Zumry Niyas, Charuni Madhushani, M. Gunathilake, Vindhya Basnayaka, Komali Kantamaneni, Upaka S. Rathnayake
This study evaluates the rainfall erosivity (RE) and erosivity density (ED) over the Kelani River basin, Sri Lanka for a period of 31 years (1990–2020). The river basin is well known for its annual floods during the southwestern monsoon season and severe erosion including landslides can be observed. The catchment was analyzed for its RE using the Wischmeier and Smith algorithm and for its ED using Kinnel's algorithm. The monthly rainfall data spreading over the river basin were used to analyze the monthly, seasonal, and annual RE and ED. Interestingly, the annual RE showed a linear increasing trend line over 31 years, and a maximum value of 2,831.41 MJ mm ha−1 h−1 yr−1 was able to be observed in the year 2016. The RE peaks in May which is in the southwestern monsoon season. This reveals that the risk of soil erosion in the basin is high in the southwestern monsoon season. In addition, land use and land cover changes over the years have adversely impacted the erosion rates. Therefore, it is highly recommended to investigate soil erosion in-depth and then implement relevant regulations to conserve the soil layers upstream of the river basin.
本研究评估了斯里兰卡凯拉尼河流域 31 年间（1990-2020 年）的降雨侵蚀率（RE）和侵蚀密度（ED）。众所周知，该流域每年在西南季风季节都会发生洪水，并出现严重的侵蚀现象，包括山体滑坡。该流域的 RE 分析采用 Wischmeier 和 Smith 算法，ED 分析采用 Kinnel 算法。利用遍布流域的月降雨量数据分析了月度、季节和年度 RE 和 ED。有趣的是，年可再生能源在 31 年中呈现线性增长趋势线，在 2016 年观测到最大值 2,831.41 MJ mm ha-1 h-1 yr-1。每年 RE 的峰值出现在西南季风季节的 5 月份。这表明该流域在西南季风季节的水土流失风险较高。此外，多年来土地利用和土地覆盖的变化也对水土流失率产生了不利影响。因此，强烈建议对水土流失进行深入调查，然后实施相关法规，以保护流域上游的土壤层。
{"title":"Rainfall erosivity assessment over a flooding basin, Kelani River basin, Sri Lanka","authors":"Zumry Niyas, Charuni Madhushani, M. Gunathilake, Vindhya Basnayaka, Komali Kantamaneni, Upaka S. Rathnayake","doi":"10.2166/hydro.2024.202","DOIUrl":"https://doi.org/10.2166/hydro.2024.202","url":null,"abstract":"\u0000 This study evaluates the rainfall erosivity (RE) and erosivity density (ED) over the Kelani River basin, Sri Lanka for a period of 31 years (1990–2020). The river basin is well known for its annual floods during the southwestern monsoon season and severe erosion including landslides can be observed. The catchment was analyzed for its RE using the Wischmeier and Smith algorithm and for its ED using Kinnel's algorithm. The monthly rainfall data spreading over the river basin were used to analyze the monthly, seasonal, and annual RE and ED. Interestingly, the annual RE showed a linear increasing trend line over 31 years, and a maximum value of 2,831.41 MJ mm ha−1 h−1 yr−1 was able to be observed in the year 2016. The RE peaks in May which is in the southwestern monsoon season. This reveals that the risk of soil erosion in the basin is high in the southwestern monsoon season. In addition, land use and land cover changes over the years have adversely impacted the erosion rates. Therefore, it is highly recommended to investigate soil erosion in-depth and then implement relevant regulations to conserve the soil layers upstream of the river basin.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141103323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The frequent occurrence of typhoons causes geological disasters, such as debris flow and landslide, by bringing extreme rainfall events. Due to the lack of data collection on extreme rainfall events caused by typhoons, the relationship between rainfall patterns and debris flow has not been deeply studied. Therefore, based on hourly rainfall data during typhoons in Wenzhou from 1980 to 2017, this study used a variety of methods to classify the rainfall events and analyze the characteristics of typhoon-induced rainfall events and their impacts on the probability of debris-flow occurrence. Three classification techniques, including dynamic time warping, K-Means cluster, and self-organizing maps, are applied with two ways to normalize rainfall records, including dimensionless rainfall density curves and dimensionless rainfall cumulation curves, for extracting rainfall patterns from recorded 1 h rainfall data. The rainfall patterns are then used for the estimation of typhoon-induced debris-flow occurrence probability. Results show that different methods present different rainfall patterns. The probability of debris flows varies with different patterns of rainfall events. The research results help deepen the understanding of typhoon rainfall events and debris-flow disaster prevention in the region and contribute to regional flood control and disaster reduction.
