LAUSR

A novel approach for qualitative seasonal forecast of precipitation at a basin scale is presented as significant enhancement in seasonal forecast at regional and country scales in India. The process utilizes empirical and typically lagged relationships between target variables of interest, namely precipitation at the basin level and various large-scale climate predictors (LSCPs). A total of 14 LSCPs have been considered for the seasonal forecast of precipitation with lead times of 1, 2, and 3 months in the Kosi Basin, India. Random split training and testing were conducted on seven machine-learning (ML) models using a potential predictor dataset for model selection. The Logistic Regression (LR) model was adopted since it had the highest mean accuracy score compared to the remaining six ML models. The LR model has been optimized by testing it on all possible combinations of potential predictors using Leave-One-Out Cross-Validation (CV) scheme. The resulting Seasonal Prediction Model (SPM) provides the probability of each tercile categorized as Above Normal (AN), Normal (N), and Below Normal (BN). The model has been evaluated using various metrics.