## 7 Conclusions and Future Work  
This study focuses on the proof-of-concept of applying XGBoost, a state-of-the-art ML package for application on tabular data, to hourly site temperature and dew point forecasts up to seven days ahead, showing test results for various sites located across Australia. The resultant ML framework, Multi-SiteBoost, comprises the following three significant aspects: (1) an optimized data pre-processing pipeline, with each data transformation technique proven effective based on parametric study; (2) the application of SHAP values for model explainability, and (3) the identification of individual unreliable predictions at the moment when the prediction is made, so that extra caution can be taken for time spots when predictions are unreliable and may be subject to higher error. The predictions generated by the proposed ML framework are evaluated on both RMSE and various customized metrics, and satisfactory prediction accuracy has been achieved on most of the metrics at various sites across Australia at various lead days. Across the selected sites, an overall reduction in RMSE of 11% for temperature and 12% for dew point is estimated on average, as compared to grid values of IMPROVER (which itself is already blended and post-processed from multiple raw NWP data sources). By improving the general forecast accuracy, improvements in other related metrics such as RMSE of daily minimum and maximum, and critical error rate, have been achieved as well--even if the ML models are not trained with those objective functions. However, model accuracy shows noticeable variance across sites, highlighting the necessity of local parameters and hyperparameter tuning instead of a global setup. This is enabled in the current framework, granting flexibility in case of customer interest in the future. Explaining the trained ML models with SHAP values and SHAP interactive values leads to additional insights and trustworthiness of the model. The explanation identified the different effects of each variable, which largely agree with both global explanation of feature importance and human experience. The identification of main 'linear' features also confirms the significance of proper data scaling prior to training and paves the way for the identification of some unreliable predictions. Figure 16: (**a**) Exemplar time series of XGBoost prediction, compared with IMPROVER grid value and site observations, unreliable data highlighted in red. Lead time: 0–24 h; site: Geraldton. Temperature. (**b**) Exemplar time series of XGBoost prediction, compared with IMPROVER grid value and site observations, unreliable data highlighted in red. Lead time: 0–24 h; site: Geraldton. Dew point temperature. To add additional information to the predictions and as a forecast-auditing procedure, pointwise reliability is evaluated with three different approaches. The unreliability due to out-of-bound feature values is deduced by the inherent property of the XGBoost algorithm, while unreliability identified by high-error cohorts and local unfit rule are statistical methods based on the model's accuracy on the training set. These approaches combined can identify around 5-10% of all predictions as unreliable, and these data have RMSE of up to 1.5 times higher than global RMSE on tested sites. The pointwise reliability provides a foresight of local prediction quality prior to the observation data becoming available. As an auditing process of the forecast, it adds additional information on confidence level to the deterministic forecast. Like the accuracy of the ML model, the effectiveness of these methods also varies with site locations. It should be noted that while the current study explores specific data ingets, the developed methodology has general application, as the ML training, validation and explanation in this paper do not rely on specific data properties. Similarly, it is also possible to generate forecasts for other variables such as wind speed and solar radiance with the same approach used in this study.