Partial least squares regression based variables selection for water level predictions

Floods are common phenomenon in the state of Kuala Krai, specifically in Kelantan-Malaysia. Every year , floods affecting biodiversity on this region and al so causing property loss of this residential area. The residents in Kelantan always suffered from floods s ince the water overflows to the areas adjoining to the rivers, lakes or dams. Months, average monthly rain fall, temperature, relative humidity and surface wi nd were used as predictors while the water level of Ga las River was used as response. The selection of su itable predictor variables becomes an important issue for developing prediction model since the analysis data uses many variables from meteorological and hydrogical departments. In this study, we conduct K-fold CrossValidation (CV) to select the important variables f or the water level predictions. A suitable predicti on model is needed to forecast the water level in Gala s River by adopting the Ordinary Linear Regression (OLR) and Partial Least Squares Regression (PLSR). However, we need to perform pre-processing data of the datasets since the original data contain missin g data. We perform two types of pre-processing data which are using mean of the corresponding months (t ype I pre-processing data) and OLR (type II preprocessing data) of missing data. Based on the experiment, PLSR is more suitable model rather than OLR for predicting the water level in Galas River and t he use of the type I pre-processing data gives high er accuracy than the type II pre-processing data.