Abstract—- Weather is an ever changing phenomenon. It has various components such as temperature, rainfall, cloud cover, humidity etc. Weather affects every sphere of human life, whether it is business or Entertainment. Weather Forecasting is one of the most technologically challenging tasks. Weather forecasting using Data mining is an innovative way of predicting the weather with minimal cost and maximum accuracy.
Introduction The need for weather forecasting has been amply justified over the years. Weather forecasting has existed since times unknown. The method for weather forecasting and the technology used has since massive improvement over the years. In ancient times the weather cock was used to determine the direction of the wind, the
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The common thread between all these methods is that the prediction is made on basis of the past occurrences of the characteristic of weather. It is thus noted that weather records for multiple hundreds of years are available for our perusal.
Since such humungous amounts of historical data is available to us using data mining to predict the weather seems to be a smart option. Predictive analysis is a sub type of data mining which is centered on pattern mining. Data mining to predict weather is a smart, effective and accurate way to get optimum results for predicting the weather.
Today, solar power has become part of our daily lives. Appliances like solar notebooks, solar air-conditioners, solar cars, etc. demonstrate the use of the sustainable power of the sun. As the adverse effects of burning of fossil fuels and the depletion rate of non-renewable energy sources increase, the future of solar energy looks bright. The problem with renewable sources of energy is that they are not easily predictable in advance and vary based on both weather as well as site specific conditions.
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To combat noisy data the dataset was first preprocessed and the missing values were replaced with a global constant. When we encounter a situation where the data to be considered is a missing value. The preliminary check performed identifies the global constant and the data from the previous day is used instead.
2. The second approach we adopted was to set a maximum and min criteria for the parameter to be predicted. We analyzed data for the past 5 years and we set values for the max and min occurrences for every month, if the system encounters a value which does not lie between the range of the maximum and minimum value it is scrapped as garbage value. In its place the previous day’s data is used.
3. Predictions are always tentative and many factors such as global warming affect the climate. If a prediction is made by the system which falters we adopt the feedback method. The predictions that we make are cross checked with the prediction from an authentic source and if the predictions are not found admissibly accurate the actual accurate predictions are placed in the system instead of the faltering
NOAA. (2007, December 14). History of Tornado Forecasting. Retrieved March 4, 2011, from NOAA: http://celebrating200years.noaa.gov/magazine/tornado_forecasting/welcome.html#knowledge
...xpected weather conditions over time, previous weather conditions, possible areas of less deteriorating weather conditions, expected duration of bad weather condition.
Climate, weather, and meteorology are 3 words that seem to be the same but in reality differ significantly. Two of these concepts pertain to the atmosphere but differ in what time and place they are studied in, and the last one is studying these concepts.
The argument to support the fact that global weather patterns have changed, draws its evidence from a number of sources and documented events, and by observation of what is happening in the environment. The increase in carbon emissions from industry, transportation and farming practices is widely accepted as being responsible for the greenhouse
The “normal” temperature for the day meteorologist forecast is based on median value. According to AccuWeather meteorologist they use multiple different factors to forecast temperature. The first step they look at is what has happened yesterday which is used to forecast for today and week, they take into account of temperature, pressure, wind speed, and precipitation using tools like satellite and radar. Also, surface map helps meteorologist identify weather location, like high and low pressure, cold and warm fronts, cloud cover, wind and precipitation. The next step "Meteorologists have computer models which model the atmosphere using parameters such as barometric pressure, temperature, humidity and a plethora more of complicated mathematical
Preview: Today I will discuss the potential that solar power has to become this country’s main supply of energy and the latest research that can make solar power more efficient and cost effective. I will also present the environmental benefits that come with using solar power over other and more harmful forms of energy.
Forecasting hurricanes has two components to it: where the hurricane is going (track) and how strong it is going to be (intensity). Hurricane forecasting has improved over time, “In 1992, hurricane forecasts were issued to only three days, but now they are issued to five—and soon they will be given for up to a week.” (Main). Predicting hurricanes is extremely difficult, but the development of faster computers and better satellite data has decreased the error percentage of forecasting hurricanes.
Data mining is a combination of database and artificial intelligence technologies. Although the AI field has taken a major dive in the last decade; this new emerging field has shown that AI can add major contributions to existing fields in computer science. In fact, many experts believe that data mining is the third hottest field in the industry behind the Internet, and data warehousing.
Data is defined as facts, concepts, information, or instructions in a formalized manner suitable for communication, interpretation, or processing by humans or by automatic means. Like mentioned before this processing is usually assumed to be automated and running on a computer. Data are most useful when well-presented and actually informative, data-processing systems are often referred to as information systems. Now that we know the purpose and meaning of data, we proceed to explain what data mining consist of.
Data mining is a field that is a combination of numerous other fields such as the database research, artificial intelligence and statistics. Data mining involves looking for patterns in vast amounts of data as a part of knowledge discovery process. (Huang, Joshua Zhexue, Cao, Longbing, Srivastava, Jaideep, 2011) contains numerous papers that are solely dedicated to discussing the advancements that have been made in the field of data mining and knowledge discovery. A lot of people have performed a thorough research on all that has been done in data mining and the future possibilities that are soon to be implemented practically. The research not only covers the history and the reasons that led to various advancements being made but they also cover the detail models of the proposed solutions to deficiencies in existing systems.
I knew a lot about weather because my mom was studying weather when she was in the military. Meteorologist study weather. My mom would map out every weather system in different colors.
Description: Data Mining contains of several algorithms that fall into four different categories(Shobana et al. 2015)
The weather forecasters use probability and statistics just as much if not more than any other field on earth. As weather patterns are not fully understood and are dynamic, analysts have to rely heavily on past weather systems and patterns to “guess'; or estimate the possibility of present weather systems to behave in similar manners. If the probability of its behavior, subject to certain factors, in one manner over another is high forecasters make decisions as to how to advise the public.
As early as in 1938, in late nineteenth Century a British meteorologist Kalinda in the analysis of
...echnology in the agricultural sector has created opportunities for farmers to apply data from remote sensing satellites to their crops. Data mining especially been very handy in helping systems make better suggestions to farmers based on the legacy data. Crop predictions and quality forecasts are now more accurate. Artificial intelligence and neural networks have been employed to build adaptive systems that teach themselves from the results entered by the farmers. The systems are now “situation-aware”.