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Beside in situ observations, satellite-based products can provide an ideal data source for spatiotemporal monitoring of drought. In this study, the spatiotemporal pattern of drought was investigated for the northwest part of Iran using ground- and satellite-based datasets. First, the Standardized Precipitation Index series were calculated via precipitation data of 29 sites located in the selected area and the CPC Merged Analysis of Precipitation satellite. The Maximal Overlap Discrete Wavelet Transform (MODWT) was used for obtaining the temporal features of time series, and further decomposition was performed using Ensemble Empirical Mode Decomposition (EEMD) to have more stationary time series. Then, multiscale zoning was done based on subseries energy values via two clustering methods, namely the self-organizing map and K-means. The results showed that the MODWT-EEMD-K-means method successfully identified homogenous drought areas. On the other hand, correlation between the satellite sensor data (i.e. the Normalized Difference Vegetation Index, the Vegetation Condition Index, the Vegetation Healthy Index, and the Temperature Condition Index) was evaluated. The possible links between central stations of clusters and satellite-based indices were assessed via the wavelet coherence method. The results revealed that all applied satellite-based indices had significant statistical correlations with the ground-based drought index within a certain period. [ABSTRACT FROM AUTHOR]

Drought as a severe natural disaster has devastating effects on the environment; therefore, reliable drought prediction is an important issue. In the current study, based on lower upper bound estimation, hybrid models including data preprocessing, permutation entropy, and artificial intelligence (AI) methods were used for point and interval predictions of short- to long-term series of Standardized Precipitation Index in the Northwest of Iran. Ground-based and remote sensing precipitation data were used covering the period of 1983-2017. In the modeling process, first, the data processing capability via variational mode decomposition (VMD), ensemble empirical mode decomposition, and permutation entropy (PE) was investigated in drought point prediction. Then, interval prediction was applied for tolerating increased uncertainty and providing more details for practical operation decisions. The simulation results demonstrated that the proposed integrated models could achieve significantly better performance compared to single models. Hybrid PE models increased the modeling accuracy up to 40 and 55%. Finally, the efficiency of developed models was verified for Normalized Difference Vegetation Index prediction. Results demonstrated that the proposed methodology based on remote sensing data and VMD-PE-AI approaches could be successfully used for drought modeling, especially in limited or non-gauged areas. [ABSTRACT FROM AUTHOR]

In the present study, classical and proposed methods were used to investigate the monthly precipitation characteristics of 30 stations in the southeastern United States during 1968-2018. Maximal overlap discrete wavelet transform (MODWT) as preprocessing method and K-means clustering method were used. First, the monthly precipitation time series of stations were decomposed into several subseries using MODWT and considering db as the mother wavelet. Then, the energy values of theses subseries were calculated and used as inputs in K-means and radial basis functions (RBF) methods. The optimum number of clusters obtained for the considered stations in both classical and proposed methods was five clusters. In order to use the data as the input of the RBF method, the data correlation was evaluated by variogram. Based on the results of clustering and in accordance with the latitude and longitude variations of the stations, it was found that with increasing the energy of the clusters, the amount of precipitation in the stations decreased and vice versa. The silhouette coefficient of clustering for the classical method obtained was 0.3 and for the proposed method it was 0.8, which indicates better clustering of the selected area using the proposed method. [ABSTRACT FROM AUTHOR]

Growing energy demand, rising greenhouse gas emissions and the depletion of fossil fuels necessitates the development of renewable energy sources. In order to improve renewable energy utilization, it is necessary to determine the most important factors influencing energy strategy. The energy system of Iran is highly dependent on fossil fuels; however, Iran has a high potential for solar energy development and several policies are being pursued by the government to develop power generation by renewable energy resources. This study presents and discusses several important parameters of energy policy and security which make the installation of photovoltaic (PV) systems crucial for the future. Installation of solar PV will improve energy security, foster job creation and reduce environmental problems. It also introduces and investigates those institutions and organizations involved in developing renewable energies, especially solar energy. These include the Ministry of Energy and universities, both of which play key roles in the development of solar energy. [ABSTRACT FROM AUTHOR]

The type of solar panels has a great impact on the optimal sizing of a hybrid photovoltaic–battery scheme. The optimization of these schemes based on a powerful optimization approach results in more cost-effective schemes. In this paper, a new global dynamic harmony search method, as an optimization method, is presented for the optimal sizing of a hybrid photovoltaic–battery scheme. The new optimization method is aimed at minimizing the total cost and loss of load supply probability of the scheme at the same time. In this regard, the effect of the type of solar panels on the optimal sizing of the hybrid scheme is investigated. Furthermore, performance optimizations are performed with an original global dynamic harmony search, an original harmony search, and simulated annealing to determine the effectiveness of the suggested optimization method. The effects of the initial costs and efficiency of monocrystalline and polycrystalline solar panels on the optimization of hybrid systems are analyzed. The superiority of the suggested method in terms of time and cost indicators of the hybrid scheme is presented comparing the other algorithm. [ABSTRACT FROM AUTHOR]