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it includes the feasibilty,objectives,methodology all other may topics for the weather forecasting
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Abstract......................................................................................................................................ii
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Figure 1: Flow chart of System…………………………………………………
Figure 3: Scheduling in Gantt Char……………………………………………
Weather forecasting is the application of science and technology to predict the conditions of the atmosphere for a given location and time. Prediction of the weather requires substantial amount of knowledge about the working of the environment. There are various techniques to predict weather like Persistence Forecasting, Synoptic Forecasting, Statistical Forecasting and Computer forecasting. a) Persistence Forecasting The first of these methods is the Persistence Method; the simplest way of producing a forecast. The persistence method assumes that the conditions at the time of the forecast will not change. b) Synoptic Forecasting This method uses the basic rules for forecasting. Meteorologists take their observations, and apply those rules to make a short-term forecast. c) Statistical Forecasting Meteorologists ask themselves, what does it usually do this time of the year? Records of average temperatures, average rainfall and average snowfall over the years give forecasters an idea of what the weather is "supposed to be like" at a certain time of the year. d) Computer forecasting Forecasters take their observations and plug the numbers into complicated equations. Several ultra- high-speed computers run these various equations to make computer "models" which give a forecast for the next several days. Often, different equations produce different results, so meteorologists must always use the other forecasting methods along with this one. There are various other techniques which makes use of the combination of the aforementioned techniques. Meteorologist exploit these techniques to predict weather. With sufficient research and realizing the need of the extremely high computing power for the numerical weather model and ensemble forecasting, high uncertainty of other techniques we concluded to use the statistical technique to predict weather. This approach involves collection of past years data of the few crucial parameters and using various approaches and algorithms to predict weather with some error. Also we
will be employing sensors to measure the parameters to show current or recent weather condition and also serve as a base for conformity of our predicted result.
Weather forecasting is mainly concerned with the prediction of weather condition in the given future time. Weather forecasts provide critical information about future weather. There are various approaches available in weather forecasting, from relatively simple observation of the sky to highly complex computerized mathematical models. The prediction of weather condition is essential for various applications. Some of them are climate monitoring, drought detection, severe weather prediction, agriculture and production, planning in energy industry, aviation industry, communication, pollution dispersal, and so forth. In military operations, there is a considerable historical record of instances when weather conditions have altered the course of battles. Accurate prediction of weather conditions is a difficult task due to the dynamic nature of atmosphere. The weather condition at any instance may be represented by some variables. Out of those variables, one found that the most significant are being selected to be involved in the process of prediction. The selection of variables is dependent on the location for which the prediction is to be made. The variables and their range always vary from place to place. The weather condition of any day has some relationship with the weather condition existed in the same tenure of precious year and previous week.
A statistical model is designed that could predict the rainfall and temperature with the help of past data by making use of time-delayed feed forward neural network. Artificial neural network was combined with the genetic algorithm to get the more optimized prediction. The rainfall self-regression model was proposed by Lu Feng and Xu Xiao Guang which makes use of the form of self-related sequence number according to observed number. The self- related coefficients were computed by making use of Fuzzy Logic. A combined approach of neural network with Fuzzy Logic is being proposed for the weather prediction system. The work has applied principle component analysis technique to the fuzzy data by making use of Auto associative neural networks.
Weather forecasting is a difficult task to perform. To make an accurate forecast, we must first understand what processes are occurring in the atmosphere to produce the current weather at the location for which then we are forecasting. This is done by measuring certain elements (making observations) of the atmosphere; i.e., temperature, pressure, wind direction and speed, humidity, cloud cover, precipitation, etc. The more complete measurement coverage across the earth's surface and vertically through the atmosphere of the elements which affect the weather we experience, the better picture we have of the processes producing the weather we are currently experiencing. By observing the changes which take place to these elements over time and comparing the changing patterns with historical patterns, an understanding of expected weather conditions can be made.
Problems
The major problem faced while performing is the unpredictable weather conditions as we majorly depend on the historical data for the prediction. And to increase the accuracy of the forecasting the many other weather parameters should be included which makes the model more complex.AS the weather pattern goes on changing the prediction done on basis of the historical data may not be accurate. At the same time different forecasting model may predict different weather conditions. The lack of accurate historical data is also the major problem and weather stations are also limited.
And all the parameters cannot obtained from same weather stations. So for different parameters different stations should visited.
With the help of sensors we can collect the different parameters like temperature, humidity and rain fall we can able to display the real time data by processing the sensors data and also we can predict the upcoming values of the parameter with the help of past years values and real time values. The most efficient values are selected and predicted for upcoming.
