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An Insight into Weather Forecasting using Machine Learning and Artificial Intelligence
Weather forecasting is the task of predicting the state of the atmosphere at a future time and a speciﬁed location. Traditionally, this has been done through physical simulations in which the atmosphere is modeled as a ﬂuid. The present state of the atmosphere is sampled, and the future state is computed by numerically solving the equations of ﬂuid dynamics and thermodynamics. However, the system of ordinary diﬀerential equations that govern this physical model is unstable under perturbations, and uncertainties in the initial measurements of the atmospheric conditions and an incomplete understanding of complex atmospheric processes restrict the extent of accurate weather forecasting to a 10-day period, beyond which weather forecasts are signiﬁcantly unreliable.
What is Machine Learning?
Machine learning, is relatively robust to perturbations and doesn’t require a complete understanding of the physical processes that govern the atmosphere. Therefore, machine learning may represent a viable alternative to physical models in weather forecasting. Before the advancement of Technology, weather forecasting was a hard nut to crack. Weather forecasters relied upon satellites, data model’s atmospheric conditions with less accuracy. Weather prediction and analysis has vastly increased in terms of accuracy and predictability with the use of Internet of Things, since last 40 years. With the advancement of Data Science, Artificial Intelligence, Scientists now do weather forecasting with high accuracy and predictability.
How Machine Learning helps in Prediction of weather related events?
There are many types of machine learning algorithms, of which two are most important in predicting the weather, which are Linear Regression and a variation of Functional Regression. These models are trained based on the historical data provided of any location. Input to these models are provided such as if predicting temperature, then minimum temperature, mean atmospheric pressure, maximum temperature, mean humidity, and classification for 2 days. Based on this Minimum Temperature and Maximum Temperature of 7 days will be achieved.
What is Classification?
When collecting datasets to provide to the models there are certain parameters which are called as classified data which includes: snow, thunderstorm, rain, fog, overcast, mostly cloudy, partly cloudy, scattered clouds, and clear. These can be further classified into four classes.
- 1. Rain, thunderstorm, and snow into precipitation
- 2. Mostly cloudy, foggy, and overcast into very cloudy
- 3. Scattered clouds and partly cloudy into moderately cloudy
- 4. Clear as clear
How Algorithms are used in Predicting Weather?
There are various techniques of predicting weather using Regression and variation of Functional Regression, in which datasets are used to perform the calculations and analysis. To Train the algorithms ¾ size of data is used and ¼ size of data is termed as Test set. For Example, if we want to predict weather of Austin Texas using these Machine Learning algorithms, we will use 6 Years of data to train the algorithms and 2 years of data as a Test dataset.
On the contrary to Weather Forecasting using Machine Learning Algorithms which is based primarily on simulation based on Physics and Differential Equations, Artificial Intelligence is also used for predicting weather: which includes models such as Neural Networks and Probabilistic model Bayesian Network, Vector Machines. Among these models Neural Network is widely used due to its ability to capture non-linear dependencies of past weather trends and future weather conditions.
However, certain machine learning algorithms and Artificial Intelligence Models are computationally expensive, such as using Bayesian Network and machine learning algorithm in parallel.
To conclude, Machine Learning and Artificial Intelligence has greatly change the paradigm of Weather forecasting with high accuracy and predictivity. And within the next few years more advancement will be made using these technologies to accurately predict the weather to prevent disasters like hurricane, Tornados, and Thunderstorms.