Evaluation of a novel hybrid lion swarm optimization – AdaBoostRegressor model for forecasting monthly precipitation

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Precipitation is essential for crop production, water resource management, and other activities. Since precipitation has unpredictable sequential and seasonal characteristics, it is extremely difficult to forecast precipitation. Hence, a better forecasting model will lead to an early warning that minimizes the risk to life and property. Many approaches, such as statistical, Machine Learning (ML), Deep Learning (DL), and hybrid algorithms, are used to build precipitation forecasting systems. Feature selection is especially important for selecting past values (lags) and to eliminate irrelevant and/or redundant features. The optimization algorithm helps identify correct time series lags because it does not make data assumptions. In this work, a hybrid long-term (1-month ahead) precipitation forecast model (ABR-LSO) is proposed by integrating the AdaBoostRegressor (ABR) with the Lion Swarm Optimization (LSO) algorithm. Time series precipitation data for the period 1989–2019 for Idukki, Kerala, India. The ABR model considered 12 antecedent rainfall records as its input features and predicted the rainfall data for the next time step (t + 1), whereas the ABR-LSO hybrid model optimized the input features with LSO. The efficacy of the forecast models is evaluated using statistical indices like mean absolute error (MAE) and root mean square error (RMSE). It has been noted that in models 1, 2, and 3, the LSO-ABR model produced good MAE above ABR model improvements of 5.08 %, 4 %, and 6.12 %, respectively. Precipitation forecasts will help the administrators devise disaster prevention mechanisms against flooding in the downstream areas.