## 3 Data Used and Methodology
### Random Forests and Performance Analysis  
The study of drought forecasting has seen the use of various ML-based models; however, the present study focusses only on Random Forest (RF), as its performance is similar to other supervised learning models, such as support vector machine or boosted regression trees [9; 47]. However, the RF-model has been comparatively less tested, especially in Australia. Previous studies have used remote-sensing-based data to forecast drought, using neural networks and RF [48] for wheat belts in the region. The present study forecasts the drought index, using a climatological dataset for the entire NSW region, and is the first study of its kind. The RF model was proposed by [PERSON] [49], an ensemble technique which reduces over-fitting and reduces the uncertainty, proving to be much better than single-tree-based techniques. The other benefit of using an RF model is its ability to handle large datasets; it is also highly interpretable, especially involving multiple features. It provides a reliable global estimate of variable importance and also determines the marginal effect of a predictor on response variable [50]. The model initiates by initially building a forest of decision trees utilizing the bootstrap technique, wherein every tree is created independently, based on a randomized subset of predictor variables [51]. The trees grow to a maximum size, without pruning, and thereby the output mean from all the multiple decision trees is the final result [52], achieving effective regression performance [27]. We used the scikit learn library to carry out the RF model [53]. There are two key things while running the model: first, understanding the right set of hyper parameters (number of decision trees and number of features under each tree), which would tune the parameters and evaluate the model for every combination. Although there are several parameter-tuning techniques, we have focused on the use of two most popular techniques, random search and grid search. As the name suggests, the random search technique uses random combinations of hyper parameters to find the \begin{table}
\begin{tabular}{c c} \hline
**SPEI Values** & **Drought Classes** \\ \hline \(\leq\)–2.0 & Extremely Dry \\ \(-\)1.99 to \(-\)1.5 & Severely Dry \\ \(-\)1.49 to \(-\)1.0 & Moderately Dry \\ \(-\)0.99 to 0.99 & Near Normal \\ 1.0 to 1.49 & Moderately Wet \\ 1.5 to 1.99 & Severely Wet \\ \(\geq\)2.0 & Extremely Wet \\ \hline \end{tabular}
\end{table} 
Table 1: Drought classification according to Standard Precipitation Evaporation Index (SPEI) index values. Figure 5: SPEI variation for two major droughts, (**a**) 1937–1945 (World War II Drought) and (**b**) 2001–2010 (Millennium Drought). optimal solution, whereas the grid search technique utilizes a grid approach through every combination of the hyper parameters. We tested both the approaches and found grid search to provide better results. The concerns regarding over-fitting were addressed by using a cross-validation approach under both the techniques. Thereafter, analyzing the relative importance of the variables and finding out the key variables affecting the regression model is the key to develop a reliable and interpretable model. The default feature importance in scikit-learn has a tendency to depict high importance of continuous features, and it sometimes can be biased. Therefore, to counter this, we used a drop-column feature importance technique, using rfpimp Python package ([[https://pypi.org/project/rfpimp/](https://pypi.org/project/rfpimp/)]([https://pypi.org/project/rfpimp/](https://pypi.org/project/rfpimp/))), which is based on the permutation importance strategy [54]. Although this package is resource-intensive, it proved to be most accurate feature importance. The performance of the RF model was carried out by using Coefficient of Determination (\(R^{2}\)) and Root-Mean-Square-Error (\(RMSE\)) method.