Hasil untuk "River, lake, and water-supply engineering (General)"

Muhamad Demirel Prasetya, Doddi Yudianto, Willy

Bogor City, with a population of approximately 1,064,000 in 2022 and an annual growth rate of 2.01%, faces increasing pressure to meet housing demands. To accommodate this need, one modern residential development in the area has undergone extensive expansion. A previous assessment indicated that the development of a 10.75-hectare residential area in this neighborhood could increase peak discharge by approximately 24.74% for the 2-year return period and 16.67% for the 5-year return period. Based on these findings, this study aims to evaluate the effectiveness of Low Impact Development (LID) measures, specifically the use of rain barrels, in mitigating the hydrological impacts of land-use changes. Simulations were conducted using the Storm Water Management Model (SWMM) to: (1) analyze changes in peak discharge and runoff volume before and after development; (2) design the residential drainage system while testing different rain barrel capacities; and (3) evaluate the effectiveness of rain barrels in reducing peak discharge and runoff. Simulation results indicate that the installation of two rain barrels per household, each with a diameter of 1.41 m and a height of 1.19 m, can reduce peak discharge by 19.66%, approaching pre-development conditions. However, total runoff volume remains higher than baseline levels, suggesting that additional LID strategies are necessary for more comprehensive flood mitigation. These findings underscore the potential of rain barrels as an effective tool for urban runoff management and provide practical guidance for optimizing their implementation in similar residential developments

CHEN Jia-wen, ZHU Xin, TANG Zheng-yang, SHEN Ke-yan, CHEN Xiao-lin, QIN Hui

[Objective] To address the shortcomings of differential evolution (DE) algorithms in cascade reservoir optimization, this study proposes an intelligent algorithm that couples reinforcement learning and differential evolution (RLDE). [Methods] The RLDE algorithm improved the standard DE algorithm through three key strategies: chaotic mapping to enhance initial solution quality, Q-learning-based adaptive parameter adjustment, and a variable step-size strategy. Specifically, (1) chaotic mapping enhanced the initial solution quality. Logistic mapping with the best experimental performance was selected and applied to the population initialization of the RLDE algorithm. (2) The adaptive parameter adjustment was conducted based on the Q-learning algorithm. (3) A variable step-size strategy was designed for the actions in the Q-table, where the precision of action rows gradually increased with the number of iterations. To validate the feasibility and effectiveness of the RLDE algorithm, it was applied to optimize the power generation scheduling model for four major cascade reservoirs (Wudongde, Baihetan, Xiluodu, and Xiangjiaba) on the lower Jinsha River. [Results] (1) The chaotic initialization strategy effectively improved the initial solution quality. The adaptive parameter adjustment strategy based on the Q-learning algorithm enabled the algorithm to continuously adapt by receiving feedback from the environment. This process enhanced population diversity, greatly mitigated problems such as premature convergence or population evolutionary stagnation found in the traditional DE algorithm, thereby improving optimization performance. The variable step-size strategy allowed the algorithm to better respond to environmental feedback, further strengthening the optimization capability of the algorithm. (2) Compared with the traditional DE algorithm and adaptive genetic algorithm, the RLDE algorithm achieved an average annual power generation increase of 2.02% and 2.06%, respectively, under three typical inflow scenarios (wet, normal, and dry). Moreover, the average standard deviation of the proposed algorithm after multiple runs was reduced by an average of 729 million kW·h compared with the traditional DE algorithm, and by 844 million kW·h compared with the adaptive genetic algorithm. [Conclusions] This study proposes an intelligent algorithm that integrates reinforcement learning with differential evolution, effectively addressing issues such as premature convergence and search stagnation in the traditional DE algorithm. The proposed method provides an efficient and reliable solution for the optimal scheduling of cascade reservoirs.

TONG Yu, JIA Jinrui, HOU Jingming et al.

