Modelling an automated rainfall forecasting system using an optimized intelligent agent
In one sentence. An ANN trained on Nigerian station data reaches over 90% rainfall prediction accuracy, versus 70% for multiple linear regression.
Abstract
Weather forecasting information is very crucial in decision making process regarding to activities and works, such as in the field of agriculture to determine initial growing season. Recently, climate change causes trouble in weather forecasting. Time series data analysis for forecasting, is one of the most important aspects of the practical usage. Time Series data is large in volume, highly dimensional and continuous updating. Accurate rainfall forecasting with the help of time series data analysis had helped in the field of agriculture, in evaluating drought and flooding situations in advance. The data used within this paper is taken from Automatic Digital Meteorological Station (NECOP Station) in National Space Research and Development Agency (NASRDA-Centre for Basic Space Science, Nsukka, Enugu State Nigeria). Those data include ambient temperature, air pressure, solar radiation, relative humidity, and wind speed etc. In this paper, rainfall forecasting models were developed for Artificial Neural Network (ANN) based on Levenberg-Marquardt training function and Multiple linear regression (MLR) and was used for the prediction. Based on experimental result, it was concluded that prediction using ANN model for NASRDA-CBSS weather data produced prediction with more than 90% accuracy while MLR prediction gives 70%.
Labels
- Topic
- Local & station forecasting
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- 6 h–3 days
- Domain
- Local / station
- Data
- Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.30574/gjeta.2023.15.1.0077 OpenAlex:W4366994326
- Added
- 2026-10-22
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