Optimization of Nonlinear Parameters of Muskingum NL5 model With SHO algorithm

The Muskingum method was first developed by U.S. Army engineers to study flood control in the Muskingum River Basin in Ohio. To evaluate the performance of the SHO algorithm, the results of its implementation have been compared with other basic algorithms such as GA and ICA. The coding of SHO, GA and ICA algorithms was done in the MATLAB (R2018b) software programming section. The results showed that the statistical parameters obtained for the river studied by SHO algorithm in two nonlinear models of Muskingum indicate the proper performance of these algorithms in estimating the optimal values ​​of nonlinear masking modeling parameters in flood detection compared to other algorithms

Comparison between the circular and square collar in reduction of local scouring around bridge piers

In this study operation of two types of circular and square collars has been investigated on a single cylindrical pier. The results showed that using of these two types of collars cause to reduction of scour depth especially under the bed level (elevation). This research showed that the square collar is more effective than circular shape in decreasing of the scour depth). The square and circular collars showed 70% and 50% decrease in rate of scour depth, respectively in compared with simple pier without collar

Comparison between the circular and square collar in reduction of local scouring around bridge piers

In this study operation of two types of circular and square collars has been investigated on a single cylindrical pier. The results showed that using of these two types of collars cause to reduction of scour depth especially under the bed level (elevation). This research showed that the square collar is more effective than circular shape in decreasing of the scour depth). The square and circular collars showed 70% and 50% decrease in rate of scour depth, respectively in compared with simple pier without collar

Daily Rainfall Forecasting for Mashhad Synoptic Station using Artificial Neural Networks

Abstract. In this paper, we have utilized ANN (Artificial Neural Network) modeling for daily rainfall forecasting in Mashhad synoptic station. To achieve such a model, we have used daily rainfall data of March as a month with high humidity and May and December as months with medium humidity from 1986 to 2010 for this synoptic station. First, the Hurst rescaled range statistical (R/S) analysis is used to evaluate the predictability of the collected data. Then, to extract the precipitation dynamic of this station using ANN modeling, a new approach of three-layer feed-forward perceptron network with back propagation algorithm is proposed. Using this ANN model as a black box model, we have realized the hidden dynamics of rainfall through the past information of the system. The approach employs the gradient decent algorithm to train the network. Trying different parameters, some structures including GS 531 and GS 651 for March, GS 521 and GS 681 for May and GS 571 and GS 631 for December, have been selected which give the best estimation performance. Performance statistical analysis of the obtained models shows that in the best chosen model of daily forecasting, the correlation coefficient (R), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are 0.89, 0.14(mm) and 1.15(mm) for March, 0.85, 0.14(mm) and 1.16(mm) for May and 0.86, 0.15(mm) and 1.17(mm) for December, respectively which presents the effectiveness of the proposed models

Analyzing bank profile shape of alluvial stable channels using robust optimization and evolutionary ANFIS methods

Abstract In natural rivers and artificial channels in addition to the channel dimensions (widening, reduction in slope and depth in the channel banks), formed shape profile in the case that the sediment on the banks with no movement (thresholds state) is of considerable importance for engineers. To determine the bank shape profiles, various theoretical, empirical and statistical relations have been provided based on physical and numerical models by different researchers. In this study, a simple model of adaptive neuro-fuzzy inference systems (ANFIS) is combined with two algorithms of differential evolution (DE) and singular value decomposition value (SVD) and the performance of these models to predict the stable shape profiles of the channels is evaluated and compared. In this paper, the main goal is to assess extensively the effect of hybrid models based on optimized algorithms (ANFIS-DE) and multi-objective evolutionary algorithm (ANFIS-DE/SVD) in improvement of performance of ANFIS and ANFIS-DE models, respectively. Accordingly, the results assessment show that all three ANFIS, ANFIS-DE and ANFIS-DE/SVD models are perfectly able to predict shape profiles in accordance with the observed profiles for the threshold channels. Using optimized and evolutionary algorithms has a positive impact on the performance of simple model of ANFIS. As compared to the simple ANFIS model, ANFIS-DE approximately 10.1% and ANFIS-DE/SVD model 7.2% is improved compared to the ANFIS-DE model. The accuracy of ANFIS-DE/SVD model showed better performance as well about 18.6% compared to the simple ANFIS model. Therefore, it can be said that not only DE optimization algorithms have a significant impact on increasing the performance of a simple ANFIS model but also using evolutionary algorithms (ANFIS-DE/SVD) reduce the ANFIS-DE model error accordingly. Polynomial equations of bank profiles proposed by hybrid ANFIS models in the present study can be used in design and implementation of cross section of stable channels

Diagnostic Accuracy of Ultrasonography for Identification of Elbow Fractures in Children; a Systematic Review and Meta-analysis

Introduction: In spite of the results of previous studies regarding the benefits of ultrasonography for diagnosis of elbow fractures in children, the exact accuracy of this imaging modality is still under debate. Therefore, in this diagnostic systematic review and meta-analysis, we aimed to investigate the accuracy of ultrasonography in this regard. Methods: Two independent reviewers performed systematic search in Web of Science, Embase, PubMed, Cochrane, and Scopus for studies published from inception of these databases to May 2023. Quality assessment of the included studies was performed using Quality Assessment Tool for Diagnostic Accuracy Studies (QUADAS-2). Meta-Disc software version 1.4 and Stata statistical software package version 17.0 were used for statistical analysis. Results: A total of 648 studies with 1000 patients were included in the meta-analysis. The pooled sensitivity and specificity were 0.95 (95% CI: 0.93-0.97) and 0.87 (95% CI: 0.84-0.90), respectively. Pooled positive likelihood ratio (PLR) was 6.71 (95% CI: 3.86-11.67), negative likelihood ratio (NLR) was 0.09 (95% CI: 0.03-0.22), and pooled diagnostic odds ratio (DOR) of ultrasonography in detection of elbow fracture in children was 89.85 (95% CI: 31.56-255.8). The area under the summary receiver operating characteristic (ROC) curve for accuracy of ultrasonography in this regard was 0.93. Egger's and Begg's analyses showed that there is no significant publication bias (P=0.11 and P=0.29, respectively). Conclusion: Our meta-analysis revealed that ultrasonography is a relatively promising diagnostic imaging modality for identification of elbow fractures in children. However, clinicians employing ultrasonography for diagnosis of elbow fractures should be aware that studies included in this meta-analysis had limitations regarding methodological quality and are subject to risk of bias. Future high-quality studies with standardization of ultrasonography examination protocol are required to thoroughly validate ultrasonography for elbow fractures