In order to explore the desilting effects of different channel desilting schemes, a hydrodynamic model accelerated by a graphic process unit (GPU) was used to construct a flood evolution model for the study area and simulate the flood inundation situation of the channels in the study area after implementing four desilting schemes at different return periods. The results show that: ① The schemes in terms of flood inundation alleviation and desilting excavation volume in descending order are as follows: full-section scheme, combination scheme, only floodplain scheme, and only thalweg scheme. ② The inundated area of each scheme decreases by 4.20%~21.68%, 1.57%~13.85%, 6.66%~43.6%, and 5.63%~39.7%, respectively. In terms of inundated area reduction rate, the combination scheme has an average reduction of 2.19% compared with the full-section scheme at different return periods, but the excavation volume of the full-section scheme is 21.89% higher than that of the combination scheme. ③ The ratio of the inundated area reduction rate to the excavation volume is used to represent the ability to reduce the inundated area per unit excavation volume. The ability of each desilting scheme to reduce inundated area is 7.34%~12.49%, 2.75%~7.98%, 8.86%~19.08%, and 9.13%~21.18%, respectively. The combination scheme has the strongest ability to reduce the inundated area at different return periods. Therefore, the combination scheme is determined as the comprehensive optimal scheme among the four desilting schemes. This study can provide a reference for channel desilting, flood control, and disaster reduction.

Pawan Adhikari, Satish Raj Regmi, Hari Ram Shrestha

Glacial Lake Outburst Floods (GLOFs) are one of the most devastating climate change induced hazards. Existing remote monitoring approaches often prioritise maximising spatial coverage to train generalistic models or rely on optical imagery hampered by persistent cloud coverage. This paper presents an end-to-end, automated deep learning pipeline for the targeted monitoring of high-risk Himalayan glacial lakes using time-series Sentinel-1 SAR. We introduce a "temporal-first" training strategy, utilising a U-Net with an EfficientNet-B3 backbone trained on a curated dataset of a cohort of 4 lakes (Tsho Rolpa, Chamlang Tsho, Tilicho and Gokyo Lake). The model achieves an IoU of 0.9130 validating the success and efficacy of the "temporal-first" strategy required for transitioning to Early Warning Systems. Beyond the model, we propose an operational engineering architecture: a Dockerised pipeline that automates data ingestion via the ASF Search API and exposes inference results via a RESTful endpoint. This system shifts the paradigm from static mapping to dynamic and automated early warning, providing a scalable architectural foundation for future development in Early Warning Systems.

Hailekiros Tadesse, Desta Berhe Sbhatu, Gebreselema Gebreyohannes

Mekelle is one of the Ethiopian cities suitable for urban and peri-urban agriculture for cultivating leafy vegetables using wastewater. The consumption of unprocessed and processed leafy vegetables is also very high in the city. Wastewater samples collected from four experimental sites (ESs) in Hadnet district of the city had higher concentrations in 4 (i.e., Cd, Cr, Cu, and Mn) of the 10 heavy metals tested than the permissible limit established by pertinent standards. Spring water samples collected from another site called Kallamino, designated as a comparison site, also had higher concentrations in 4 (i.e., Al, As, Cd, and Cu) of the 10 heavy metals tested. However, the leafy vegetables grown in the least contaminated ES had higher concentrations in 6-7 of the 10 heavy metals tested. The lettuce and spinach samples had elevated concentrations of As and Al, respectively. The wastewater used to irrigate vegetable farms in Hadnet district is not safe enough. More importantly, the soils of the farms might have accumulated far more heavy metals. The cultivated lettuce, spinach, and cauliflower are highly contaminated. Thus, the use of wastewater for irrigating urban and peri-urban farms needs to be regulated. HIGHLIGHTS Tests of wastewaters used in urban agriculture for heavy metal contents.; Tests of three leafy vegetables irrigated with wastewater for heavy metal contents.; Contamination of the three leafy vegetables with high level of 6 to 7 heavy metals tested.;

Yuan Huang, Yinan Chen, Xiangping Chen et al.

