Statistical Approach to Estimate the Weather Prediction
In one sentence. Compares curve-fitting and soft computing models to predict India's annual average temperature; ML is only loosely central and the study is weak.
Abstract
The burgeoning research in the fields of statistical has given rise to numerous weather prediction models. But the problem of accurately predicting or forecasting the weather still persists. Numerical weather prediction is taking the existing numerical data on weather conditions and applying machine learning algorithms on it to forecast the weather. If the assessment of weather cannot be selected properly, the prediction information becomes incorrect and this will create lots of the problem in the real life. Therefore selection of an appropriate technique for the purpose of forecasting is needed. Here the effort is to select the soft computing technique properly for the prediction of the futuristic annual average Temperature of India. Initially, the concepts of least square base linear equation, exponential, asymptotic , curvilinear , logarithm have been applied to optimize the error and the concept of soft computing model will be used for the further enhance of the error. Moreover, the same concept has been applied on the tested data to select the optimality of the model predicted model.
Labels
- Topic
- Sub-seasonal to seasonal
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- Seasonal to annual
- Domain
- Regional
- Data
- Station / in-situ
- Status
- Research
- Identifiers
- OpenAlex:W7217151054
- Added
- 2026-10-22
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