River最新文献_第2页

Flow and sediment measurement for the Three Gorges Project 三峡工程的流量和泥沙测量

River Pub Date : 2024-02-20 DOI: 10.1002/rvr2.76

Guanglei Duan, Yanbo Peng

引用次数: 0

Quantification of sediments and delineation of their sources in Nyabugogo Stream, RWANDA: Relationship with economic development activities 卢旺达尼亚布戈戈溪流沉积物定量及其来源划分：与经济发展活动的关系

River Pub Date : 2024-02-18 DOI: 10.1002/rvr2.75

Jules Turatsinze, Pancras Ndokoye

{"title":"Quantification of sediments and delineation of their sources in Nyabugogo Stream, RWANDA: Relationship with economic development activities","authors":"Jules Turatsinze, Pancras Ndokoye","doi":"10.1002/rvr2.75","DOIUrl":"https://doi.org/10.1002/rvr2.75","url":null,"abstract":"Nyabugogo Stream receives sediment loads from practiced economic activities along its path, these sediment loads affect the composition of the water by changing its natural state, and lead to its deterioration and riverine wetland ecosystem. In this study the main sources of sediments are delineated, while corresponding loads are also quantified. After the analysis of those sediments in different periods, the relationship between economic development activities and sediment loads in Nyabugogo Stream were also determined. The findings revealed that the top most economic activities impacting the quantity of sediment load in Nyabugogo Stream were found to be mining followed by poor agricultural practices, deforestation, untreated sewages, and clay mining/fabrication of bricks respectively. Analysed samples showed in laboratory that at point A situated in Rutare sector have the lowest value of sediment loads of 3.29 × 106 tons/year while at point C situated in Kigali sector have the highest value of 141.35 × 106 tons/year, these results showed to be increased as the stream flows from Lake Muhazi to Nyabarongo River as the Stream continue to be experiencing the increase of economic activities practiced in the its catchment which also have been delineated using ArcMap, this showed the relationship between economic activities and sediment loads generated in the stream. The researchers recommend to impose the enforcement of regulations, policies and guidelines for different economic activities so that they cannot pollute natural water bodies and disturb aquatic ecosystem.","PeriodicalId":515392,"journal":{"name":"River","volume":"111 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140452617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Opportunities and challenges of interdisciplinarity in river water environmental ethics and integrated river basin management 河水环境伦理和流域综合管理跨学科性的机遇与挑战

River Pub Date : 2024-02-04 DOI: 10.1002/rvr2.74

Jun Xia, Ying Xue, Pengjun Li, Jiyun Song, Gangsheng Wang, Wenguang Luo

引用次数: 0

Opportunities and challenges of interdisciplinarity in river water environmental ethics and integrated river basin management 河水环境伦理和流域综合管理跨学科性的机遇与挑战

River Pub Date : 2024-02-04 DOI: 10.1002/rvr2.74

Jun Xia, Ying Xue, Pengjun Li, Jiyun Song, Gangsheng Wang, Wenguang Luo

引用次数: 0

A real‐time flood forecasting hybrid machine learning hydrological model for Krong H'nang hydropower reservoir 用于 Krong H'nang 水电站水库的实时洪水预报混合机器学习水文模型

River Pub Date : 2024-01-28 DOI: 10.1002/rvr2.72

Phuoc Sinh Nguyen, Truong Huy (Felix) Nguyen, The Hung Nguyen

{"title":"A real‐time flood forecasting hybrid machine learning hydrological model for Krong H'nang hydropower reservoir","authors":"Phuoc Sinh Nguyen, Truong Huy (Felix) Nguyen, The Hung Nguyen","doi":"10.1002/rvr2.72","DOIUrl":"https://doi.org/10.1002/rvr2.72","url":null,"abstract":"Flood forecasting is critical for mitigating flood damage and ensuring a safe operation of hydroelectric power plants and reservoirs. This paper presents a new hybrid hydrological model based on the combination of the Hydrologic Engineering Center‐Hydrologic Modeling System (HEC‐HMS) hydrological model and an Encoder‐Decoder‐Long Short‐Term Memory network to enhance the accuracy of real‐time flood forecasting. The proposed hybrid model has been applied to the Krong H'nang hydropower reservoir. The observed data from 33 floods monitored between 2016 and 2021 are used to calibrate, validate, and test the hybrid model. Results show that the HEC‐HMS‐artificial neural network hybrid model significantly improves the forecast quality, especially for results at a longer forecasting time. In detail, the Kling–Gupta efficiency (KGE) index, for example, increased from ∆KGE = 16% at time t + 1 h to ∆KGE = 69% at time t + 6 h. Similar results were obtained for other indicators including peak error and volume error. The computer program developed for this study is being used in practice at the Krong H'nang hydropower to aid in reservoir planning, flood control, and water resource efficiency.","PeriodicalId":515392,"journal":{"name":"River","volume":"412 28","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140490428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring the past to discern the present: The role of historico‐geographical study in differentiating natural river channels from artificial canals 探索过去，鉴别现在：历史地理研究在区分天然河道和人工运河中的作用

