## 4 Discussion
### Advantages of Using Modelling Platforms
#### 4.3.3 Potential for Machine Learning Modelling  
As noted earlier, _DIMONA_ has a machine learning model capability for studies where a process-based explanatory component is not in the prime objective, and especially where a reliable predictive power is needed based on meaningful physical, biological, or ecological predictors. Also, ML simulations may apply as an independent method to confirm the process-based modelling outcomes and the significance of the model drivers. Model users have two options for executing ML simulations via the _DIMONA_ platform. One option is via the _DIMONA_ ML model, coded in C++ as part of the _DIMONA_ platform, for simple and multiple ML linear regressions by applying the method of gradient descend. The other option is via the TensorFlow framework installed as an open-source library, together with Python 3.9, on the Apache server hosting the _DIMONA_ platform. Both options are facilitated through the _DIMONA_ GUI, which makes them accessible to researchers with limited ML experience (Figure 4). To demonstrate the potential of the _DIMONA_ platform for ML research, the _DIMONA_ ML model was trained for hourly GPP modelling on the hourly records of \(\rm I_{\rm swr}\), \(\rm T_{\rm air}\), RH, \(\rm v_{\rm w}\), and \(\rm d_{\rm WT}\) during 1999-2001 and then tested against 2002-2004 data. The statistics of linear regressions of simulated on observed ecosystem GPP, RMSE, and Willmott's d values are summarized for the 2002-2004 period and for each individual year for the _DIMONA_ ML model (Table 6). To illustrate the performance of the ML model, model outcomes for three 2-week periods, i.e., DOY 214-227 (2 to 15 August) in 2002 and 2003 and DOY 235-248 (23 August to 5 September) in 2004, were compared vs. available observations (Figure 27a-c). The outcomes for 2002 showed comparable \(\rm R^{2}\), RMSE, and d values (Table 6) to both PB models (Tables 3 and 4) and a reasonable fit to observations (Figure 27a), with the 2002 data as a testing set alone constituting \(\sim\)25% of the data for the 1999-2002 period. However, the statistics and outcomes for 2003, and especially 2004 (Table 6, Figure 27b,c), showed a deteriorating pattern, as the testing set of 2003, and especially 2004, become more distant from the training set. This pattern was also visible with decreasing slopes, \(\rm R^{2}\) and d values, and large intercepts and RMSE (Table 6), when compared to both PB models in 2003 and 2004 (Tables 3 and 4), Thus, both PB models outperformed the ML model over the entire period of comparison, i.e., 2000-2004 (Tables 3 and 4) and 2002-2004 (Table 6). However, this behavior of the ML model might suggest that using larger data sequences consisting of more years, especially for training purposes, and enhancing the ML experiment with cross-validation for time series could improve the model's performance [67; 68]. As ML modelling is not in the focus of this study, no further details are considered here. Instead, this demonstration is made simply to highlight the potential uses of the _DIMONA_ modellingplatform, as well as to demonstrate ML modelling as an alternative and/or complementary predictive method to PB modelling. While ML frameworks like TensorFlow [69] are now largely available on the Internet, running simulations on _DIMONA_ may not only facilitate the process for less experienced users but may also make ML simulations directly comparable to and benefitting from concurrent PB simulations conducted simultaneously on the same platform. Figure 23: The meteorological inputs for both locations (to the **left**), soil hydrology parameterization with daily near-surface \(\theta\) at the upper transect location (in the **middle**), and soil hydrology parameterization with daily near-surface \(\theta\) at the lower transect location (to the **right**). Figure 24: The average real-time IoT-simulated vascular, bryophyte, and ecosystem photosynthesis rates in umol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\) at the **upper (left)** and **lower (right)** transect locations.