Speaker
Description
Weather forecasting plays a crucial role in daily life, impacting various aspects such as agriculture, disaster readiness, and environmental conservation. Accurate and location-specific weather forecasts for a longer period are particularly vital for smaller, geographically specific regions where some specific crops are grown and animals are reared. Though several websites are available for weather forecasting, they forecast for shorter period. This study aimed to identify time series patterns and select the best-fitted model for long-term forecasting in Kamburupitiya area. The ARIMA time series analysis method was employed to forecast weather factors, and time series plots were utilized to identify patterns such as seasonal and non-seasonal variations in weather factors. Weather data (including rainfall, sunshine hours, maximum temperature, and minimum temperature) from 2009 to 2022 were used to develop the models, while data from 2023 were employed to verify prediction precision. The model development comprised four sequential steps: identification, estimation, diagnostic checking, and forecasting. The model validity was assessed using standard statistical techniques with a test dataset. In the second stage of validation, forecasted values for monthly rainfall, sunshine hours, as well as maximum and minimum temperatures were compared with actual data series. After completing necessary analysis and observing forecasts, SARIMA (2,0,0) (1,0,1), SARIMA (1,1,1) (1,0,1), SARIMA (0,1,1) (1,1,0), and SARIMA (0,1,1) (0,1,1) were fitted for maximum temperature, minimum temperature, rainfall, and sunshine hours, respectively. These models were identified as the most effective ARIMA forecasting models for long-term prediction. This long-term prediction is anticipated to support local authorities, farmers, and residents in making informed decisions and taking proactive measures to adapt to and mitigate the effects of these weather patterns.
| Type of presentation | Poster |
|---|---|
| Classification | Both methodology and application |
| Keywords | ARIMA model, SARIMA models, Weather forecasting |