{"title":"Effects of rainfall pattern classification methods on the probability estimation of typhoon-induced debris-flow occurrence","authors":"Zhixu Bai, Youjian Yang, Lin Guo, Leman Lin","doi":"10.2166/hydro.2024.286","DOIUrl":"https://doi.org/10.2166/hydro.2024.286","url":null,"abstract":"\u0000 \u0000 The frequent occurrence of typhoons causes geological disasters, such as debris flow and landslide, by bringing extreme rainfall events. Due to the lack of data collection on extreme rainfall events caused by typhoons, the relationship between rainfall patterns and debris flow has not been deeply studied. Therefore, based on hourly rainfall data during typhoons in Wenzhou from 1980 to 2017, this study used a variety of methods to classify the rainfall events and analyze the characteristics of typhoon-induced rainfall events and their impacts on the probability of debris-flow occurrence. Three classification techniques, including dynamic time warping, K-Means cluster, and self-organizing maps, are applied with two ways to normalize rainfall records, including dimensionless rainfall density curves and dimensionless rainfall cumulation curves, for extracting rainfall patterns from recorded 1 h rainfall data. The rainfall patterns are then used for the estimation of typhoon-induced debris-flow occurrence probability. Results show that different methods present different rainfall patterns. The probability of debris flows varies with different patterns of rainfall events. The research results help deepen the understanding of typhoon rainfall events and debris-flow disaster prevention in the region and contribute to regional flood control and disaster reduction.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141106932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Multi-reservoir systems that have diverse and conflicting objectives are challenging to design due to their uncertainties, non-linearities, dimensions and conflicts. The operation of multi-reservoir systems is crucial to increasing hydropower production. In this study, we have investigated the application and effectiveness of the new optimization algorithm MOAHA in multi-objective cascade reservoirs with conflicting objectives, and it has been investigated on a case-by-case basis on Karun cascade reservoirs (Karun 3, Karun 1, Masjed Soleyman and Gotvand). The suggested method (MOAHA) output with other optimization algorithms, MOALO, MOGWO and NSGA-II, were compared and evaluation criteria were used to select the best performance. Additionally, we employed the powerful TOPSIS method to determine the most suitable algorithm. The considered restrictions have also been observed. The results indicate that MOAHA's proposed method is better than the compared algorithms in solving optimal reservoir utilization problems in multi-reservoir water resource systems. The reduction of evaporation (losses) from the tank surface by 9% is accompanied by a 15% increase in hydropower energy production. MOAHA, scoring 0.90, is deemed the best algorithm in this study, whereas MOGWO, with a score of 0.10, is regarded as the least effective algorithm.
