Python
Aim:
The goal of this project is to create a reliable model for predicting droughts in regions that are vulnerable to them. Using Indian rainfall data, the project applies ARIMA and SARIMAX models to forecast droughts. The project aims to support better planning and response strategies, helping communities prepare for and mitigate the effects of droughts.
Abstract:
Droughts can lead to severe impacts, including crop failures, water shortages, and food insecurity, especially in India, which receives 450-600 mm of annual rainfall. This project focuses on developing a reliable drought forecasting system using time series models. The Standardized Precipitation Index (SPI) is used as an indicator for forecasting drought episodes. We employ ARIMA and SARIMAX models on the Indian rainfall dataset to predict drought conditions at a 1-step lead time.
The models are trained using historical rainfall data and are evaluated based on model performance and error metrics such as Root Mean Square Error (RMSE) and Nash-Sutcliffe Efficiency (NSE). Results show that the models are capable of accurately predicting droughts, This approach can help improve drought preparedness and support effective drought management strategies in vulnerable regions
Existing System:
Droughts, due to their severity and variability, can cause significant impacts such as crop failures, water shortages, and food insecurity. Accurate and timely forecasting is crucial to mitigating the effects of extreme weather events like droughts, particularly in drought-prone areas such as Chitradurga, India, which typically receives 450-600 mm of annual rainfall. This study focuses on 1-step lead time forecasting of meteorological drought episodes using the 6-month Standardized Precipitation Index (SPI-6) as an indicator.
Rainfall data with a fine spatial resolution, obtained from the Indian Meteorological Department, was used to derive SPI-6 values across 23 grid stations. Mutual Information was employed to select the most relevant input features for drought prediction. Several advanced machine learning models, including Artificial Neural Networks (ANN), Multivariate Adaptive Regression Splines (MARS), CatBoost Regression, and Gradient Tree Boosting, were applied to forecast drought conditions
Proposed System:
Droughts pose significant challenges to agriculture and water resources, necessitating effective forecasting methods for early intervention and management. This project aims to develop a reliable forecasting system for meteorological drought episodes in India, using advanced time series models. By analyzing the Indian rainfall dataset, the project employs Auto-Regressive Integrated Moving Average (ARIMA) and Seasonal Auto-Regressive Integrated Moving-Average with Exogenous factors (SARIMAX) models to predict drought conditions based on the Standardized Precipitation Index (SPI).
The historical rainfall data is utilized to derive SPI values, and the models are trained to predict drought episodes with a 1-step lead time. The performance of the models is evaluated using various accuracy and error metrics, including Root Mean Square Error (RMSE) and Nash-Sutcliffe Efficiency (NSE). Initial results indicate that both ARIMA and SARIMAX models provide accurate forecasts, with SARIMAX demonstrating enhanced performance by effectively capturing seasonality and external influences.
The outcomes of this research contribute to improved drought preparedness and management strategies, enabling stakeholders to make informed decisions and mitigate the adverse effects of droughts in vulnerable regions.
Advantages:
- Seasonality Handling: SARIMAX can account for seasonality making it more robust in forecasting droughts that are influenced by seasonal weather variations.
- Flexibility for Different Regions: The models can be easily adapted to forecast drought conditions for different regions by incorporating localized rainfall and weather data.
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