River engineering

About: River engineering is a research topic. Over the lifetime, 435 publications have been published within this topic receiving 10286 citations. The topic is also known as: Channelisation.

Effects and optimal proposal analyses of soil and water conservation measures for disturbed accumulations of typical production and construction projects in Shenzhen City

Abstract: Due to the specific natural conditions and high intensity of human activities in Shenzhen, the characteristics of water and soil erosion of Shenzhen are prone and frequent. There are no large rivers, lakes and reservoirs in Shenzhen, and the rainwater storage capacity is unsubstantial that result in some problems such as ecological environment deterioration, river siltation, drainage pipe network blockage, and aggravation of flood disasters. The work of soil and water conservation in Shenzhen is still grim. In this paper, the sediment under different soil and water conservation measures were measured based on long-term observation of typical road engineering, river engineering and pipeline engineering in Shenzhen. Then, the effect of soil and water conservation measures implemented in these typical engineering were analyzed. At last, the optimal proposals of soil and water conservation measures for these typical engineering were proposed. This research aims to provide data support for the formulation and modification of soil and water conservation technical specifications for production and construction projects in Shenzhen.

Comparison of Artificial Neural Networks, Adaptive Neuro-Fuzzy and Sediment Rating Curve Models for Estimating Suspended Sediment Load of Ajichay River

Abstract: In water construction projects, river engineering, and irrigation and drainage engineering, it is vital to estimate the accurate volume of the sediment transported by rivers. As the sediment transport phenomenon is an immensely complex problem, therefore presenting an appropriate solution for precise evaluation of the suspended load in rivers is tedious and the mathematical models are not also accurate enough to be applied. Nowadays application of artificial intelligence systems has been developed as a novel solution in analysis of water resources problems. In this research, the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Artificial Neural Networks (ANNs) models were utilized to determine suspended sediment rate of Ajichay River. Discharge, sediment load and water level data were used to prepare the models and obtain sediment rating curves. The statistical period is also divided into three seasons, namely dry, wet and snow melting. The accuracy of the models for these periods has been tested. The results showed that ANFIS neuro-fuzzy had better accuracy for determination of suspend sediment loads in comparison with both the ANNS and the rating curve.

Morphological changes of river due to construction of a port at Baan Paeng, Nakorn Panom

Abstract: The purpose of this paper is to analyze fluvial process and evaluate the impact of a portconstruction on river by using quasi-two-dimensional GSTARS 2.1 that can simulate the lateralchange or river bank failure. The study emphasizes the quantitative influence of construction of theport which obstructs or diverts the flow direction. Furthermore, it shows the efficiency and applicationof the mathematical model on river engineering especially on the cases that have limited data. Theselected case study is the project for surveying and the design of a port at the Mekong River, AmphorBaan Paeng, Nakorn Panom Province. The data of 19 river cross-sections, discharge of water,maximum level of water in 2002 and minimum level of water in 1992 with 20 return periods wereused. The results show that the port does not severely affects the changing of the thalweg and thebank of the Mekong River at the project location and nearby areas. Moreover, the scouring or failureof river bank was found to be reduced at some river cross-sections.

Effects of water level and flow discharge fluctuations on suspended sediment load using artificial neural networks (case study: ahar chay river)

Abstract: Evaluation of suspended sediments and parameters affecting them is of great importance in river engineering. In this research the effects of water level and flow discharge fluctuations on suspended sediment load were studied using Artificial neural networks (ANNs). Ahar chay river basin, located in north of Iran, with area totaling about 2400 km as a sub-basin of Aras river was chosen. Data from Tazeh-Kand, Orang, Casein, Oshdologh and Bermice (upstream of Sattarkhan dam) stations were employed in ANNs method and the suspended sediment were predicted. The results showed that, suspended load forecasted by water level data had low accuracy than that forecasted by the flow discharge. The maximum and minimum coefficients of correlation for water level data were and 0.08 in Orang and Oshdologh stations, respectively. The corresponding values for flow discharge were 0.84 in Oshdologh and 0.7 in Bermice. The main reason for the low coefficient of tness in some stations probably were due to shortage of data, lack of temporal sequence and inaccuracy of water level fluctuations compared to the flow measurements. It appeared that, in this basin, in moving from high to low land with increasing discharge and sediment rates, the results of ANN became more reliable. Water level fluctuations did not show this trend.