{"title":"A combination approach for optimization operation of multi-objective cascade reservoir systems (Case study: Karun reservoirs)","authors":"Zahra Khoramipoor, Saeed Farzin","doi":"10.2166/hydro.2024.264","DOIUrl":"https://doi.org/10.2166/hydro.2024.264","url":null,"abstract":"\u0000 \u0000 Multi-reservoir systems that have diverse and conflicting objectives are challenging to design due to their uncertainties, non-linearities, dimensions and conflicts. The operation of multi-reservoir systems is crucial to increasing hydropower production. In this study, we have investigated the application and effectiveness of the new optimization algorithm MOAHA in multi-objective cascade reservoirs with conflicting objectives, and it has been investigated on a case-by-case basis on Karun cascade reservoirs (Karun 3, Karun 1, Masjed Soleyman and Gotvand). The suggested method (MOAHA) output with other optimization algorithms, MOALO, MOGWO and NSGA-II, were compared and evaluation criteria were used to select the best performance. Additionally, we employed the powerful TOPSIS method to determine the most suitable algorithm. The considered restrictions have also been observed. The results indicate that MOAHA's proposed method is better than the compared algorithms in solving optimal reservoir utilization problems in multi-reservoir water resource systems. The reduction of evaporation (losses) from the tank surface by 9% is accompanied by a 15% increase in hydropower energy production. MOAHA, scoring 0.90, is deemed the best algorithm in this study, whereas MOGWO, with a score of 0.10, is regarded as the least effective algorithm.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141103866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Roraya River Basin is an important water conservation area in Sulawesi. The soil erosion status in this study was investigated using Revised Universal Soil Loss Equation (RUSLE) on Google Earth Engine (GEE). Soil erosion modulus, a characteristic of the spatiotemporal variation of soil erosion intensity, is calculated and analyzed from various multi-source data. The research results show that (1) the average soil erosion modulus in the Roraya River Basin in 2001–2021 was 307.22 t · h−1 · year−1. This shows that around 25% of the Roraya River Basin requires soil protection measures as the region faces a significant risk of erosion; (2) the trend in the range of soil erosion in the Roraya River Basin in 2001–2021 tends to vary, initially stable, then decreases and increases significantly with increasing altitude and slope (western plateau). A striking trend occurs in various classes of vegetation cover and rainfall erosivity where the increase in soil erosion is caused by both and this applies in reverse, thus encouraging the dynamic development of soil erosion: (3) RUSLE model integrated into GEE can handle vegetation cover factors and conservation measure factors. This is a reliable soil erosion monitoring tool on a wide scale.
{"title":"Spatiotemporal dynamic of soil erosion in the Roraya River Basin based on RUSLE model and Google Earth Engine","authors":"S. Aldiansyah, Farida Wardani","doi":"10.2166/hydro.2024.219","DOIUrl":"https://doi.org/10.2166/hydro.2024.219","url":null,"abstract":"\u0000 \u0000 The Roraya River Basin is an important water conservation area in Sulawesi. The soil erosion status in this study was investigated using Revised Universal Soil Loss Equation (RUSLE) on Google Earth Engine (GEE). Soil erosion modulus, a characteristic of the spatiotemporal variation of soil erosion intensity, is calculated and analyzed from various multi-source data. The research results show that (1) the average soil erosion modulus in the Roraya River Basin in 2001–2021 was 307.22 t · h−1 · year−1. This shows that around 25% of the Roraya River Basin requires soil protection measures as the region faces a significant risk of erosion; (2) the trend in the range of soil erosion in the Roraya River Basin in 2001–2021 tends to vary, initially stable, then decreases and increases significantly with increasing altitude and slope (western plateau). A striking trend occurs in various classes of vegetation cover and rainfall erosivity where the increase in soil erosion is caused by both and this applies in reverse, thus encouraging the dynamic development of soil erosion: (3) RUSLE model integrated into GEE can handle vegetation cover factors and conservation measure factors. This is a reliable soil erosion monitoring tool on a wide scale.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141108309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Muhammad Faisal, Zai-Jin You, Muhammad Bilal Idrees, Shoaib Ali, Noman Ali Buttar
To accurately figure out how much pollution comes from urban surface runoff and take steps to protect receiving water, we needed to fully understand how road-deposited sediments (RDS) wash off. Twelve RDS sample activities along an urban road were used to define the RDS accumulation and wash-off mechanism. Our research indicates that particles smaller than 100 μm imparted 59–73% of the wash-off load. Two instances of natural rainfall reduced the aggregate RDS mass by approximately 27–36%. On days without rain, the RDS particle shrank in size, but it became heavier after a downpour. The results showed that the source restricted the tiny particles washed off of RDS, while transport generally restricted the heavier particles washed off. We used 39 artificial rainfall events with different particle sizes to confirm our results on RDS wash-off. When compared to the heavier particles, tiny particles have a greater wash-off percentage, and when it comes to describing the wash-off mechanism, Fw values offer an inventive and insightful assessment. It has been assessed that tiny particles were source-restricted and this mechanism occurred during the initial stage, but heavier particles were transport-restricted and it occurred during the late stage.
