Regression based Prediction and Analysis of Rainfall Data for Heavy Rainfall and Drought of Sikta Weather Station-Banke District Nepal

Technical Summary: Regression-based Prediction and Analysis of Rainfall Data for Heavy Rainfall and Drought of Sikta Weather Station, Banke District, Nepal

Problem Statement
Rural development in Nepal is heavily dependent on agriculture, with over 80% of the population residing in rural areas. However, agricultural planning and water resource management are severely challenged by unpredictable weather patterns, specifically droughts and heavy rainfall events. The Banke District, located in the Terai region, faces increasing climate variability, where rainfall patterns have become erratic in both seasonality and volume. While previous studies have explored links between meteorological and hydrological droughts, there is a critical need for region-specific, data-driven strategies to monitor and adapt to these conditions. The primary objective of this study is to address the difficulty of predicting rainfall in the Sikta Weather Station area of Banke District, aiming to identify a forecasting method capable of achieving at least 60% accuracy, as predictions below this threshold could yield detrimental effects for planning.

Methodology
The research utilizes a comprehensive data analytics approach centered on the Sikta Weather Station dataset, covering monthly and annual rainfall precipitation from 1993 to 2023. The dataset includes seasonal breakdowns (JF, MAM, JJAS, OND) and is analyzed using Python.

The core methodology involves a comparative evaluation of five regression-based machine learning models to determine the optimal approach for forecasting rainfall and drought conditions:

1. Linear Regression: Models the relationship between dependent and independent variables by minimizing Mean Squared Error (MSE).
2. Lasso Regression (L1 Regularization): Incorporates a penalty term to shrink coefficients, effectively performing feature selection by reducing some coefficients to zero.
3. Ridge Regression (L2 Regularization): Introduces a penalty to stabilize estimates in the presence of multicollinearity, preventing high variance in predictions.
4. Elastic Net Regression: Combines L1 and L2 regularization to handle correlated meteorological predictors while performing feature selection.
5. XGBoost (Extreme Gradient Boosting): An ensemble learning method that combines multiple weak decision trees to create a strong predictive model, noted for its speed and efficiency with large datasets.

The workflow includes data cleaning, Exploratory Data Analysis (EDA) to identify patterns, deseasonalization of rainfall data, and the application of both classification and regression techniques. Model performance is rigorously evaluated using the Correlation Coefficient (R), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE).

Key Results
The study compared the performance of the five models against historical rainfall data. The analysis revealed that while all models provided varying degrees of insight, the XGBoost regression model demonstrated the highest accuracy in predicting rainfall patterns and potential drought conditions.

Visual and statistical evaluations, including confusion matrices and error metrics (MAE and RMSE), confirmed XGBoost's superiority over Linear, Lasso, Ridge, and Elastic Net models for this specific dataset. The model successfully captured the non-linear interactions inherent in the rainfall data, particularly for the JJAS (June–September) monsoon season, which exhibits the highest average precipitation.

Significance and Claims
The paper claims that the proposed weather prediction strategy, utilizing the XGBoost regression model, offers a significant enhancement for agricultural planning and water resource management in rural Nepal. By accurately forecasting heavy rainfall and drought conditions, the study suggests that local stakeholders can better prepare for climate-induced risks.

Furthermore, the analysis highlights a practical application for parametric insurance. The study posits that the identified trigger points and rainfall thresholds can enable farmers in Banke to enroll in low-cost parametric insurance schemes (potentially under 50 cents). Under this framework, farmers would receive claims automatically when specific rainfall conditions (drought or flood thresholds) are met, thereby mitigating economic losses without the need for complex loss assessments.

Ultimately, the research concludes that deploying such predictive data analytics is crucial for supporting sustainable rural development in Nepal, transforming historical weather data into actionable insights for climate adaptation.