather that can last for several weeks to months. Hence, successful predictions of these stratospheric events would be beneficial for extended range weather prediction. However, the predictability limit of extreme stratospheric events is most often limited to around 2 weeks or less. The predictability also strongly differs between events, and between event types. The reasons for the observed differences in the predictability, however, are not resolved. To better understand the predictability differences between events, we expand the definitions of extreme stratospheric events to wind deceleration and acceleration events, and conduct a systematic comparison of predictability between event types in the European Centre for Medium-Range Weather Forecasts (ECMWF) prediction system for the sub-seasonal predictions. We find that wind deceleration and acceleration events follow the same predictability behaviour, that is, events of stronger magnitude are less predictable in a close to linear relationship, to the same extent for both types of events. There are however deviations from this linear behaviour for very extreme events. The difficulties of the prediction system in predicting extremely strong anomalies can be traced to a poor predictability of extreme wave activity pulses in the lower stratosphere, which impacts the prediction of deceleration events, and interestingly, also acceleration events. Improvements in the understanding of the wave amplification that is associated with extremely strong wave activity pulses and accurately representing these processes in the model is expected to enhance the predictability of stratospheric extreme events and, by extension, their impacts on surface weather and climate.

A Whole Process Cost Prediction System for Construction Projects Based on Improved Support Vector Machines

The current project cost system requires high data scale, small amount of data and large prediction deviation. In order to improve the prediction accuracy of the whole process cost of construction project, this paper designs a whole process project cost prediction system based on improved support vector machine. In the hardware part of the system, the control core adopts arm controller S3C6410 and introduces 4G communication module to analyze the actual engineering data with the support of hardware. In the software part, the whole process cost prediction index system of the construction project is established, the index is reduced by the principal component method, and the support vector machine is improved by particle swarm optimization algorithm to realize the whole process cost prediction of the project. The system function test results show that the average prediction deviation of the designed system is 4.11%, the average prediction deviation of the cost prediction system is 3.05%, and the average prediction deviation of the system is 1.57%.