{"title":"Exploring urban runoff complexity: road-deposited sediment wash-off mechanisms and dynamics of constraints","authors":"Muhammad Faisal, Zai-Jin You, Muhammad Bilal Idrees, Shoaib Ali, Noman Ali Buttar","doi":"10.2166/hydro.2024.022","DOIUrl":"https://doi.org/10.2166/hydro.2024.022","url":null,"abstract":"\u0000 \u0000 To accurately figure out how much pollution comes from urban surface runoff and take steps to protect receiving water, we needed to fully understand how road-deposited sediments (RDS) wash off. Twelve RDS sample activities along an urban road were used to define the RDS accumulation and wash-off mechanism. Our research indicates that particles smaller than 100 μm imparted 59–73% of the wash-off load. Two instances of natural rainfall reduced the aggregate RDS mass by approximately 27–36%. On days without rain, the RDS particle shrank in size, but it became heavier after a downpour. The results showed that the source restricted the tiny particles washed off of RDS, while transport generally restricted the heavier particles washed off. We used 39 artificial rainfall events with different particle sizes to confirm our results on RDS wash-off. When compared to the heavier particles, tiny particles have a greater wash-off percentage, and when it comes to describing the wash-off mechanism, Fw values offer an inventive and insightful assessment. It has been assessed that tiny particles were source-restricted and this mechanism occurred during the initial stage, but heavier particles were transport-restricted and it occurred during the late stage.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141108422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Díaz, Javier González, Kevin Lansey, Michael Pointl
The effect of different temporal (from seconds to months) and spatial aggregation scales (from individual users to full urban areas) on water demand behavior has been explored to a limited degree. The effort described here extends those works by evaluating the scale effects of residential water consumption in a unique US data set that covers 10,000 households with a 1-gallon (3.79 L) hourly resolution over 2 years. A preliminary data analysis and a sequential Principal Component Analysis (PCA) is carried out to assess the effect of different temporal (weekly, daily, hourly) and spatial aggregation (individual meters and groups every 10, 100 and 1,000 m) levels on demand. Results show that individual users act very differently from each other, and individual consumer variability is only canceled out when a significant number of households are aggregated. The implications of this finding are assessed from a hydraulic modeling perspective as the spatiotemporal scale of measurements may condition the type of analysis that can be carried out in practice. However, additional work is needed to explore the point at which it may be worth to embrace a micro (per fixture/household) or a macro (per node/network) approach for different purposes.
