A substantial amount of datasets stored for various applications are often high dimensional with redundant and irrelevant features. Processing and analyzing data under such circumstances is time consuming and makes it difficult to obtain efficient predictive models. There is a strong need to carry out analyses for high dimensional data in some lower dimensions, and one approach to achieve this is through feature selection. This paper presents a new relevancy-redundancy approach, called the maximum relevance-minimum multicollinearity (MRmMC) method, for feature selection and ranking, which can overcome some shortcomings of existing criteria. In the proposed method,

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The grassroots of the presence of missing precipitation data are due to the malfunction of instruments, error of recording and meteorological extremes. Consequently, an effective imputation algorithm is indeed much needed to provide a high quality complete time series in assessing the risk of occurrence of extreme precipitation tragedy. In order to overcome this issue, this study desired to investigate the effectiveness of various Q-components of the Bayesian Principal Component Analysis model associates with Variational Bayes algorithm (BPCAQ-VB) in missing daily precipitation data treatment, which the ideal number of Q-components is identified by using The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) algorithm. The effectiveness of BPCAQ-VB algorithm in missing daily precipitation data treatment is evaluated by using four distinct precipitation time series, including two monitoring stations located in inland and coastal regions of Kuantan district, respectively. The analysis results rendered the BPCA5-VB is superior in missing daily precipitation data treatment for the coastal region time series compared to the single imputation algorithms proposed in previous studies. Contrarily, the single imputation algorithm is superior in missing daily precipitation data treatment for an inland region time series rather than the BPCAQ-VB algorithm.

One of the primary constraints for development and management of water resources is the spatial and temporal uncertainty of rainfall. This is due to the stability and reliability of water supply is dynamically associated with the spatial and temporal uncertainty of rainfall. However, this spatial and temporal uncertainty can be assessed using the intensity entropy (IE) and apportionment entropy (AE). The main objective of this study is to investigate the implications of the use of Doane’s and Freedman-Diaconis’ binning rule in characterizing potential water resource availability (PWRA), which the PWRA is assessed via the standardized intensity entropy (IE’) against the standardized apportionment entropy (AE’) scatter diagram. To pursue the objective of this study, the daily rainfall data recorded ranging from January 2008 to December 2016 at four rainfall monitoring stations located Coastal region of Kuantan District Pahang are analyzed. The analysis results illustrated that the use of Doane’s binning rule is more appropriate than Freedman-Diaconis’ binning rule. This is due to the resulted PWRA characteristics using Doane’s binning rule is relatively consistent with practical climate such that the study region is experiencing poor-in-water zone with less amount and high uncertainty of rainfall during the Southwest Monsoon, while abundant and perennial rainfall during the Northeast Monsoon. Furthermore, the use of Doane’s binning rule is more advantages compared to the Freedman-Diaconis’ binning rule with the abstraction of computational cost and time.