AI Advancement is Making Weather Forecasting More Precise Day by Day.
For a long time, weather prediction seemed impracticable. Yet, with advancements in technology and new innovations, this endeavor has become both feasible and bizarre. Our access to a varied range of information is advancing at a rapid pace as technology advances.
The most recent AI weather prediction has improved on traditional methods. All of this research has been published in journals, and it is projected that catastrophic weather can be predicted two to six weeks in advance.
Researchers from China, the United States, and India evaluated the distribution of temperature, precipitation, and wind patterns in a geographical region. The results demonstrate that Artificial intelligence - generated predictions can accurately prognosticate extreme weather phenomena like heavy rain, snowfall, and wind.
How does prediction work?
The traditional process:-
Meteorologists forecast the weather using a range of traditional approaches. Satellite imagery is one of them. Deep space satellites, weather balloons, and radar systems are used in this manner. However, satellites can't see the entire world, and their data isn't always reliable, especially since so many things can go wrong with each individual collection equipment.
The current stage
With the advent of technology, new ways for measuring current and historical weather have been developed. Artificial intelligence is increasingly used to make weather forecasts. Weather forecasting using AI is far more accurate than previous approaches.
It generates more detailed weather forecasts. Predictions seem to be the market's next big thing, and AI techniques can assist improve the precision of real-time predictions because they are unique from all others. AI forecasts have the potential to have a greater impact on businesses than ever before because they are unbiased and based on machine learning. Analytics Foresight discusses these AI models and methodologies, which can manage huge data sets from numerous sources and improve the precision of real-time forecasts. AI technologies can assist meteorologists in completing the gaps in their data gathering based on the premortem analysis.
So - how does it help meteorologists?
Based on the premortem analysis, AI systems can fill in the gaps in meteorologists' data collection.
AI has a lot of potential in meteorological prediction, but if we give AI more data at a faster rate, it can produce more beneficial results across the board.
The data received from the satellite is essentially in raw form; all direct data for image prediction is useless unless and until it is processed using specified mathematical models.
The raw data that we need as input for mathematical models are processed and stored in a data warehouse during the prediction process.
This data appears to be directly taken in as input by the mathematical model, which absolutely gives necessary information. Accordingly, data mining/ data preprocessing has been introduced.
Why is data pre-processing so important in AI-powered weather forecasting?
Data preprocessing is part of data mining that helps in the processing of raw data. Similarly, Data mining is the process of collecting information from data for a variety of purposes, the most common of which include forecasting the future and identifying patterns in the data. Today's data collection is at an all-time high, and businesses want tools to help them sort through it all and make sense of it. In today's data-driven society, data mining technologies like NMF are essential. There are various types of tools that could really help prediction, such as:-
Decision Trees
Rule-based Methods
Neural Networks
Naive Bayes
Bayesian Belief Networks
Support Vector Machines
Among all of them, the method decision tree method is one of the most innovative and quick methods of finding.
Decision tree method:-
The method of decision tree learning is widely utilized in data mining. The goal is to build a model that can predict the value of a target parameter given a set of input parameters.
By dividing the source data set into subsets based on an attribute value test, a tree can be taught to learn. This procedure is analogous to the learning of the Gini coefficient. It is a method of using the data set's information to create a model that predicts the values of the parameters.
When a target parameter has a large number of possible values, decision tree learning is utilized as a machine learning technique. For example, in comparison with weather forecasting, we have intuited different models just for reference. For example, in similarity to weather forecasting, we have devised a number of alternative models solely as a point of comparison.
on.
| model\_1 = DecisionTreeClassifier(max\_depth = 8, criterion = 'gini')
model\_1 .fit(x\_train , y\_train)
model\_1\_score\_train = model\_1.score(x\_train,y\_train)
print("Training score:" , model\_1\_score\_train)
model\_1\_score\_test = model\_1.score(x\_test,y\_test)
print("Testing score:" , model\_1\_score\_test) |
(We've developed a machine learning model based on the Decision Tree classifier, and by specifying the max depth parameter, the tree will classify itself until the passed value is reached; if the parameter is left empty, the classification will continue until there is only one node.) This is why, compared to other methods, the decision tree makes it easier to reach a conclusion.
We've added a few images for the references to make it easier to comprehend and get started.
| Date | Climate | Temperature (C) | Humidity | Target |
|---|---|---|---|---|
| 1/1/2006 | rain | 20 | 25 | 1 |
| 2/1/2006 | snow | 12 | 21 | 0 |
| 3/1/2006 | snow | 15 | 23 | 1 |
| 4/1/2006 | rain | 22 | 25 | 0 |
| 5/1/2006 | rain | 23 | 26 | 1 |
| 6/1/2006 | snow | 16 | 22 | 0 |
| 7/1/2006 | rain | 21 | 23 | 0 |
| 8/1/2006 | rain | 22 | 24 | 1 |
| 9/1/2006 | snow | 15 | 21 | 0 |
| 10/1/2006 | rain | 21 | 25 | 0 |
| Node | GINI Computation Formula | GINI Index |
|---|---|---|
| Overall | 1-((4/10)^2 + (6/10)^2) | 0.48 |
| Climate = S | 1-((3/6)^2 + (3/6)^2) | 0.5 |
| Climate = R | 1-((1/4)^2 + (3/4)^2) | 0.375 |
| Climate | (6/10)*0.5 + (4/10)*0.375 | 0.45 |
| GINI Gain | Gini(Overall)-Gini(Climate) | 0.03 |
With a root node that is divided into two split nodes, the Gini coefficient is clearly enforced in these pictures. We can also ensure that Gini increases value by using the algorithm below. This phase essentially aids the decision tree approach, making it more straightforward and reliable than other methods of searching.
Advantages of AI in weather forecasting:-
As we can understand that AI can predict weather information, it becomes too reliable for other people to ensure poor weather conditions result. They can simply organize meetings, programs, and other events.
In the event of a disaster, the prediction can assist you in evaluating your neighborhood. Unfortunately, those cautions may not be heeded since people do not place enough faith in forecasts. Scientists are working on a new forecast that will utilize artificial intelligence to predict and forecast future weather disasters.
Forecasting has never been more accurate than it is now, thanks to AI. In fact, this new projection is anticipated to be 70% correct. Tornadoes, hurricanes, and other catastrophic weather disasters can be tracked using artificial intelligence. The evacuation process will be must faster and more reliable. Furthermore, when the severity of such storms grows, the speed and accuracy of this tracker may save lives.
One of the most important applications of weather forecasting is in the aviation business, where it was formerly difficult for pilots and other aviation employees to keep up with weather conditions, which might result in a considerable waste of time, money, and safety.
However, with this new invention, an alert was given to pilots and aviation workers a few hours ago, allowing them to check each and every safety point that may be triggered for passenger safety.
Artificial intelligence and technological innovation are definite boons for humankind. They help us do a lot of tedious work and make things easier. More importantly, they save human life. It is said that one day, artificial intelligence will be as intelligent as humans, and maybe even more so. But given the present scenario, AI seems to be an essential part of humankind. If you want to upgrade yourself with this emerging and future-mandated technology, you must opt for a job-ready artificial intelligence course. It will help you secure your career and foster growth at the same time.