{"title":"Scale effects and implications of the stochastic structure of customer water demands","authors":"S. Díaz, Javier González, Kevin Lansey, Michael Pointl","doi":"10.2166/hydro.2024.207","DOIUrl":"https://doi.org/10.2166/hydro.2024.207","url":null,"abstract":"\u0000 \u0000 The effect of different temporal (from seconds to months) and spatial aggregation scales (from individual users to full urban areas) on water demand behavior has been explored to a limited degree. The effort described here extends those works by evaluating the scale effects of residential water consumption in a unique US data set that covers 10,000 households with a 1-gallon (3.79 L) hourly resolution over 2 years. A preliminary data analysis and a sequential Principal Component Analysis (PCA) is carried out to assess the effect of different temporal (weekly, daily, hourly) and spatial aggregation (individual meters and groups every 10, 100 and 1,000 m) levels on demand. Results show that individual users act very differently from each other, and individual consumer variability is only canceled out when a significant number of households are aggregated. The implications of this finding are assessed from a hydraulic modeling perspective as the spatiotemporal scale of measurements may condition the type of analysis that can be carried out in practice. However, additional work is needed to explore the point at which it may be worth to embrace a micro (per fixture/household) or a macro (per node/network) approach for different purposes.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141122541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Reliable annual runoff prediction is crucial for efficient water resource planning. Therefore, this study proposes a hybrid model based on the combination of sand cat swarm optimization (SCSO), echo state network (ESN), gated recurrent unit (GRU), least squares method (LSM), and Markov chain (MC) models to improve the accuracy of annual runoff prediction. First, correlation analysis is conducted on multifactor data related to runoff to determine the input of the model. Second, the SCSO algorithm is used to optimize the parameters of the ESN and GRU models, and the SCSO-ESN and SCSO-GRU models are established. Next, the LSM is used to couple the prediction results of the SCSO-ESN and SCSO-GRU models to obtain the initial prediction results of the SCSO-ESN-GRU model. Finally, the initial prediction results are corrected for errors using MC to get the final prediction results. Two stations are selected as experimental stations, and five evaluation indicators are chosen to reflect the model's predictive performance at the experimental stations. The results show that the combined prediction model corrected by the MC achieved the optimal prediction performance at both experimental stations. This study emphasizes that using a combination prediction model based on MC correction can significantly improve the accuracy of prediction.
可靠的年径流预测对于高效的水资源规划至关重要。因此，本研究提出了一种基于沙猫群优化（SCSO）、回声状态网络（ESN）、门控循环单元（GRU）、最小二乘法（LSM）和马尔可夫链（MC）模型组合的混合模型，以提高年径流预测的精度。首先，对与径流相关的多因素数据进行相关性分析，以确定模型的输入。其次，利用 SCSO 算法优化 ESN 和 GRU 模型的参数，建立 SCSO-ESN 和 SCSO-GRU 模型。接着，利用 LSM 将 SCSO-ESN 和 SCSO-GRU 模型的预测结果耦合起来，得到 SCSO-ESN-GRU 模型的初始预测结果。最后，利用 MC 对初始预测结果进行误差修正，得到最终预测结果。选取两个站点作为实验站，选取五个评价指标来反映模型在实验站的预测性能。结果表明，经 MC 修正的组合预测模型在两个实验站都达到了最佳预测性能。本研究强调，使用基于 MC 修正的组合预测模型可以显著提高预测精度。
{"title":"A hybrid annual runoff prediction model using echo state network and gated recurrent unit based on sand cat swarm optimization with Markov chain error correction method","authors":"Jun Wang, Wenchuan Wang, Xiao-xue Hu, Miao Gu, Yang-hao Hong, Hong-fei Zang","doi":"10.2166/hydro.2024.038","DOIUrl":"https://doi.org/10.2166/hydro.2024.038","url":null,"abstract":"\u0000 \u0000 Reliable annual runoff prediction is crucial for efficient water resource planning. Therefore, this study proposes a hybrid model based on the combination of sand cat swarm optimization (SCSO), echo state network (ESN), gated recurrent unit (GRU), least squares method (LSM), and Markov chain (MC) models to improve the accuracy of annual runoff prediction. First, correlation analysis is conducted on multifactor data related to runoff to determine the input of the model. Second, the SCSO algorithm is used to optimize the parameters of the ESN and GRU models, and the SCSO-ESN and SCSO-GRU models are established. Next, the LSM is used to couple the prediction results of the SCSO-ESN and SCSO-GRU models to obtain the initial prediction results of the SCSO-ESN-GRU model. Finally, the initial prediction results are corrected for errors using MC to get the final prediction results. Two stations are selected as experimental stations, and five evaluation indicators are chosen to reflect the model's predictive performance at the experimental stations. The results show that the combined prediction model corrected by the MC achieved the optimal prediction performance at both experimental stations. This study emphasizes that using a combination prediction model based on MC correction can significantly improve the accuracy of prediction.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140969372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shravan Kumar S. M., Chidanand Patil, Anamika Yadav, Lavanya Bukke, Laxmana Reddy, Praveen Kumar Sakare
This study delves into the impact of downstream obstruction angles on the discharge coefficient (Cd) over ogee weirs within open channel flows, a critical factor for accurate flow rate predictions in hydraulic engineering. Employing a series of detailed laboratory experiments the influence of various obstruction angles on Cd was scrutinized applying a suite of regression analysis to develop predictive models. The analysis was enriched by considering hydraulic parameters such as flow rate, water level, and weir geometry. Despite the established importance of Cd in hydraulic designs the nuanced effects of downstream obstructions have received limited attention highlighting a critical research gap. The findings highlight a strong correlation between obstruction angles and Cd, with developed regression models demonstrating notable predictive strength. Remarkably the models exhibited varying levels of accuracy, with the Random Forest regressor achieving an exceptionally low root mean square error (RMSE) of 0.005, indicating superior predictive performance. Conversely, traditional models like Decision tree and XG BOOST reflected higher RMSE values of 0.60, suggesting less predictive accuracy in this context. LASSO, Bayesian Ridge, and OMP regressors stood out with an RMSE of zero, denoting perfect predictions under the study's specific conditions.
本研究深入探讨了下游阻塞角对明渠水流中鹅卵石堰排流系数（Cd）的影响，这是水利工程中准确预测流量的关键因素。通过一系列详细的实验室实验，运用一套回归分析方法来建立预测模型，仔细研究了各种阻塞角对 Cd 的影响。考虑到流速、水位和堰体几何形状等水力参数，分析结果更加丰富。尽管 Cd 在水力设计中的重要性已得到证实，但下游障碍物的细微影响却未得到足够重视，这凸显了一个关键的研究缺口。研究结果凸显了障碍物角度与 Cd 之间的密切联系，开发的回归模型显示出显著的预测能力。值得注意的是，这些模型表现出不同程度的准确性，其中随机森林回归模型的均方根误差 (RMSE) 特别低，仅为 0.005，显示出卓越的预测性能。相反，决策树和 XG BOOST 等传统模型的均方根误差值较高，达到 0.60，表明在这种情况下预测准确性较低。LASSO、贝叶斯岭和 OMP 回归因子的 RMSE 值为零，表明在研究的特定条件下预测结果完美。
{"title":"Impact of downstream obstructions on ogee weir efficiency: a regression analysis","authors":"Shravan Kumar S. M., Chidanand Patil, Anamika Yadav, Lavanya Bukke, Laxmana Reddy, Praveen Kumar Sakare","doi":"10.2166/hydro.2024.029","DOIUrl":"https://doi.org/10.2166/hydro.2024.029","url":null,"abstract":"\u0000 \u0000 This study delves into the impact of downstream obstruction angles on the discharge coefficient (Cd) over ogee weirs within open channel flows, a critical factor for accurate flow rate predictions in hydraulic engineering. Employing a series of detailed laboratory experiments the influence of various obstruction angles on Cd was scrutinized applying a suite of regression analysis to develop predictive models. The analysis was enriched by considering hydraulic parameters such as flow rate, water level, and weir geometry. Despite the established importance