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In engineering and scientific disciplines, there are extensive Optimization Application Problems (OAPs) such as economic dispatch, structural design, and water resources. One of the major OAPs is the operation of dams and reservoirs to minimize the gap between water supply for irrigation and demand patterns such as hydropower generation. Drawing optimal operation for dams and reservoirs is often categorized as discontinuity, multimodality, non-differentiability and nonconvexity. Classical mathematical programming-based methods for optimization might be inappropriate or unrealizable in drawing optimal operation rules for dam and reservoir operation. During the last two decades, new optimization methodsbased on nature-inspired meta-heuristic algorithms (MHAs) have motivated hydrologists to investigate MHAs as better alternative optimization tools for identifying the optimal dam and reservoir operation rules. To solve the dam and reservoir-optimization applications better, this review presents the past, present, and prospective research directions using MHAs. The problem of dam and reservoir optimization requires a fundamental shift of focus towards enhancing not only the problem formulation and decomposition but also the computational efficiency of MHAs.

The main aim of this study is to perform a time series frequency analysis and assessment for stream flow over the Johor River using a comparative method between wavelet transform (WT) and Fourier transform (FT). One of the wavelet analyses used was the discrete wavelet transform (DWT), which revealed the periodic wavelet components responsible for the trend detection. Using the FT, the periodicities that governed the trend can be obtained; however, in terms of the time domain analysis, FT seems to be lacking compared to the WT. The conditions for using DWT are discussed, and the selection decisions for such discretization are considered. Besides, using the global wavelet spectrum (GWS) and the continuous wavelet transform (CWT), the dominant periodicity components can be further well described in time frequency characteristic. In addition, the integration of the WT and Mann-Kendall (MK) test allows the determination of possible trends present in the stream flow dataset series. It is shown that the wavelet analysis is more suitable than the Fourier analysis as it exhibits good extraction of the time and frequency characteristics, especially for a nonstationary data series.

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