Research on weather forecast using Artificial Intelligence

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Predicting Weather Data Using Artificial Intelligence

Artificial Neural Network with Feed Back Propagation algorithm used to implement artificial intelligence in weather forecast. Artificial Intelligence minimizes the error and provides appropriate weather forecast.

Product Overview

Predicting accurate weather condition is vital for many reasons in multiple areas such as agriculture, energy supply, transportations etc. Here we come up with a technique by combining whether forecast with artificial intelligence. There are multiple artificial intelligence techniques to identify and predicting climatic condition with certain accuracy which are used for multiple purpose. For weather forecast there are Artificial Intelligence techniques such as Artificial Neural Network, Ensemble Neural Network, Backpropagation Network, Radial Basis Function Network, General Regression Neural Network, Genetic Algorithm, Multilayer Perceptron, Fuzzy clustering, etc. In this study we found neural network with backpropagation algorithm with minimal error. We explore new directions with forecasting weather as a data intensive challenge that involves inferences across space and time. We introduce methods that show promise for advancing the state of the art of weather forecasting systems. In this technique we use multiple input parameters to forecast weather based on terms such as temperature, rainfall, humidity, cloud condition, and weather of the day. Now-a-days many live systems depend on weather conditions to make necessary adjustments in their systems. In this study we focused on Artificial Neural Networks to forecast the weather. An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems. An Artificial Neural Network is configured for a specific application, such as pattern recognition or data classification, through a learning process. In this study we had proposed Artificial Neural Network with backpropagation algorithm for forecasting weather accurately. Accuracy is vital in weather forecasting. The input parameters must be handled based on the Artificial Intelligence technique. Artificial Intelligence is associated with non-linear data.Artificial Neural Network technique uses an iterative process of training data and repeatedly compares the observed output with targeted output and calculate the error. This error is used to readjust the values of weights and bias to get an even better output. Hence this method tries to minimize the error and provides accurate whether forecast.


  • To get accurate weather forecast
  • To minimize the error
  • Analyze pattern to generate weather forecast
  • To be used in multiple areas
  • To be associated with non-separable data

  • Input Data may include following parameters
  • Temperature: Temperature is the subjective perceptions of hot and cold. Temperature is quantitative information.
  • Humidity: Humidity is the quantitative information in our study which gives information about amount of water vapor present in the air.
  • Rainfall: In this study, a vital parameter to predict weather forecast is rainfall measurement which is in the form of quantitative information in this application.
  • Cloud Condition: Cloud condition specifies the fraction of the sky obscured by clouds when observed from a particular location.
  • Predicts accurate whether forecast which is quantitative information and can be used for multiple real-time applications.
  • Algorithm Used
    We will use Multi-Layer Perceptron (MLP) and Backpropagation algorithm which will be used to train the data
  • However, we will concentrate on nets with units arranged in layers
  • Multi-Layer Perceptron Methodology works on non-linear separable data
  • Following are steps of algorithm
  • Initialize weights at random, choose a learning rate n
  • Until network is trained:

  • For each training example (input pattern and target outputs):
  • Present inputs for the first pattern to the input layer
  • Sum the weighted inputs to the next layer and calculate their activations using activation function formula
  • Present activations to the next layer, repeating (2) until the activations of the output layer are known
  • Compare output activations to the target values for the pattern and calculate deltas for the output
  • Propagate error backwards by using the output layer deltas to calculate the deltas for the previous layer
  • Use these deltas to calculate those of the previous layer, repeating until the first layer is reached
  • Calculate the weight changes for all weights and biases (treat biases as weights from a unit having an activation of 1)
  • If training by pattern, update all the weights and biases, else repeat the cycle for all patterns, summing the changes and applying at the end of the epoch
  • Conclusion

    Predicting weather forecasting with minimum error is a big challenge. Accurate weather forecast is used in many real-time systems like electricity departments, airports, tourism centers, etc. Predicting weather forecast depends on nature of parameters. Each parameter has a different range of values. In this study we overcome this issue by addressing Artificial Neural Networks. Proposed Artificial Intelligence technique analyzes all complex parameters and generates intelligent patterns and used this pattern to predict whether. The Artificial Neural Network minimizes error by using back propagation algorithm where actual output is subtracted from desired output in order to minimize the error in predicting whether forecast. Here we can use this whether forecast for multiple real-time systems.