A comparison of artificial intelligence approaches in predicting discharge coefficient of streamlined weirs

In the present research, three different data-driven models (DDMs) are developed to predict the discharge coefficient of streamlined weirs (C-dstw). Some machine-learning methods (MLMs) and intelligent optimization models (IOMs) such as Random Forest (RF), Adaptive NeuroFuzzy Inference System (ANFIS), and gene expression program (GEP) methods are employed for the prediction of C-dstw. To identify input variables for the prediction of C-dstw by these DMMs, among potential parameters on C-dstw, the most effective ones including geometric features of streamlined weirs, relative eccentricity (lambda), downstream slope angle (beta), and water head over the crest of the weir (h(1)) are determined by applying Buckingham pi-theorem and cosine amplitude analyses. In this modeling, by changing architectures and fundamental parameters of the aforesaid approaches, many scenarios are defined to obtain ideal estimation results. According to statistical metrics and scatter plot, the GEP model is determined as a superior method to estimate C-dstw with high performance and accuracy. It yields an R-2 of 0.97, a Total Grade (TG) of 20, RMSE of 0.032, and MAE of 0.024. Besides, the generated mathematical equation for C-dstw in the best scenario by GEP is likened to the corresponding measured ones and the differences are within 0-10%

Proceedings of the International Workshop on Hydraulic Design of Low-Head Structures - IWLHS 2013

Scientific standards applicable to publication of BAWProceedings: http://izw.baw.de/publikationen/vzb_dokumente_oeffentlich/0/2020_07_BAW_Scientific_standards_conference_proceedings.pd

The Application of Hydraulic and Sediment Transport Models in Fluvial Geomorphology

After publishing the famous “Fluvial Processes in Geomorphology” in the early 1960s, the work of Luna Leopold, Gordon Wolman, and John Miller became a key for opening the door to understanding rivers and streams. They first illustrated the problem to geomorphologists and geographers. Later, Chang, in his “Fluvial Processes in River Engineering”, provided a basis for engineers, showing this group of professionals how to deal with rivers and how to understand them. Since then, more informative studies have been published. Many of the authors started to combine fluvial geomorphology knowledge and river engineering needs, such as “Tools in Fluvial Geomorphology” by G. Mathias Kondolf and Hervé Piégay, or focused more on river engineering tasks, such as “Stream Restoration in Dynamic Fluvial Systems: Scientific Approaches” by Andrew Simon, Sean Bennett, and Janine Castro. Finally, Luna Leopold summarized river and stream morphologies in the beautiful “A view of the river”. It appears that we continue to explore this subject in the right direction. We better understand rivers and streams, and as engineers and fluvial geomorphologists, we can establish tools to help bring rivers alive. However, there is still a hunger for more scientific tools that we could use to further understand rivers and to support the development of healthy streams and rivers with high biodiversity in the present world, which has started to face water scarcity

Computational Optimizations for Machine Learning

The present book contains the 10 articles finally accepted for publication in the Special Issue “Computational Optimizations for Machine Learning” of the MDPI journal Mathematics, which cover a wide range of topics connected to the theory and applications of machine learning, neural networks and artificial intelligence. These topics include, among others, various types of machine learning classes, such as supervised, unsupervised and reinforcement learning, deep neural networks, convolutional neural networks, GANs, decision trees, linear regression, SVM, K-means clustering, Q-learning, temporal difference, deep adversarial networks and more. It is hoped that the book will be interesting and useful to those developing mathematical algorithms and applications in the domain of artificial intelligence and machine learning as well as for those having the appropriate mathematical background and willing to become familiar with recent advances of machine learning computational optimization mathematics, which has nowadays permeated into almost all sectors of human life and activity

Advances in Hydraulics and Hydroinformatics Volume 2

This Special Issue reports on recent research trends in hydraulics, hydrodynamics, and hydroinformatics, and their novel applications in practical engineering. The Issue covers a wide range of topics, including open channel flows, sediment transport dynamics, two-phase flows, flow-induced vibration and water quality. The collected papers provide insight into new developments in physical, mathematical, and numerical modelling of important problems in hydraulics and hydroinformatics, and include demonstrations of the application of such models in water resources engineering

Research on Hydraulics and River Dynamics

This Special Issue includes nine original contributions focused on river hydraulics. Four of these resulted from cooperation between universities from different countries: (a) Russia and Poland , (b) Taiwan and the USA , (c) Iran and Italy, and (d) India and Italy . The other contributions resulted from research carried out in universities from South Korea [5], Greece [6], China , and Japan