## Appendix A
### ANN Model Performance by Lag Time  
The study proceeded, in the test of assumptions, to run the ANN process on the entire set of models in the ANN process. A summary of the results is provided following on the same set as the GAM models. In training, as measured by the performance in the 30% (validation) dataset portion of the training data, the performance of the ANN models is as shown in Figure A2. From Figure A2, it is shown that for each of the models, predictions 1-month ahead outperform those for 2-month and 3-month ahead except for the last cases three cases (models 32-34) when predictions 2-month ahead are better. At no point does any model have its predictions 3-month ahead out-perform any of the short time period predictions. A summary of the descriptive statistics of the ANN models is provided in Table A2. From Table A2, is noted that predictions 1-month ahead post the highest range but still end up recording the highest mean of the lagged predictions. At an average \(R^{2}\) of 0.6 for all the 102 models the predictions 1-month ahead are judged predictive enough for use in an operational ex-ante system. The best model for prediction 1-month ahead differs from the best model for 2-month and 3-month ahead predictions. Both models have the variable VCIDekad while RFE for the predictions 1-month ahead and SPI1M for both predictions 2-months and 3-months ahead. \begin{table} \begin{tabular}{c c c c c c c} \hline \hline
**No** & **Model** & \begin{tabular}{c} **R\({}^{2}\)** \\ **Training** \\ \end{tabular} & \
\begin{tabular}{c} **R\({}^{2}\)** \\ **Validation** \\ \end{tabular} &
\begin{tabular}{c} **Overfit** \\ **Index** \\ \end{tabular} & **Overfit** & **Lag Time** \\ \hline
51 & NDVIDekad\_lag2+SPI1M\_lag2 & 0.4 & 0.37 & 0.03 & No & Lag2 \\
52 & VCIDekad\_lag3+SPI1M\_lag3 & 0.35 & 0.33 & 0.01 & No & Lag3 \\
53 & VCIIM\_lag3+SPI1M\_lag3 & 0.34 & 0.33 & 0.01 & No & Lag3 \\
54 & NDVIDekad\_lag2+RFESM\_lag2 & 0.35 & 0.33 & 0.02 & No & Lag2 \\
55 & VCI3M\_lag3+SPI1M\_lag3 & 0.33 & 0.32 & 0.01 & No & Lag3 \\
56 & VCI3M\_lag2 & 0.33 & 0.31 & 0.02 & No & Lag2 \\
57 & RFE3M\_lag1 & 0.32 & 0.31 & 0.01 & No & Lag1 \\
58 & NDVIDekad\_lag3+SPI3M\_lag3 & 0.33 & 0.31 & 0.02 & No & Lag3 \\
59* & NDVIDekad\_lag2+RCIIM\_lag2 & 0.35 & 0.31 & 0.05 & Yes & Lag2 \\  
DVIDekad\_lag1+RFE1M\_lag1 & 0.26 & 0.23 & 0.02 & No & Lag1 \\
84 & SPI1M\_lag3 & 0.25 & 0.23 & 0.02 & No & Lag3 \\
85 & VCI3M\_lag3+RFE3M\_lag3 & 0.24 & 0.23 & 0.02 & No & Lag3 \\
86 & NDVIDekad\_lag3+RCII3M\_lag3 & 0.24 & 0.22 & 0.02 & No & Lag3 \\
87* & RCI1M\_lag2 & 0.25 & 0.21 & 0.04 & Yes & Lag2 \\
88 & RFE1M\_lag2 & 0.24 & 0.21 & 0.03 & No & Lag2 \\
89 & NDVIDekad\_lag3+RFE1M\_lag3 & 0.24 & 0.21 & 0.03 & No & Lag3 \\
90 & NDVIDekad\_lag3+RFE3M\_lag3 & 0.23 & 0.2 & 0.02 & No & Lag3 \\
91 & RFE3M\_lag3 & 0.21 & 0.19 & 0.02 & No & Lag3 \\
92 & NDVIDekad\_lag1 & 0.22 & 0.19 & 0.03 & No & Lag1 \\
93 & VCI1M\_lag3 & 0.19 & 0.18 & 0.01 & No & Lag3 \\
94 & VCIDekad\_lag3 & 0.19 & 0.18 & 0.01 & No & Lag3 \\
95 & RCIIM\_lag3 & 0.19 & 0.17 & 0.02 & No & Lag3 \\
96 & RFE1M\_lag3 & 0.2 & 0.17 & 0.03 & No & Lag3 \\
97 & SPI1M\_lag1 & 0.17 & 0.15 & 0.03 & No & Lag1 \\
98 & VCI3M\_lag3 & 0.15 & 0.13 & 0.02 & No & Lag3 \\
99 & NDVIDekad\_lag2 & 0.16 & 0.13 & 0.03 & No & Lag2 \\
100 & RCI1M\_lag1 & 0.13 & 0.12 & 0.01 & No & Lag1 \\
101 & RFE1M\_lag1 & 0.11 & 0.09 & 0.02 & No & Lag1 \\
102 & NDVIDekad\_lag3 & 0.11 & 0.09 & 0.02 & No & Lag3 \\ \hline \hline \end{tabular}   
* The overfit models are marked with \({}^{*}\) on the column No. The GAM models only have two out of 102 (under 2%) of models judged as overfit. This is as compared to the ANN models in Table 14, which indicates 64 out of 102 models losing performance in validation by more than an \(R^{2}\) of 0.03 as compared to their performance in training.