of Cd in hydraulic designs the nuanced effects of downstream obstructions have received limited attention highlighting a critical research gap. The findings highlight a strong correlation between obstruction angles and Cd, with developed regression models demonstrating notable predictive strength. Remarkably the models exhibited varying levels of accuracy, with the Random Forest regressor achieving an exceptionally low root mean square error (RMSE) of 0.005, indicating superior predictive performance. Conversely, traditional models like Decision tree and XG BOOST reflected higher RMSE values of 0.60, suggesting less predictive accuracy in this context. LASSO, Bayesian Ridge, and OMP regressors stood out with an RMSE of zero, denoting perfect predictions under the study's specific conditions.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141022770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Long-term inflow forecasting is extremely important for reasonable dispatch schedules of hydropower stations and efficient utilization plans of water resources. In this paper, a novel forecast framework, meteorological data long short-term memory neural network (M-LSTM), which uses the meteorological dataset as input and adopts LSTM, is proposed for monthly inflow forecasting. First, the meteorological dataset, which provides more effective information for runoff prediction, is obtained b
{"title":"Long-term inflow forecast using meteorological data based on long short-term memory neural networks","authors":"Hongye Zhao, Shengli Liao, Yitong Song, Zhou Fang, Xiangyu Ma, BinBin Zhou","doi":"10.2166/hydro.2024.196","DOIUrl":"https://doi.org/10.2166/hydro.2024.196","url":null,"abstract":"<div><div data- reveal-group-><div><img alt=\"graphic\" data-src=\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/jh/26/5/10.2166_hydro.2024.196/1/m_hydro-d-23-00196gf01.png?Expires=1720086512&Signature=VNdAXZpk4liIQy-2g49DLM1buWS0bh-Rlxj7fxEFDcTtBAlBqRR3j4J0qq8I00odrFVO3Q1IxOK8NyvEQ4tsP~ASPku6wrd9HFUJuePzyRsGV5ZhMQYLcRV4Xm8-Y4mXjlNJzvgQSuSlDvTRS-NbMWjOFQbNM7KjAc1SIuxAq6YwzV2iCTgUDUy-WEv1Fq5otEyLiHLKd1sW2gBBnS6M-f2cS1hiAoe02YY7zOTAlR2VXXBASuYbRp80AOeMkihdC1shOj7VM5T4pIMpVoajlP0-YsehwU5SE88fxAKRnlxwt9ZigvLdcTda4~UmX8G~wIENx6gpLiwreYd5fIVRVg__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\" path-from-xml=\"hydro-d-23-00196gf01.tif\" src=\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/jh/26/5/10.2166_hydro.2024.196/1/m_hydro-d-23-00196gf01.png?Expires=1720086512&Signature=VNdAXZpk4liIQy-2g49DLM1buWS0bh-Rlxj7fxEFDcTtBAlBqRR3j4J0qq8I00odrFVO3Q1IxOK8NyvEQ4tsP~ASPku6wrd9HFUJuePzyRsGV5ZhMQYLcRV4Xm8-Y4mXjlNJzvgQSuSlDvTRS-NbMWjOFQbNM7KjAc1SIuxAq6YwzV2iCTgUDUy-WEv1Fq5otEyLiHLKd1sW2gBBnS6M-f2cS1hiAoe02YY7zOTAlR2VXXBASuYbRp80AOeMkihdC1shOj7VM5T4pIMpVoajlP0-YsehwU5SE88fxAKRnlxwt9ZigvLdcTda4~UmX8G~wIENx6gpLiwreYd5fIVRVg__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\"/><div>View largeDownload slide</div></div></div><div content- data-reveal=\"data-reveal\"><div><img alt=\"graphic\" data-src=\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/jh/26/5/10.2166_hydro.2024.196/1/m_hydro-d-23-00196gf01.png?Expires=1720086512&Signature=VNdAXZpk4liIQy-2g49DLM1buWS0bh-Rlxj7fxEFDcTtBAlBqRR3j4J0qq8I00odrFVO3Q1IxOK8NyvEQ4tsP~ASPku6wrd9HFUJuePzyRsGV5ZhMQYLcRV4Xm8-Y4mXjlNJzvgQSuSlDvTRS-NbMWjOFQbNM7KjAc1SIuxAq6YwzV2iCTgUDUy-WEv1Fq5otEyLiHLKd1sW2gBBnS6M-f2cS1hiAoe02YY7zOTAlR2VXXBASuYbRp80AOeMkihdC1shOj7VM5T4pIMpVoajlP0-YsehwU5SE88fxAKRnlxwt9ZigvLdcTda4~UmX8G~wIENx6gpLiwreYd5fIVRVg__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\" path-from-xml=\"hydro-d-23-00196gf01.tif\" src=\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/jh/26/5/10.2166_hydro.2024.196/1/m_hydro-d-23-00196gf01.png?Expires=1720086512&Signature=VNdAXZpk4liIQy-2g49DLM1buWS0bh-Rlxj7fxEFDcTtBAlBqRR3j4J0qq8I00odrFVO3Q1IxOK8NyvEQ4tsP~ASPku6wrd9HFUJuePzyRsGV5ZhMQYLcRV4Xm8-Y4mXjlNJzvgQSuSlDvTRS-NbMWjOFQbNM7KjAc1SIuxAq6YwzV2iCTgUDUy-WEv1Fq5otEyLiHLKd1sW2gBBnS6M-f2cS1hiAoe02YY7zOTAlR2VXXBASuYbRp80AOeMkihdC1shOj7VM5T4pIMpVoajlP0-YsehwU5SE88fxAKRnlxwt9ZigvLdcTda4~UmX8G~wIENx6gpLiwreYd5fIVRVg__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\"/><div>View