Weather Prediction Using Machine Learning

In our work we predict the weather that will usually be in the field of physics-based Numerical Weather Prediction (NWP) methods. We have the accessibility of big amount of previous weather data and the improvements in machine learning; there will be an increasing interest in exploiting these methods for prediction. We need your contribution to this project only at minimum level. We will update the work program to scholars on a regular basis. Our team will take care of everything right from topic selection to thesis writing and paper publishing of your machine learning project. To develop a weather prediction system by utilizing machine learning methods, we have given guidance:

  1. Objective Definition

            State the main aim: “To forecast particular weather parameters (e.g., temperature, humidity, and rainfall) by proposing a machine learning method for a given location and future time”.

  1. Data Collection
  • Public Datasets: Our work utilizes the datasets like NOAA’s Integrated Surface Database that offers previous weather data.
  • Weather API’s: We provide previous data from the services like Open Weather Map or Weather Underground.
  1. Data Preprocessing
  • Data Cleaning: Our work cleans the data by handling missing values, outliers or mistaken records.
  • Temporal Features: In our work, we take out features like time of day, day of the week, month and season.
  • Feature Engineering: Our work examines some of the developing structures like rolling averages (e.g., for the past three days average temperature).
  1. Model Selection & Development
  • Regression Models: Our work forecast the constant variables like temperature by utilizing the following methods:
  • Linear Regression
  • Decision Trees or Random Forests
  • Gradient Boosting Machines
  • Classification Models: To predict the categorical results like “will it rain tomorrow?”
  • Logistic Regression
  • SVM
  • Neural Networks
  • Time Series Models: We propose it particularly for time-ordered data
  • ARIMA or Seasonal ARIMA
  • LSTM (Long Short-Term Memory) networks
  • Prophet by Facebook
  1. Training the Model
  • Our work divides the datasets into two sets namely training and testing. To make sure that the training data is in sequential order before testing the data, it is important for time series.
  • We make sure that the training data is not overfit by train the selected framework on training data.
  1. 6. Model Evaluation
  • Mean Absolute Error (MAE), Mean Squared Error (MSR), or score are the metrics that were utilized for regression tasks.
  • We examine the metrics like accuracy, precision, recall, F1-score and ROC curves for classification tasks.
  • Based on the test data, we always estimate the models achievements.
  1. Optimization & Hyperparameter Tuning
  • To get better achievements, our work modifies the model’s parameter.
  • For efficient hyperparameter tuning, our project utilizes the methods like grid search or random search.
  1. Deployment
  • To offer real time weather prediction, we combine the prediction framework into web applications, mobile apps, or other systems.
  • Based on the prediction horizon, we taking into account that the updating forecasting’s at even intervals.
  1. Feedback Loop
  • We frequently observe the methods forecasting against real weather findings.
  • To enhance and retrain our model periodically, we utilize the feedback.
  1. Conclusions & Future Enhancements
  • At last we summarize the achievements, limitations and the field of enhancement during our project.
  • Our work ensures the developments like:
  • Increased to multi-location or regional prediction.
  • Including real-time data feeds (e.g., from weather satellites).
  • Forecasting additional parameters like air quality or UV index.


  • External Data: To enhance the forecasting accuracy, we include extra datasets like satellite imagery or data from local weather stations.
  • Temporal Consistency: Managing sequential order of records is essential, when we are working with the time series data.
  • Combining methods: We combine machine learning with traditional numerical weather forecasting methods to obtain hybrid models that provide higher outcomes.

Machine Learning displays the assurance in weather prediction, and it is important to recall that the weather structures are complicated. We offer the more accurate and dependable predictions by merging machine learning forecasting with insights from traditional meteorological methods. So without no more delay contact us to succeed in your academics.

Weather Prediction using Machine Learning Projects

Weather Prediction Using Machine Learning Thesis Topics

The best thesis topic that suits your interest will be suggested we don’t impose our wish on you. Thesis topics will be given and we carry out the work with the topic that you have chosen. The methodological approach that we use to test or research problem will be explained. We are surer that you can get a good grade by working with us.

  1. Machine Learning-based Weather Prediction: A Comparative Study of Regression and Classification Algorithms


machine learning, classification, weather prediction, boosting algorithms, ensemble learning

            Our paper utilizes the different types of ML and boosting algorithms to predict weather like rain, sunshine, clouds, fog, drizzle and snow. We use a dataset made up of historical data to train and estimate different methods. The ML methods we used are decision tree, random forest, Naïve Bayes, K-NN and SVM. We also used Boosting methods like XGBoost and AdaBoost to improve the accuracy of our prediction. The results were confirmed using ROC curve and lift curve analysis. 

  1. A Survey on Weather Prediction using Big Data and Machine Learning Techniques


Artificial neural network, Photovoltaic, Random Forest, Weather, Numerical weather prediction, Climate

            Our paper uses ML techniques to predict the weather and climate. Our paper debated about the precipitation, pressure, radiation, wind and temperature like meteorological fields, Random Forest, Artificial Neural Network, Deep Learning, XGBoost and Support Vector Machine methods. We also provide a systematic outline for big data analytical methods to predict weather.      