River Pub Date : 2024-01-19 DOI: 10.1002/rvr2.73

B. Das

{"title":"Exploring the past to discern the present: The role of historico‐geographical study in differentiating natural river channels from artificial canals","authors":"B. Das","doi":"10.1002/rvr2.73","DOIUrl":"https://doi.org/10.1002/rvr2.73","url":null,"abstract":"The historical background of a watercourse, whether it be a natural river or an artificial canal, holds great importance in understanding hydrological heritage, landscape evolution, planning processes, and making informed predictions for the future. This study focuses on the case study of the Churni River, which has been regarded as an artificial canal in the local literature, history, and geography research papers. The objective of this study is to investigate the authenticity of the claims and explore whether the Churni River is a natural river or an artificially constructed canal. Through an examination of existing literature and the present channel morphology, it becomes apparent that the alleged myths and rumors surrounding the origin of the Churni River, proposing its deliberate construction as a man‐made canal by Maharaja Krishnachandra, lack substantial evidence. Conversely, a more plausible scenario emerges, suggesting that the Maharaja's endeavors were likely focused on rejuvenating the degraded and silt‐laden course of an already existing river. Additionally, no evidence is found to support the claim that the name “Churni” was assigned by Maharaja Krishnachandra, and the alternative name “Kangkana” lacks substantiation as a name for the river. This research contributes to a comprehensive understanding of the historical and geographical context of the Churni River, shedding light on its origin and nature while emphasizing the significance of historical analysis in distinguishing natural river channels from artificial canals and will widen the avenue of future research on river heritage of like situation.","PeriodicalId":515392,"journal":{"name":"River","volume":"26 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139613589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prediction of the water level at the Kien Giang River based on regression techniques 基于回归技术的建江河水位预测

River Pub Date : 2024-01-18 DOI: 10.1002/rvr2.71

T. Q. Chieu, Nguyen Thi Phuong Thao, Dao Thi Hue, Nguyen Thi Thu Huong

{"title":"Prediction of the water level at the Kien Giang River based on regression techniques","authors":"T. Q. Chieu, Nguyen Thi Phuong Thao, Dao Thi Hue, Nguyen Thi Thu Huong","doi":"10.1002/rvr2.71","DOIUrl":"https://doi.org/10.1002/rvr2.71","url":null,"abstract":"Model accuracy and runtime are two key issues for flood warnings in rivers. Traditional hydrodynamic models, which have a rigorous physical mechanism for flood routine, have been widely adopted for water level prediction in river, lake, and urban areas. However, these models require various types of data, in‐depth domain knowledge, experience with modeling, and intensive computational time, which hinders short‐term or real‐time prediction. In this paper, we propose a new framework based on machine learning methods to alleviate the aforementioned limitation. We develop a wide range of machine learning models such as linear regression (LR), support vector regression (SVR), random forest regression (RFR), multilayer perceptron regression (MLPR), and light gradient boosting machine regression (LGBMR) to predict the hourly water level at Le Thuy and Kien Giang stations of the Kien Giang river based on collected data of 2010, 2012, and 2020. Four evaluation metrics, that is, R2, Nash–Sutcliffe efficiency, mean absolute error, and root mean square error, are employed to examine the reliability of the proposed models. The results show that the LR model outperforms the SVR, RFR, MLPR, and LGBMR models.","PeriodicalId":515392,"journal":{"name":"River","volume":"70 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139526407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0