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Data on optimization of the Karun-4 hydropower reservoir operation using evolutionary algorithms.

ABSTRACT: This article describes the time series data for optimizing the hydropower operation of the Karun-4 reservoir located in Iran for a period of 106 months (from October 2010 to July 2019). The utilized time-series data included reservoir inflow, reservoir storage, evaporation from the reservoir, precipitation on the reservoir, and release of water through the power plant. In this data article, a model based on Moth Swarm Algorithm (MSA) was developed for the optimization of water resources. The analysis showed that the best solutions achieved by the MSA, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) were 0.147, 0.3026, and 0.1584, respectively. The analysis of these datasets revealed that the MSA algorithm was superior to GA and PSO algorithms in the optimal operation of the hydropower reservoir problem.

SUBMITTER: Akbarifard S

PROVIDER: S-EPMC6965708 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Data on optimization of the Karun-4 hydropower reservoir operation using evolutionary algorithms.

Akbarifard Saeid S Sharifi Mohammad Reza MR Qaderi Kourosh K

Data in brief 20200108

This article describes the time series data for optimizing the hydropower operation of the Karun-4 reservoir located in Iran for a period of 106 months (from October 2010 to July 2019). The utilized time-series data included reservoir inflow, reservoir storage, evaporation from the reservoir, precipitation on the reservoir, and release of water through the power plant. In this data article, a model based on Moth Swarm Algorithm (MSA) was developed for the optimization of water resources. The ana ...[more]

PMID: 31970276

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Project description:BackgroundOptimization of indirect shoot regeneration protocols is one of the key prerequisites for the development of Agrobacterium-mediated genetic transformation and/or genome editing in Passiflora caerulea. Comprehensive knowledge of indirect shoot regeneration and optimized protocol can be obtained by the application of a combination of machine learning (ML) and optimization algorithms.Materials and methodsIn the present investigation, the indirect shoot regeneration responses (i.e., de novo shoot regeneration rate, the number of de novo shoots, and length of de novo shoots) of P. caerulea were predicted based on different types and concentrations of PGRs (i.e., TDZ, BAP, PUT, KIN, and IBA) as well as callus types (i.e., callus derived from different explants including leaf, node, and internode) using generalized regression neural network (GRNN) and random forest (RF). Moreover, the developed models were integrated into the genetic algorithm (GA) to optimize the concentration of PGRs and callus types for maximizing indirect shoot regeneration responses. Moreover, sensitivity analysis was conducted to assess the importance of each input variable on the studied parameters.ResultsThe results showed that both algorithms (RF and GRNN) had high predictive accuracy (R2 > 0.86) in both training and testing sets for modeling all studied parameters. Based on the results of optimization process, the highest de novo shoot regeneration rate (100%) would be obtained from callus derived from nodal segments cultured in the medium supplemented with 0.77 mg/L BAP plus 2.41 mg/L PUT plus 0.06 mg/L IBA. The results of the sensitivity analysis showed the explant-dependent impact of exogenous application of PGRs on indirect de novo shoot regeneration.ConclusionsA combination of ML (GRNN and RF) and GA can display a forward-thinking aid to optimize and predict in vitro culture systems and consequentially cope with several challenges faced currently in Passiflora tissue culture.

Dataset Information

Data on optimization of the Karun-4 hydropower reservoir operation using evolutionary algorithms.

Publications

Data on optimization of the Karun-4 hydropower reservoir operation using evolutionary algorithms.

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