Weather forecasting is most important in various fields like agriculture sector, vehicle traffic control, to predict the imminent natural disasters in order to save the life of all living and non-living things. In the same way it might be useful for travelers to travel in suitable and comfortable environment. Weather forecasting has vital role in water navigation also, by knowing the upcoming condition of weather submarine, ships, boats etc. can be guided easily. With the help of weather forecast and analysis of previous data, we can increase the productivity of crops also by predicting the proper cultivating times with help of coming climatic conditions. On the opposite side of the climatic information, the ultra-dry deserts provide an ideal location for mining minerals and metals that are used for many production industries such as electronics. Similarly through the use of latitude, we can determine the likelihood of snow and hail reaching the surface. We can also be able to identify the thermal energy from the sun that is accessible to a region. By taking these things under consideration, it helps in security and growth of whole nation also as a nation is mean to the citizens and the resources available.
Weather forecasting cannot and never be accurate or precise due to the reasons like data analysis uncertainties, model limitations and chaotic nature of the environment. Our system takes very few parameters with the help of less sensors to predict weather within a certain small area of a season. We are undertaking the parameters like temperature, humidity and precipitation. So our system can provide the information about maximum and minimum temperature, humidity and precipitation. The system becomes complex if we incorporate other parameters. In the same way if we take long historical data it becomes difficult in analyzing the data and operating on them prediction. Although the system can become more accurate if we take more data but it might require skilled manpower for the interpretation of data and analysis which increases the cost of the
[1]The art of weather forecasting began with early civilizations using reoccurring astronomical and meteorological events to help them monitor seasonal changes in the weather. Around 650 B.C., the Babylonians tried to predict short-term weather changes based on the appearance of clouds and optical phenomena such as haloes. By 300 B.C., Chinese astronomers had developed a calendar that divided the year into 24 festivals, each festival associated with a different type of weather.
Weather prediction is one of the most challenging field.
[2] It was not until the invention of the electric telegraph in 1835 that the modern age of weather forecasting began. Further the different complex models were developed for forecasting the weather with higher accuracy. Modern technology, particularly computers and weather satellites, and the availability of data provided by coordinated meteorological observing networks, has resulted in enormous improvements in the accuracy of weather forecasting. The satellite technology has largely helped in the large area weather forecasting and made it easier. The accuracy of the weather forecasting has been going on increasing due to inclusion of more parameters and the help of the simulators.
[3]Accurate and ideal forecasting of weather data through modeling has been challenging for scientists along with engineers since decades and centuries. The mathematical modeling and computation play significant role to overcome this challenge. Different techniques have been working, with various enhancements to get
accurate diagnosis. Though, the diagnosis is difficult due to presence of complex nonlinear relationships dependent and independent parameters of this data set. So due this reason
[4] AI can help make more accurate forecasting a reality. Weather forecasting is an ideal use case for AI, because there is a treasure trove of historical and currently available weather data (with more sensors being frequently added), which can feed data into algorithms that literally require both quality and quantity in order to be effective at mating past occurrences with future predictions.
Among companies using AI to predict the weather, few have invested so heavily as IBM.
The company first got involved in trying to use their computer systems to improve forecasts in 1996 and have been refining their project ever since. In 2016 IBM closed on their purchase of Weather Company’s properties including weather.com, Weather Underground, The Weather Company brand and WSI, its global business-to-business brand.
Panasonic has been working on its own weather forecasting model for years, and it stepped up its effort with the purchase of AirDat in 2013. The company makes TAMDAR, a specialty weather sensor installed on commercial airplanes.
[5] Weather parameters
measure and predict the amount to precipitation. The standard way to measure the rainfall is through rain gauge. The standard unit of measuring rain by rain gauge is millimeter per hour which means in one square meter of area is covered by one liter of water.
v. Wind
Wind is the movement of air in a horizontal direction over the earth’s surface. The direction from which the wind is blowing can be a good indicator of weather to come. Wind always blows in a circular pattern around high and low pressure cells. It blows clockwise around a high and counterclockwise around a low. This circulation is a direct result of the earth’s rotation and is termed the Coriolis Effect.
Besides the hardware components are DHT11 sensor to measure relative humidity and temperature and for precipitation we use rain gauge. Similarly for the collection of real time data, GSM module is being used.
For instance, if the weather condition of 16 November 2012 is to be predicted then we will take into consideration the conditions from 09 November 2012 to 15 November 2012 and conditions from 09November to 22November 2011 for previous years. Now in order to model the aforesaid dependencies the current year‘s variation throughout the week is being matched with those of previous years by making use of sliding window. The best-matched window is selected to make the prediction. The selected window and the current year‘s weekly variations are together used to predict the weather condition. The reason for applying sliding window matching is that the weather conditions prevailing in a year may not lie or fall on exactly the same date as they might have existed in previous years. That is why seven previous days and seven on-going days are being considered.
Hence a total period of fortnight is checked in previous condition to find the similar one. Sliding window is quite good technique to capture the variation that could match the current year‘s variation.
Also form the regression equation we are able to predict the future upcoming weather condition. In regression equation, linear regression equation, Lagatic regression equations are most popular predicting equations.
Expected outcome
The outcome of the project will be web desktop based web application program which will provide the basic information of weather as maximum and minimum temperature, humidity and precipitation. This will help many people like farmers, travelers, etc. including all general people. In the same way it will provide information to the various factors like Road Traffic Control System, Water Navigation, Air Traffic Control System.
