Weather Prediction using Neural network

Neural network is an exciting region of research that is employed by weather forecasting. Traditional Numerical Weather Prediction (NWP) frameworks are incorporated by meteorological agencies; simulate the atmosphere by employing difficult physical equations. These frameworks are computationally exclusive and sometimes may not accurately capture local weather phenomena. We offer individual attention for scholars as we have more than 100+ experts in our concern. The professionals who will handle all your research are PhD experts so all your research will lead to success.  In contrast, neural networks learn patterns from past data, which possibly increases prediction accuracy, particularly for short-term forecasting.

            Here we give an outline of how one may go about weather forecasting using neural networks:

  1. Data Collection:
  • Historical Weather Data: Temperature, pressure, humidity, wind speed and wind direction are some gathered historical data. We employ the datasets from NOAA, ECMWF or other meteorological agencies.
  • Satellite Images: Incorporating Convolutional Neural Networks (CNNs) can be valuable when we are dealing with satellite imagery data that offers details about cloud information.
  1. Data Preprocessing:
  • Normalization: Min-max scaling normalizes every feature, since the neural networks are complex to input scales.
  • Sequence Creation: For recurrent networks like LSTM or GRU, Our framework generates arrangement of data like the historical 24 hours of data to forecast the next hour.
  • Train-Test Split: Our model divides the data into two sets namely training and test datasets.
  1. Model Selection:
  • Feedforward Neural Networks (FNN): We employ FNN for straight forward forecasting, for instance: our framework forecasts temperature on the basis of the day before data.
  • Convolutional Neural Networks (CNNs): CNN is useful for us when we are dealing with spatial data like satellite imagery.
  • Recurrent Neural Networks (RNNs): For consecutive data, RNN is helpful and for time series prediction, LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Units) are the famous selection.
  1. Training the Model:
  • Backpropagation: For training neural networks, Backpropagation is the standard framework for us.
  • Regularization: By taking into account the methods like dropout or L1/L2 regularization is employed to avoid overfitting.
  • Hyperparameter Tuning: To find the best framework hyperparameters, our model utilizes a cross-validation method. For instance learning rate, number of layers and number of units in each layer.
  1. Evaluation:
  • Mean Absolute Error (MAE), Mean Squared Error (MSE) or Root Mean Squared Error (RMSE) are the metrics that we utilize to estimate the framework’s efficiency on the test set.
  1. Deployment:
  • If we are fulfilled with the model, then deploy the framework for real-time or near-real-time forecasting. Taking into account that the framework may require retraining as novel data becomes present.


  • Non-Stationarity: Periodically the weather pattern alters, so the framework trained on the historical data is less accurate in future.
  • Complex Interactions: Weather phenomena end from complicated interactions of many reasons that are difficult for a framework to seizure.
  • High Dimensionality: We utilize satellite imagery particularly for high-dimensional weather data.


            Neural networks provide a promising substitute or supplement to traditional NWP frameworks, particularly for short-term prediction or in the region where traditional frameworks are low accurate. However the success of a neural network based approach highly depends on the quality and quantity of data, the structure of the neural network and the particular issue being noticed.

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Weather Prediction Using Machine Learning

Weather Prediction Research Topics

  1. Comparative Study to determine Accuracy for Weather Prediction using Machine Learning
  2. Weather Prediction in Jakarta: An Analysis of Climate Data and Regional Influences using LSTM and GRU
  3. Weather Prediction using linear regression model
  4. A Survey on Weather Prediction using Big Data and Machine Learning Techniques
  5. Visual Weather Analytics-Leveraging Image Recognition for Weather Prediction
  6. Comparative Analysis of Machine Learning Algorithms for Weather Prediction using Error Detection
  7. Spatiotemporal Post-Calibration in a Numerical Weather Prediction Model for Quantifying Building Energy Consumption
  8. Systematic Analysis of Weather Prediction for Jaipur City Dataset Using Deep Learning
  9. Real Time Weather Prediction System using Ensemble Machine Learning
  10. Machine Learning-based Weather Prediction: A Comparative Study of Regression and Classification Algorithms
  11. Prediction of Weather Forecasting with Long Short-Term Memory using Deep Learning
  12. Time Stamp Feature Analysis Based Weather Prediction Using Dense Hared Rate Based Classification for Successive Crop Recommendation in Big Data Analysis
  13. IoT for Agriculture System-Weather Prediction & Smart Irrigation System for Single Plot, Multiple Crops
  14. Weather Prediction using Long Short Term Memory (LSTM) model
  15. DAOS as HPC Storage: a View From Numerical Weather Prediction
  16. Weather Prediction Analysis using Classifiers and Regressors in Machine Learning
  17. Prediction of Cloud-to-Ground Lightning Through Gaussian Process Regression with Satellite Thermal Infrared Imagery and Numerical Weather Prediction Modeling Data
  18. Investigations on offshore wind turbine inflow modelling using numerical weather prediction coupled with local-scale computational fluid dynamics
  19. BMAE-Net: A Data-Driven Weather Prediction Network for Smart Agriculture
  20. Pairing ensemble numerical weather prediction with ensemble physical model chain for probabilistic photovoltaic power forecasting
  21. Enhancing short-term forecasting of daily precipitation using numerical weather prediction bias correcting with XGBoost in different regions of China
  22. Evaluating neural network models in site-specific solar PV forecasting using numerical weather prediction data and weather observations
  23. Outdoor heat stress assessment using an integrated multi-scale numerical weather prediction system: A case study of a heatwave in Montreal
  24. Correcting rainfall forecasts of a numerical weather prediction model using generative adversarial networks
  25. Analyzing the Performance of Diverse Deep Learning Architectures for Weather Prediction
  26. Using AIoT to Implement a Weather Monitoring and Prediction System
  27. Short-Term Forecast of the Signal Propagation Conditions, Based on Numerical Weather Prediction, Radar, and SatCom Ground Terminal Data
  28. A 3-D Cloud Detection Method for FY-4A GIIRS and Its Application in Operational Numerical Weather Prediction System
  29. Optimizing Numerical Weather Prediction Model Performance Using Machine Learning Techniques
  30. Estimating Annual Temperate Rain Attenuation Distributions at 20/40 GHz With a High-Resolution Numerical Weather Prediction Model
  31. Hybrid Approach for Weather Prediction in IoT Network
  32. An IoT Assisted Weather Prediction and Information Monitoring Scheme based on Intensive Learning Strategy
  33. Spatial Variability of Nocturnal Stability Regimes in an Operational Weather Prediction Model
  34. A modified deep learning weather prediction using cubed sphere for global precipitation
  35. Impact of Hyperspectral Infrared Sounding Observation and Principal-Component-Score Assimilation on the Accuracy of High-Impact Weather Prediction
  36. Development of Non-Parametric Model for Weather Prediction Framework
  37. Temporal Structural Probability Network for Weather Prediction
  38. Grid-to-Point Deep-Learning Error Correction for the Surface Weather Forecasts of a Fine-Scale Numerical Weather Prediction System
  39. Smart Urban Wind Power Forecasting: Integrating Weibull Distribution, Recurrent Neural Networks, and Numerical Weather Prediction
  40. Optimized Wavelength Sampling for Thermal Radiative Transfer in Numerical Weather Prediction Models

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