Short-Term Hydrological Drought Forecasting Based on Different Nature-Inspired Optimization Algorithms Hybridized With Artificial Neural Networks

Nabipour, Narjes; Dehghani, Majid; Mosavi, Amir; Shamshirband, Shahaboddin

doi:10.1109/access.2020.2964584

Cited by 63 publications

(45 citation statements)

References 41 publications

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“…The ANN is the most widely used method in water resources modeling [4,20,34,35,36]. Multilayer perceptron (MLP) with a feed-forward back-propagation algorithm is one of the most popular types of ANN, which was used for forecasting hydrological variables such as drought, streamflow, evaporation, etc [37,38,39,40,41,42]. The capability of ANN-based models in fast learning and using noisy data made them accessible during the past decades [43].…”

Section: Introductionmentioning

confidence: 99%

Derivation of Optimized Equations for Estimation of Dispersion Coefficient in Natural Streams Using Hybridized ANN With PSO and CSO Algorithms

et al. 2020

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In this paper, a new hybrid model is developed to improve the accuracy in the prediction of the longitudinal dispersion coefficient (Kx) and the derivation of novel optimized explicit equations for natural streams. For this purpose, an artificial neural network (ANN) is hybridized with particle swarm optimization (PSO) and cat swarm optimization (CSO) algorithms. The CSO and PSO are used to find the optimum values of biases and weights in ANN structure and formulate the results as novel explicit predictive equations than the classical black-box methods. The hydraulic parameters of the natural stream and some geometric parameters were utilized for the model developments. Eight different input combinations are used as the input vectors to ANN, ANN-PSO, and ANN-CSO models, whereas the dispersion coefficient (Kx) is the target model output. The developed models are trained and tested bya comprehensive reference data sets measured on streams in the United States, that were used previously by Tayfur and Singh (2005) in ANN models. The main aims, novelty, and contributions of the present study are 1) improving the accuracy of classical ANN-based Kx predictions by hybridizing with CSO and PSO. 2) Performing sensitive analysis of ANN, ANN-CSO, and ANN-PSO based on input combinations 3) derivation of novel explicit optimized ANN-CSO, ANN-PSO, equations for predicting Kx rather than the classical ANN black-box methods. The results depicted that the highest accuracy and superiority were attained by the ANN-PSO model, with input variables of B, H, U, U*, followed by ANN-CSO and ANN. By using the optimized trained black box ANN models, two novel explicit predictive equations are derived, and their results are compared with the empirical equations. Comparative assessments confirmed significant improvements in the hybrid equations' results than the classical ANN and previously published equations. The developed novel equations can be used to estimate the Kx in one-dimensional pollutant transfer models that are essential for the pollution studies in environmental river engineering practices.

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Section: Introductionmentioning

confidence: 99%

Derivation of Optimized Equations for Estimation of Dispersion Coefficient in Natural Streams Using Hybridized ANN With PSO and CSO Algorithms

et al. 2020

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“…Since the learning rate of the artificial neural network is fixed, the convergence rate of the network is slow and needs a long training time. So, in recent years, some scholars try to use some algorithms combined with the ANN to speed up its convergence rate, such as AEEMD–ANN (an adaptive ensemble empirical mode decomposition with the ANN), SSA–ANN (a singular spectrum analysis with the ANN), PSO–ANN (Particle Swarm Optimization PSO with the ANN), and other optimization models are proposed [ 54 , 55 , 56 ].…”

Section: Methodsmentioning

confidence: 99%

Assessing and Predicting the Water Resources Vulnerability under Various Climate-Change Scenarios: A Case Study of Huang-Huai-Hai River Basin, China

Chen

Feng

Zhang

et al. 2020

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The Huang-Huai-Hai River Basin plays an important strategic role in China’s economic development, but severe water resources problems restrict the development of the three basins. Most of the existing research is focused on the trends of single hydrological and meteorological indicators. However, there is a lack of research on the cause analysis and scenario prediction of water resources vulnerability (WRV) in the three basins, which is the very important foundation for the management of water resources. First of all, based on the analysis of the causes of water resources vulnerability, this article set up the evaluation index system of water resource vulnerability from three aspects: water quantity, water quality and disaster. Then, we use the Improved Blind Deletion Rough Set (IBDRS) method to reduce the dimension of the index system, and we reduce the original 24 indexes to 12 evaluation indexes. Third, by comparing the accuracy of random forest (RF) and artificial neural network (ANN) models, we use the RF model with high fitting accuracy as the evaluation and prediction model. Finally, we use 12 evaluation indexes and an RF model to analyze the trend and causes of water resources vulnerability in three basins during 2000–2015, and further predict the scenarios in 2020 and 2030. The results show that the vulnerability level of water resources in the three basins has been improved during 2000–2015, and the three river basins should follow the development of scenario 1 to ensure the safety of water resources. The research proved that the combination of IBDRS and an RF model is a very effective method to evaluate and forecast the vulnerability of water resources in the Huang-Huai-Hai River Basin.

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“…In this paper, the GA algorithm was used to solve the GEP optimization problem [46][47][48][49][50][51][52][53][54][55]. The general trend of the GA algorithm for solving the GEP problem is shown in Figure 1.…”

Section: Ga Optimizationmentioning

confidence: 99%

Generation Expansion Planning in the Presence of Wind Power Plants Using a Genetic Algorithm Model

et al. 2020

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One of the essential aspects of power system planning is generation expansion planning (GEP). The purpose of GEP is to enhance construction planning and reduce the costs of installing different types of power plants. This paper proposes a method based on a genetic algorithm (GA) for GEP in the presence of wind power plants. Since it is desirable to integrate the maximum possible wind power production in GEP, the constraints for incorporating different levels of wind energy in power generation are investigated comprehensively. This will allow the maximum reasonable amount of wind penetration in the network to be obtained. Besides, due to the existence of different wind regimes, the penetration of strong and weak wind on GEP is assessed. The results show that the maximum utilization of wind power generation capacity could increase the exploitation of more robust wind regimes. Considering the growth of the wind farm industry and the cost reduction for building wind power plants, the sensitivity of GEP to the variations of this cost is investigated. The results further indicate that for a 10% reduction in the initial investment cost of wind power plants, the proposed model estimates that the overall cost will be minimized.

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Short-Term Hydrological Drought Forecasting Based on Different Nature-Inspired Optimization Algorithms Hybridized With Artificial Neural Networks

Cited by 63 publications

References 41 publications

Derivation of Optimized Equations for Estimation of Dispersion Coefficient in Natural Streams Using Hybridized ANN With PSO and CSO Algorithms

Derivation of Optimized Equations for Estimation of Dispersion Coefficient in Natural Streams Using Hybridized ANN With PSO and CSO Algorithms

Assessing and Predicting the Water Resources Vulnerability under Various Climate-Change Scenarios: A Case Study of Huang-Huai-Hai River Basin, China

Generation Expansion Planning in the Presence of Wind Power Plants Using a Genetic Algorithm Model

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