largeDownload slide</div></div><i> </i><span>Close modal</span></div></div><p>Long-term inflow forecasting is extremely important for reasonable dispatch schedules of hydropower stations and efficient utilization plans of water resources. In this paper, a novel forecast framework, meteorological data long short-term memory neural network (M-LSTM), which uses the meteorological dataset as input and adopts LSTM, is proposed for monthly inflow forecasting. First, the meteorological dataset, which provides more effective information for runoff prediction, is obtained b","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141188388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rapid urbanization and population growth are placing more demands on the world's natural water resources. New infrastructures are increasing the degree of surface sealing as well as the tendency for urban flooding and water quality degradation. These problems can be counteracted by nature-based solutions (NBS) for urban drainage in developed countries mostly having a temperate climate. Hence, there is a need to develop similar sustainable measures for tropical regions as currently there are no guidelines available. In this study, the multi-criteria decision analysis (MCDA) approach was utilized to identify the best site for NBS in the Asian Institute of Technology (AIT) in Bangkok, Thailand. Then, PCSWMM software was used to develop a numerical model. It was found that MCDA approach is an appropriate approach to determine the best site for NBS implementation considering different aspects including economic, environmental, and technical ones. The results strongly suggested that Site-1 is a suitable alternative to implement NBS in the AIT campus. It was found that a bioretention system can reduce runoff volume by at least 14% and pollutants by at least 14–20%, respectively. The present study will provide a guideline for site selection and development of the NBS model for urban water management in a tropical climate.
{"title":"A community-scale study on nature-based solutions (NBS) for stormwater management under tropical climate: The case of the Asian Institute of Technology (AIT), Thailand","authors":"Fahad Ahmed, Ho Loc, M. S. Babel, Juergen Stamm","doi":"10.2166/hydro.2024.288","DOIUrl":"https://doi.org/10.2166/hydro.2024.288","url":null,"abstract":"\u0000 \u0000 Rapid urbanization and population growth are placing more demands on the world's natural water resources. New infrastructures are increasing the degree of surface sealing as well as the tendency for urban flooding and water quality degradation. These problems can be counteracted by nature-based solutions (NBS) for urban drainage in developed countries mostly having a temperate climate. Hence, there is a need to develop similar sustainable measures for tropical regions as currently there are no guidelines available. In this study, the multi-criteria decision analysis (MCDA) approach was utilized to identify the best site for NBS in the Asian Institute of Technology (AIT) in Bangkok, Thailand. Then, PCSWMM software was used to develop a numerical model. It was found that MCDA approach is an appropriate approach to determine the best site for NBS implementation considering different aspects including economic, environmental, and technical ones. The results strongly suggested that Site-1 is a suitable alternative to implement NBS in the AIT campus. It was found that a bioretention system can reduce runoff volume by at least 14% and pollutants by at least 14–20%, respectively. The present study will provide a guideline for site selection and development of the NBS model for urban water management in a tropical climate.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140657097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：