A novel weather parameters prediction scheme and their effects on crops
In one sentence. Fitness-tuned adaptive neural network forecasts Karnataka rainfall and temperature for 2021-2030 and maps effects on five crops; validation is limited.
Abstract
Weather forecast is significantly imperative in today’s smart technological world. A precise forecast model entails a plentiful data in order to attain the most accurate predictions. However, a forecast of future rainfall from historical data samples has always been challenging and key area of research. Hence, in modern weather forecasting a combo of computer models, observation, and knowledge of trends and patterns are introduced. This research work has presented a fitness function based adaptive artificial neural network scheme in order to forecast rainfall and temperature for upcoming decade (2021-2030) using historical weather data of 20 different districts of Karnataka state. Furthermore, effects of these forecasted weather parameters are realized over five major crops of Karnataka namely rice, wheat, jowar, maize, and ragi with the intention of evaluation for efficient crop management in terms of the passing relevant messages to the farmers and alternate measures such as suggesting other geographical locations to grow the same crop or growing other suitable crops at same geographical location. A graphical user interface (GUI) application has been developed for the proposed work in order to ease out the flow of work.
Labels
- Topic
- Local & station forecasting, Applications & impacts
- Type
- Application study
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- Decadal
- Domain
- Regional
- Data
- Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.11591/ijece.v12i1.pp639-648 OpenAlex:W3185994196
- Added
- 2026-10-22
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