## 5 Conclusions  
The present study was conducted with the aim of forecasting droughts and understanding the climatic variables affecting it for the NSW region of Australia. The work involved determination of the SPEI index and forecasting the index, using a machine learning approach, namely, Random Forests to one- and three-month lead times. The index was calculated by using rainfall and PET values collected from CRU dataset from 1901 to 2018, and the climatic variables included rainfall, PET, vapor pressure, cloud cover, mean, maximum and minimum temperature, also gathered from CRU. The understanding of the forecasting ability of RF was analyzed by dividing the input data into three: first used for training (1901-2010), then testing (2011-2015) and finally validation (2016-2018). For the testing period, mean SPEI values of all the observed and forecasted pixels were analyzed, using R\({}^{2}\) and RMSE statistical metric. For the validation period, the aim was to understand the classification accuracy of the model as per the drought classes, which were analyzed by using ROC-based AUC curves. The results from the study could be used for other drought-based applications, like urban heat, agriculture and fire emergency preparedness. The conclusions from the study are as follows:  
* The relative importance of the hydro-meteorological variables used to forecast drought index shows that, apart from rainfall, PET is the most significant factor, followed by vapor pressure and mean temperature.
* The model shows good forecasting capability, with R\({}^{2}\) value being 0.73 and 0.76, respectively, for SPEI 1 and SPEI 3 scenarios. However, when analyzing the variation as per drought classes, SPEI 3 depicted a greater number of similar classes in accordance to SPEI 1, thus providing slightly better predicative capability of the model for the former case.
* The classification aspect of the model into different drought classes was analyzed during the validation period. The results show that the model was able to correctly classify 82% and 84% for SPEI 1 and SPEI 3 time periods, respectively.  
The present study shows that the use of the Random Forest model has the ability to perform well for both regression and classification problems concerning drought at short-term time scales for the NSW region. However, a future study should test out other models, either single or hybrid, for both aspects at longer time scales and understanding the variation for different drought characteristics.  
Conceptualization, A.D. and B.P.; methodology and formal analysis, A.D.; data curation, A.D.; writing--original draft preparation, A.D.; writing--review and editing, B.P.; supervision, B.P.; funding--B.P. and A.M.A. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, in the University of Technology Sydney (UTS). This research was also supported by Researchers Supporting Project number RSP-2020/14, King Saud University, Riyadh, Saudi Arabia.  
The authors are thankful to the reviewers for reviewing and suggesting valuable modifications, which has helped in improving the quality of this paper.  
The authors declare no conflict of interest.