# A Review of Machine Learning Applications in Land Surface Modeling
## 5 Techniques of Machine Learning
### Traditional ML Methods  
Common traditional ML methods include SVM, CART, bootstrap aggregating (Bagging), Kernel ridge regression (KRR), RF, ANN etc. [PERSON] et al. [42] used a feedforward ANN as a supervised learning approach to retrieve monthly estimates of GPP, LE, and SH at a global scale using remotely sensed SIF estimates besides other meteor \begin{table}
\begin{tabular}{c c c} \hline \hline
**ML Algorithm** & **Category** & **Purpose** & **Reference** \\ \hline Feedforward ANN & & Provide monthly estimates of GPP, LH and SH at a global scale & [42] \\ \hline ELM and GRNN & & Obtain ET\({}_{\text{o}}\) from temperature data in southwest China & [44] \\ \hline RT, Bagging, RF and SVM & & Provide ET estimates in central Florida & [46] \\ \hline SVM and GANN & & Provide crop ET estimates in China & [47] \\ \hline RF, RT, KRR, RS, ANN & & Provide CO\({}_{2}\), LH, SH, R\({}_{\text{n}}\) at multiple sites globally & [48] \\ \hline Extra-Trees & & Obtain global parameter estimates for Noah land model & [52] \\ \hline MCMC & & Parameter optimization of land model & [54] \\ \hline RF & & Improving regional crop yield prediction & [56, 57, 59, 60] \\ \hline GBRT & & Constrain uncertainty in GPP estimates & [58] \\ \hline DL-ANN & & Obtain LH estimates over ocean & [43] \\ \hline DL-ANN & & Constructing physics-constrained ML model & [49] \\ \hline DL-ANN & & Constructing emulators for land modeling & [55] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Widely used ML algorithms used in land surface modeling. logical predictors like precipitation, temperature, soil moisture, snow cover, and R\({}_{\text{n}}\). The ANN consisted of three layers: (1) an input layer connected to input data, (2) one hidden layer, and (3) an output layer producing LE, SH, and GPP outputs. A number of neurons in the hidden layer were chosen based on the complexity of the problem. [44] compared the performance of an extreme learning machine (ELM) technique and generalized regression neural network (GRNN) model in predicting reference evapotranspiration (ET\({}_{\text{o}}\)). Their ELM consisted of 50 hidden nodes, which performed better than the GRNN, which is similar to feedforward neural networks but based on nonlinear regression theory of function estimation. More specifically, GRNN contains four layers named summation layer, input layer, pattern layer, and output layer [68]. They were able to reproduce more accurate ET\({}_{\text{o}}\) estimates than the conventional empirical Hargreaves model. A similar study was conducted by [PERSON] et al. [45] over two weather stations in Serbia. [46] compared the supervised learning approaches Regression Tree (RT), Bagging, RF, and SVM while predicting actual ET. An RT model utilizes a decision tree as a predictive model, and target variables are real values. RTs can be further advanced by gradient boosting (GBRT) [58, 59]. 'Bagging' is a machine learning ensemble technique. RF is a set of uncorrelated simple regression trees. In contrast, SVMs are supervised learning models to deal with classification and regression problems [69]. Some studies have used genetic algorithms to optimize the ANN (GANN) [47] to predict crop-specific ET estimates. Apart from these commonly used techniques, KRR [70] and regression splines (RS) [71] are also used by few studies to estimate ET [48]. Global model parameter estimation in [52] benefited from Extra-Trees [72]. MCMC has also been used with ML (Gaussian Process Regression) for parameter optimization [54]. RF is the most commonly used technique for crop yield estimate [56, 57]. To understand the relative importance of predictors, which may provide information about the usefulness of them at predicting the target variable, 'feature importance' is normally computed internally by these methods. Overall, ANN and RF are the most widely used traditional ML methods in LSM applications. However, RTs with improved 'gradient boosting' of 'Bagging' can be extremely powerful to create data-driven models for land-surface applications.