Hassan Davani, PhD, PE
Assistant Professor, Water Resources Engineering, San Diego State University
Department of Civil, Construction & Environmental Engineering
We use linear mathematical machine learning cores, such as Multiple Linear Regression (MLR), Ridge Regression (RR), Multivariate Adaptive Regression Splines (MARS) and the Model Tree (MT), or non-linear cores, such as cubic-order MARS, k-Nearest Neighbor (kNN) and Genetic Algorithm-optimized Support Vector Machine (GA-SVM) - depending on the problem structure. All these methods provide data partitioning for solving water resources problems.
For example, we have used machine learning to forecast future rainfall, and then have compared the results with a conventional model. Comparison indicates that our machine could significantly improve forecasting efficiency in terms of reproducing standard deviation and skewness for both calibration and validation periods.
Below are the parameters and structure of our Model Tree for forecasting the rainfall in the month of January as an example. x1 to x5 are different meteorological variables.