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:
- 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”.
- 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.
- 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).
- 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
- 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.
- 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.
- 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.
- 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.
- Feedback Loop
- We frequently observe the methods forecasting against real weather findings.
- To enhance and retrain our model periodically, we utilize the feedback.
- 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.
Commands:
- 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 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.
- Machine Learning-based Weather Prediction: A Comparative Study of Regression and Classification Algorithms
Keywords:
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.
- A Survey on Weather Prediction using Big Data and Machine Learning Techniques
Keywords:
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.
- Comparative Analysis of Machine Learning Algorithms for Weather Prediction using Error Detection
Keywords:
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.
- Real Time Weather Prediction System using Ensemble Machine Learning
Keywords:
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.
- Optimizing Numerical Weather Prediction Model Performance Using Machine Learning Techniques
Keywords:
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.
- Weather Prediction Analysis using Classifiers and Regressors in Machine Learning
Keywords:
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.
- An Innovative Machine Learning Technique for the Prediction of Weather Based Smart Home Energy Consumption
Keywords:
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.
- Jumpiness Correction for Station Numerical Weather Prediction Using Machine Learning Algorithm
Keywords:
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.
- Machine Learning Technique Based Weather Prediction System
Keywords:
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.
- Day-Ahead Forecasting for the Tropics with Numerical Weather Prediction and Machine Learning
Keywords
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
Milestones
MILESTONE 1: Research Proposal
Finalize Journal (Indexing)
Before sit down to research proposal writing, we need to decide exact journals. For e.g. SCI, SCI-E, ISI, SCOPUS.
Research Subject Selection
As a doctoral student, subject selection is a big problem. Phdservices.org has the team of world class experts who experience in assisting all subjects. When you decide to work in networking, we assign our experts in your specific area for assistance.
Research Topic Selection
We helping you with right and perfect topic selection, which sound interesting to the other fellows of your committee. For e.g. if your interest in networking, the research topic is VANET / MANET / any other
Literature Survey Writing
To ensure the novelty of research, we find research gaps in 50+ latest benchmark papers (IEEE, Springer, Elsevier, MDPI, Hindawi, etc.)
Case Study Writing
After literature survey, we get the main issue/problem that your research topic will aim to resolve and elegant writing support to identify relevance of the issue.
Problem Statement
Based on the research gaps finding and importance of your research, we conclude the appropriate and specific problem statement.
Writing Research Proposal
Writing a good research proposal has need of lot of time. We only span a few to cover all major aspects (reference papers collection, deficiency finding, drawing system architecture, highlights novelty)
MILESTONE 2: System Development
Fix Implementation Plan
We prepare a clear project implementation plan that narrates your proposal in step-by step and it contains Software and OS specification. We recommend you very suitable tools/software that fit for your concept.
Tools/Plan Approval
We get the approval for implementation tool, software, programing language and finally implementation plan to start development process.
Pseudocode Description
Our source code is original since we write the code after pseudocodes, algorithm writing and mathematical equation derivations.
Develop Proposal Idea
We implement our novel idea in step-by-step process that given in implementation plan. We can help scholars in implementation.
Comparison/Experiments
We perform the comparison between proposed and existing schemes in both quantitative and qualitative manner since it is most crucial part of any journal paper.
Graphs, Results, Analysis Table
We evaluate and analyze the project results by plotting graphs, numerical results computation, and broader discussion of quantitative results in table.
Project Deliverables
For every project order, we deliver the following: reference papers, source codes screenshots, project video, installation and running procedures.
MILESTONE 3: Paper Writing
Choosing Right Format
We intend to write a paper in customized layout. If you are interesting in any specific journal, we ready to support you. Otherwise we prepare in IEEE transaction level.
Collecting Reliable Resources
Before paper writing, we collect reliable resources such as 50+ journal papers, magazines, news, encyclopedia (books), benchmark datasets, and online resources.
Writing Rough Draft
We create an outline of a paper at first and then writing under each heading and sub-headings. It consists of novel idea and resources
Proofreading & Formatting
We must proofread and formatting a paper to fix typesetting errors, and avoiding misspelled words, misplaced punctuation marks, and so on
Native English Writing
We check the communication of a paper by rewriting with native English writers who accomplish their English literature in University of Oxford.
Scrutinizing Paper Quality
We examine the paper quality by top-experts who can easily fix the issues in journal paper writing and also confirm the level of journal paper (SCI, Scopus or Normal).
