If your goal is to build a predictive model, restricted randomization should not be a concern, no matter which modeling technique you choose to use. You can develop a predictive model using your data as it stands. SVEM is an excellent technique for developing your predictive model.
Split-plot designs are used when certain factors are difficult to change while other factors are easy to change. The typical examples come from agriculture, where you can only apply some factors (called whole-plot factors), such as irrigation or fertilizer, to large areas. But within those areas, you can vary other factors (called split-plot factors), such as seed variety. The fact that randomization is restricted by the whole plots affects the independence of measurements, and this impacts hypothesis tests.
If your goal is prediction, these hypothesis tests are not of interest to you. In the above example for agriculture, to predict which seed variety provides the best yield under various conditions, you can simply construct a predictive model based on your data. It is true that you might observe more variation in your whole plot factors, if you were to include more plots. However, you can model the data that you have, and still develop a useful prediction model.