  1. Comparative Analysis of Machine Learning Algorithms for Weather Prediction using Error Detection


Prediction, Linear Regression, Polynomial Regression, Cart, Forest Depth, Accuracy

            Our paper collects the meteorological and geographic data and we have used five machine learning methods like Linear regression, polynomial regression, random forest regression, decision tree regression and random forest dept. The dataset were taken from kaggle and the ML methods were chosen as regression models to predict weather. In addition we use five-fold cross validation methods to increase their performance.

  1. Real Time Weather Prediction System using Ensemble Machine Learning


Gaussian naive Bayes (GNB), K-Nearest Neighbor (KNN), gradient boosting classifiers, Support Vector Classifier (SVC)

            We used ML methods to predict weather and we examine the efficiency of four ML based classification methods namely K- Nearest Neighbors, Gaussian Naïve Bayes, Gradient boosting classifier and support vector classifier to predict weather. Our paper also finds the use of ensemble learning to merge the prediction of multiple models to increase accuracy. KNN and hybrid ensemble gives the better weather prediction. 

  1. Optimizing Numerical Weather Prediction Model Performance Using Machine Learning Techniques


Scientific application, GloSea6, I/O optimization, profiling

            The Korea Meteorological Administration (KMA) has used GloSea6 to predict weather. These model faces complication when running the model and to address this issue we used KMA to improve a low-resolution model called low GloSea6. Two steps were used in our paper at first, we collect data too utilize profiling tools to get optimal hardware platform and low GloSea6 parameters and at second ML model was trained using the gathered data.

  1. Weather Prediction Analysis using Classifiers and Regressors in Machine Learning


Weather Forecast, Classification, Regression, Comparative study

            We used ML methods to predict different weather patterns like storms, hurricanes, temperature changes, cyclones and floods. Our paper also compares the achievements of various DL methods for weather prediction like decision tree classifier and SVR that can be randomly used for this.  Our work concentrates five various ML methods for classification dataset and four ML methods for regression dataset. Decision tree regressor gives the best prediction outcome. 

  1. An Innovative Machine Learning Technique for the Prediction of Weather Based Smart Home Energy Consumption


Appliances, decision support too, energy consumption prediction, smart home

            Our paper proposes a decision algorithm model by utilizing ML based data mining and picture fuzzy operator.  First we used ML methods to train and test energy consumption of home appliances according to the weather data and at second we use Lasso Regression to understand the pattern and feature of weather data. We also propose a decision matrix using fuzzy operator to combine ML, prior to ranking using a score function. 

  1. Jumpiness Correction for Station Numerical Weather Prediction Using Machine Learning Algorithm


numerical weather prediction, jumpiness, correction, support vector machine, Gaussian process regression

            We used a jumpiness of numerical weather prediction (NWP) is not favourable for weather prediction and bring challenge to meteorological disaster prevention. To solve this issue we used two ML methods like support vector machine and Gaussian process regression. The result shows the effect of multiple forecast aging outcomes and the root mean square error has decreased improvement range increases with increase of forecast aging and the SVM gives the best result. 

  1. Machine Learning Technique Based Weather Prediction System


Gradient Boost algorithm, Extreme Gradient Boosting algorithm (XGBC)

            Our paper uses database to prediction the weather. We have to predict the atmospheric condition of certain place and a set of data. We also used the raw data to predict drizzle, rain, sun, snow, and fog have collected from kaggle that may include precipitation, temperature max, temperature min and wind. Our aim is to predict the weather using KNN, SVM, XGBC, and Gradient Boost.

  1. Day-Ahead Forecasting for the Tropics with Numerical Weather Prediction and Machine Learning


Solar forecasting, Radiation schemes

            The most popular methods to predict weather are Numerical Weather Prediction (NWP) and ML techniques. NWP models have multiple possible physical parameterizations that site- specific NWP optimization. Our paper uses hybrid numerical- statistical approach and can estimate for four radiation models. Weather Research and Forecasting (WRF) can run on both global and regional mode to give an estimate of solar irradiance.  The outcomes were gained from CAM, GFDL, New Goddard and RRTMG radiation models


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1. Novel Ideas

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To improve the quality and originality of works, we are strictly avoiding plagiarism since plagiarism is not allowed and acceptable for any type journals (SCI, SCI-E, or Scopus) in editorial and reviewer point of view. We have software named as “Anti-Plagiarism Software” that examines the similarity score for documents with good accuracy. We consist of various plagiarism tools like Viper, Turnitin, Students and scholars can get your work in Zero Tolerance to Plagiarism. DONT WORRY ABOUT PHD, WE WILL TAKE CARE OF EVERYTHING.

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  • Technical Info: We never share your technical details to any other scholar since we know the importance of time and resources that are giving us by scholars.
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