Modelling and Forecasting Drought events over Punjab using Machine Learning and Time Series Approaches

Abstract

Droughts are continuous periods of precipitation that are much below average can cause water shortages, decreased soil moisture, and a number of negative impact on ecosystems, agriculture, humans and wildlife. RDI are typically used for monthly and annually data processing to simultaneously calculate precipitation and potential evap otranspiration on an appropriate time scale. Study improves the methods for choosing predictors and create an innovative model to forecast drought in Punjab, Pakistan. This study analyzed precipitation and temperature data from 9 meterological site us ing RDI. This study aim is to compare four well-known models: Autoregressive Inte grating Moving Average (ARIMA), Support Vector regression (SVR), Neural Network Auto Regressive (NNAR) and Dynamic Linear Model (DLM) in order to assess and compare the effectiveness of various forecasting model in predicting RDI. The training (January 1961 to November 2000) to evaluate its performance and for testing data (December 2000 to December 2016) to estimate their performance. ME, RMSE, MPE and MAPE for performance measure. And for model selection we use AIC and BIC. The selection of best model is based on minimum value of RMSE, ME, MPE, MAPE, AIC and BIC. The evaluation results suggest that NNAR outperforms the other three models. The study found that the NNAR model demonstrated the highest accuracy and prediction for RDI forecasting. The NNAR model performed better than other models capturing RDI changes with unique accuracy. These results demonstrate the effectiveness of the NNAR model in predicting drought conditions and to evaluate the model selection for RDI prediction. These finding help improve planning and manage ment efforts for droughts, enabling efficient use of resources and decision-making. The results of this study provide proof for the decision, particularly droughts, as well as for creating drought mitigation efforts and applying plan into action to lessen the impact of drought in Punjab.