Comparative study of three stochastic future weather forecast approaches: a case study
In one sentence. Compares KNN, SWAT and RCP weather generators for station temperature, precipitation and wind; KNN is more robust to noisy data.
Abstract
Weather forecasting is an essential component of different hydrological studies. This article compares the weather prediction performance of various machine learning models like k-nearest neighbours (KNN), Soil and Water Assessment Tools (SWAT), and Representative Concentration Pathway (RCP). KNN is more resistant to noisy data set and provides more reliable performance than RCP and SWAT models. We simulate temperature, precipitation, and wind speed using KNN, SWAT and RCP weather generators, and we compare the results with observed data. The analyses compare WP-KNN with state-of-the-art classification and prediction models. We also suggest a systematic forecasting methodology that uses an updated version of the KNN classification. Our extensive experimental modelling findings show that the proposed technique is much more effective in a noisy dataset.
Labels
- Topic
- Local & station forecasting
- Type
- Evaluation or intercomparison of existing models
- Method
- Classical ML (trees, GP, linear, kernels)
- Domain
- Local / station
- Data
- Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.1016/j.dsm.2021.07.002 OpenAlex:W3186512974
- Added
- 2026-10-22
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