The document discusses different types of weather stations and their components. It describes the key instruments used - pyranometers to measure solar radiation, anemometers to measure wind speed and direction, thermometers for temperature, hygrometers for humidity, and barometers for atmospheric pressure. It also outlines different types of weather stations including synoptic stations for climate data, agricultural stations, and coastal stations. The document compares manual measurement results to automated weather station data for pyranometers and anemometers.
A series of modules on project cycle, planning and the logical framework, aimed at team leaders of international NGOs in developing countries.
New improved version of Writing Project Proposals in February 2014.
A series of modules on project cycle, planning and the logical framework, aimed at team leaders of international NGOs in developing countries.
New improved version of Writing Project Proposals in February 2014.
Applying Machine Learning Techniques to Revenue ManagementAhmed BEN JEMIA
Abstract:
For an effective and economical strategy, restaurant owners must accurately estimate
the number of their future visitors. In this report, we propose an approach for predicting
the number of future visitors for restaurants using statistical methods such as ARIMA,
SARIMAX and BSTS and machine learning regression algorithms. Our model has as input internal restaurant data, historical visits, historical reservations and external data such as vacation days and temperature histories. From these large data sets and time information, we constructed four groups of characteristics accordingly. Using these characteristics, our approach generates forecasts by performing regression using different algorithms such as Decision Tree, Random Forests, K-Nearest-Neighbour, Stochastic Gradient Descent and Gradient Boosted Decision Trees. The results of the evaluation show the effectiveness of our approach, as well as the useful indications for a future research project.
Keywords:
Artificial Intelligence, Machine Learning, Business Intelligence, Forecasting, Restaurant,
Yield Management, Statistical Analysis.
Applying Machine Learning Techniques to Revenue ManagementAhmed BEN JEMIA
Abstract:
For an effective and economical strategy, restaurant owners must accurately estimate
the number of their future visitors. In this report, we propose an approach for predicting
the number of future visitors for restaurants using statistical methods such as ARIMA,
SARIMAX and BSTS and machine learning regression algorithms. Our model has as input internal restaurant data, historical visits, historical reservations and external data such as vacation days and temperature histories. From these large data sets and time information, we constructed four groups of characteristics accordingly. Using these characteristics, our approach generates forecasts by performing regression using different algorithms such as Decision Tree, Random Forests, K-Nearest-Neighbour, Stochastic Gradient Descent and Gradient Boosted Decision Trees. The results of the evaluation show the effectiveness of our approach, as well as the useful indications for a future research project.
Keywords:
Artificial Intelligence, Machine Learning, Business Intelligence, Forecasting, Restaurant,
Yield Management, Statistical Analysis.
A proposal to review how the weather station predict the weather
1. Table of contents
Introduction………………………………………………………………………….
I)the different parameters of weather station………………………
a)definitionof captors……………………………………………………………
b)function of the instruments……………………………………………….
II)prediction of weather station……………………………………………..
a)the different meterological station……………………………………..
b)how the predictionis going on………………………….................
III)comparison of manualresults with station results of
materiels…………………………………………………………………………….
a)for pyranometer………………………………………………………………
b)for anemometer………………………………………………………………….
IV)the post of the pyranometerand anemometer………………
Conclusion………………………………………………………………………….
Recommendation………………………………………………………………..
Reference………………………………………………………………………….
AppendixA………………………………………………………………………..
AppendixB…………………………………………………………………………
2. I)thedifferent parameters of weather station
a)definition
pyranometer measurethe solar radiation energie(W/m^2),thereis different
class standard of it(first class(SR12,SR11pyranometer) , second
class(LI19,LP02,SR03,SR11pyranometer)secondary standart(SR20-Tr))(figure
2),theremodels vary fromone to another by the accuracy of the measurement
or the easy need of calibration
anemometer(or wind vane)is a device that measurethe speed and pressureof
the wind .historically the firstanemometer was invented by leonardo de vinci
then a another cup anemometer was developed by Dr john thomas romney in
1846(figure1)
thermometer measurethe temperature (°C)
hygrometer use to measure humidy ,essentially it has an important part for
weather forcasting and helpful for agriculture (gardener)according to Chris
Woodford. Last updated: April 18, 2014.there are two types of hygrometer(
rain gauge measurethe precipitation
barometer used for measuring the atmospheriquepression
thermohygro sensor
b)functionality of captors
II)different meterological station
Different meterologie with different variable
synoptic station=climatological station=air temperature,soil
temperature,relative humidity,precipitation,atmospheric pressureatstation
level ,wind speed and wind direction,solar radiation,sun duration
agriculture meterology =solar radiation,rainand precipitation,soil
temperature,soil moisture,wind speed and direction ,relative humidity,air
temperature