Combining Radial Basis Function Neural Network Models and Inclusive Multiple Models for Predicting Suspended Sediment Loads

Ghanbari-Adivi, Elham; Ehteram, Mohammad; Farrokhi, Alireza; Khozani, Zohreh Sheikh

doi:10.1007/s11269-022-03256-4

Cited by 15 publications

(2 citation statements)

References 33 publications

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“…In summary, they can be divided into two categories: subjective factors and objective factors. Subjective factors include the goals and norms of educational activities, which are reflected in the meaning and value of educational activities [15][16][17][18][19]. Objective factors include the objective conditions and means of educational activities, which are the ways and means to achieve educational activities [20].…”

Section: Introductionmentioning

confidence: 99%

The Optimization Path of Higher Education Resource Allocation in China Based on Fuzzy Set Theory

Zhao¹

2023

3C TIC

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The use of fuzzy set theory to adjust the educational resource allocation model is the optimal path to achieve the allocation of educational resources. The optimal path realized by fuzzy set theory promotes the healthy development of education and addresses the current important and urgent tasks in the field of higher education in China. This study utilizes the basic theory of fuzzy sets and analyzes dynamic fuzzy set theory, fuzzy relations and fuzzy matrices. The indexes of optimal allocation are selected according to the classification of higher education resources, the fuzzy set multi-objective planning model is designed, and finally, the combination of factors of education production in colleges and universities is optimized. The experiment proves that: the average allocation efficiency of excellent colleges and universities is 0.186, and the average improvement is 35.41% after optimization. The average allocation efficiency of ordinary colleges and universities is 0.174, which is improved by 22.12% on average after optimization. It can be found that the resource allocation efficiency of excellent colleges and universities is generally higher than that of ordinary colleges and universities, and the role of excellent colleges and universities with more abundant resources themselves is greater after the optimization of college resource allocation. It indicates that by adjusting the quantity and structure of educational resources according to the optimization path, the optimization of educational resource allocation can be achieved. This verifies that: the emergence of optimal paths for solving resource allocation realized by fuzzy set theory has greatly expanded the application of fuzzy methods in the field of higher education and provided a new theoretical tool for solving resource allocation.

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Section: Introductionmentioning

confidence: 99%

The Optimization Path of Higher Education Resource Allocation in China Based on Fuzzy Set Theory

Zhao¹

2023

3C TIC

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“…Population growth, droughts, and water scarcity are challenges faced by decision-makers in terms of water supply (Farrokhi et al 2021;Ghanbari-Adivi et al 2022). Groundwater (GW) is an important global water supply source subject to increasing overexploitation.…”

Section: Introductionmentioning

confidence: 99%

Prediction of future groundwater levels under representative concentration pathway scenarios using an inclusive multiple model coupled with artificial neural networks

Ehteram

Kalantari

Ferreira

et al. 2022

Journal of Water and Climate Change

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Groundwater (GW) plays a key role in water supply in basins. As global warming and climate change affect groundwater level (GWL), it is important to predict it for planning and managing water resources. This study investigates the GWL of the Yazd-Ardakan Plain basin in Iran for the base period of 1979–2005 and predicts for periods of 2020–2059 and 2060–2099. Lagged temperature and rainfall are used as inputs to hybrid and standalone artificial neural network (ANN) models. In this study, the rat swarm algorithm (RSA), particle swarm optimisation (PSO), salp swarm algorithm (SSA), and genetic algorithm (GA) are used to adjust ANN models. The outcomes of these models are then entered into an inclusive multiple model (IMM) as an ensemble model. In this study, the output of climate models is also inserted into the IMM model to improve the estimation accuracy of temperature, rainfall, and GWL. The monthly average temperature for the base period is 12.9 °C, while average temperatures for 2020–2059 under RCP 4.5 and RCP 8.5 scenarios are 14.5 and 15.1 °C, and for 2060–2099 they are 16.41 and 18.5 °C under the same scenarios, respectively. In future periods, rainfall is low in comparison with the base period. Lagged rainfall and temperature of the base period are inserted into ANN-RSA, ANN-SSA, ANN-PSO, ANN-GA, and ANN models to predict GWL for the base period. Outputs of IMM, ANN, and the five hybrid models (ANN-RSA, ANN-SSA, ANN-PSO, and ANN-GA) indicate that root mean square errors (RMSE) are 2.12, 3.2, 4.58, 6.12, 6.98, and 7.89 m, respectively, in the testing level. It is found that GWL depletion in 2020–2059 under RCP 4.5 and RCP 8.5 scenarios are 0.60–0.88 m and 0.80–1.16 m, and in 2060–2099 under the same scenarios they are 1.49–1.97 m and 1.75–1.98 m, respectively. The results highlight the need to prevent overexploitation of GW in the Ardakan-Yazd Plain to avoid water shortages in the future.

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The Importance of Agricultural and Meteorological Predictions Using Machine Learning Models

Ehteram

Seifi

Banadkooki

2023

Application of Machine Learning Models in Agricultural and Meteorological Sciences

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Combining Radial Basis Function Neural Network Models and Inclusive Multiple Models for Predicting Suspended Sediment Loads

Cited by 15 publications

References 33 publications

The Optimization Path of Higher Education Resource Allocation in China Based on Fuzzy Set Theory

The Optimization Path of Higher Education Resource Allocation in China Based on Fuzzy Set Theory

Prediction of future groundwater levels under representative concentration pathway scenarios using an inclusive multiple model coupled with artificial neural networks

The Importance of Agricultural and Meteorological Predictions Using Machine Learning Models

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