Plagiarism Checking
We at phdservices.org is 100% guarantee for original journal paper writing. We never use previously published works.
MILESTONE 4: Paper Publication
Finding Apt Journal
We play crucial role in this step since this is very important for scholar’s future. Our experts will help you in choosing high Impact Factor (SJR) journals for publishing.
Lay Paper to Submit
We organize your paper for journal submission, which covers the preparation of Authors Biography, Cover Letter, Highlights of Novelty, and Suggested Reviewers.
Paper Submission
We upload paper with submit all prerequisites that are required in journal. We completely remove frustration in paper publishing.
Paper Status Tracking
We track your paper status and answering the questions raise before review process and also we giving you frequent updates for your paper received from journal.
Revising Paper Precisely
When we receive decision for revising paper, we get ready to prepare the point-point response to address all reviewers query and resubmit it to catch final acceptance.
Get Accept & e-Proofing
We receive final mail for acceptance confirmation letter and editors send e-proofing and licensing to ensure the originality.
Publishing Paper
Paper published in online and we inform you with paper title, authors information, journal name volume, issue number, page number, and DOI link
MILESTONE 5: Thesis Writing
Identifying University Format
We pay special attention for your thesis writing and our 100+ thesis writers are proficient and clear in writing thesis for all university formats.
Gathering Adequate Resources
We collect primary and adequate resources for writing well-structured thesis using published research articles, 150+ reputed reference papers, writing plan, and so on.
Writing Thesis (Preliminary)
We write thesis in chapter-by-chapter without any empirical mistakes and we completely provide plagiarism-free thesis.
Skimming & Reading
Skimming involve reading the thesis and looking abstract, conclusions, sections, & sub-sections, paragraphs, sentences & words and writing thesis chorological order of papers.
Fixing Crosscutting Issues
This step is tricky when write thesis by amateurs. Proofreading and formatting is made by our world class thesis writers who avoid verbose, and brainstorming for significant writing.
Organize Thesis Chapters
We organize thesis chapters by completing the following: elaborate chapter, structuring chapters, flow of writing, citations correction, etc.
Writing Thesis (Final Version)
We attention to details of importance of thesis contribution, well-illustrated literature review, sharp and broad results and discussion and relevant applications study.
How PhDservices.org deal with significant issues ?
1. Novel Ideas
Novelty is essential for a PhD degree. Our experts are bringing quality of being novel ideas in the particular research area. It can be only determined by after thorough literature search (state-of-the-art works published in IEEE, Springer, Elsevier, ACM, ScienceDirect, Inderscience, and so on). SCI and SCOPUS journals reviewers and editors will always demand “Novelty” for each publishing work. Our experts have in-depth knowledge in all major and sub-research fields to introduce New Methods and Ideas. MAKING NOVEL IDEAS IS THE ONLY WAY OF WINNING PHD.
2. Plagiarism-Free
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.
3. Confidential Info
We intended to keep your personal and technical information in secret and it is a basic worry for all scholars.
- 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.
- Personal Info: We restricted to access scholars personal details by our experts. Our organization leading team will have your basic and necessary info for scholars.
CONFIDENTIALITY AND PRIVACY OF INFORMATION HELD IS OF VITAL IMPORTANCE AT PHDSERVICES.ORG. WE HONEST FOR ALL CUSTOMERS.
4. Publication
Most of the PhD consultancy services will end their services in Paper Writing, but our PhDservices.org is different from others by giving guarantee for both paper writing and publication in reputed journals. With our 18+ year of experience in delivering PhD services, we meet all requirements of journals (reviewers, editors, and editor-in-chief) for rapid publications. From the beginning of paper writing, we lay our smart works. PUBLICATION IS A ROOT FOR PHD DEGREE. WE LIKE A FRUIT FOR GIVING SWEET FEELING FOR ALL SCHOLARS.
5. No Duplication
After completion of your work, it does not available in our library i.e. we erased after completion of your PhD work so we avoid of giving duplicate contents for scholars. This step makes our experts to bringing new ideas, applications, methodologies and algorithms. Our work is more standard, quality and universal. Everything we make it as a new for all scholars. INNOVATION IS THE ABILITY TO SEE THE ORIGINALITY. EXPLORATION IS OUR ENGINE THAT DRIVES INNOVATION SO LET’S ALL GO EXPLORING.
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