The rapid development of deep learning techniques, improved computational power, and the availability of vast training data have led to significant advancements in pre-trained models and large language models (LLMs). Pre-trained models based on architectures such as BERT and Transformer, as well as LLMs like ChatGPT, have demonstrated remarkable language capabilities and found applications in Software engineering. Software engineering tasks can be divided into many categories, among which generative tasks are the most concern by researchers, where pre-trained models and LLMs possess powerful language representation and contextual awareness capabilities, enabling them to leverage diverse training data and adapt to generative tasks through fine-tuning, transfer learning, and prompt engineering. These advantages make them effective tools in generative tasks and have demonstrated excellent performance. In this paper, we present a comprehensive literature review of generative tasks in SE using pre-trained models and LLMs. We accurately categorize SE generative tasks based on software engineering methodologies and summarize the advanced pre-trained models and LLMs involved, as well as the datasets and evaluation metrics used. Additionally, we identify key strengths, weaknesses, and gaps in existing approaches, and propose potential research directions. This review aims to provide researchers and practitioners with an in-depth analysis and guidance on the application of pre-trained models and LLMs in generative tasks within SE.

Maike F. Holthuijzen, Robert B. Gramacy, Cayelan C. Carey et al.

We present a novel forecasting framework for lake water temperature, which is crucial for managing lake ecosystems and drinking water resources. The General Lake Model (GLM) has been previously used for this purpose, but, similar to many process-based simulation models, it: requires a large number of inputs, many of which are stochastic; presents challenges for uncertainty quantification (UQ); and can exhibit model bias. To address these issues, we propose a Gaussian process (GP) surrogate-based forecasting approach that efficiently handles large, high-dimensional data and accounts for input-dependent variability and systematic GLM bias. We validate the proposed approach and compare it with other forecasting methods, including a climatological model and raw GLM simulations. Our results demonstrate that our bias-corrected GP surrogate (GPBC) can outperform competing approaches in terms of forecast accuracy and UQ up to two weeks into the future.

Tim Hageman, Jessica Mejía, Ravindra Duddu et al.

Full thickness crevasses can transport water from the glacier surface to the bedrock where high water pressures can open kilometre-long cracks along the basal interface, which can accelerate glacier flow. We present a first computational modelling study that describes time-dependent fracture propagation in an idealised glacier causing rapid supraglacial lake drainage. A novel two-scale numerical method is developed to capture the elastic and viscoelastic deformations of ice along with crevasse propagation. The fluid-conserving thermo-hydro-mechanical model incorporates turbulent fluid flow and accounts for melting/refreezing in fractures. Applying this model to observational data from a 2008 rapid lake drainage event indicates that viscous deformation exerts a much stronger control on hydrofracture propagation compared to thermal effects. This finding contradicts the conventional assumption that elastic deformation is adequate to describe fracture propagation in glaciers over short timescales (minutes to several hours) and instead demonstrates that viscous deformation must be considered to reproduce observations of lake drainage rate and local ice surface elevation change. As supraglacial lakes continue expanding inland and as Greenland Ice Sheet temperatures become warmer than -8 degree C, our results suggest rapid lake drainages are likely to occur without refreezing, which has implications for the rate of sea level rise.

Matthew Rosenzweig, Sylvia Serfaty

We study so-called supercritical mean-field limits of systems of trapped particles moving according to Newton's second law with either Coulomb/super-Coulomb or regular interactions, from which we derive a $\mathsf{d}$-dimensional generalization of the Lake equation, which coincides with the incompressible Euler equation in the simplest setting, for monokinetic data. This supercritical mean-field limit may also be interpreted as a combined mean-field and quasineutral limit, and our assumptions on the rates of these respective limits are shown to be optimal. Our work provides a mathematical basis for the universality of the Lake equation in this scaling limit -- a new observation -- in the sense that the dependence on the interaction and confinement is only through the limiting spatial density of the particles. Our proof is based on a modulated-energy method and takes advantage of regularity theory for the obstacle problem for the fractional Laplacian.

Madhava Krishna, Bhagesh Gaur, Arsh Verma et al.

The creation of a Software Requirements Specification (SRS) document is important for any software development project. Given the recent prowess of Large Language Models (LLMs) in answering natural language queries and generating sophisticated textual outputs, our study explores their capability to produce accurate, coherent, and structured drafts of these documents to accelerate the software development lifecycle. We assess the performance of GPT-4 and CodeLlama in drafting an SRS for a university club management system and compare it against human benchmarks using eight distinct criteria. Our results suggest that LLMs can match the output quality of an entry-level software engineer to generate an SRS, delivering complete and consistent drafts. We also evaluate the capabilities of LLMs to identify and rectify problems in a given requirements document. Our experiments indicate that GPT-4 is capable of identifying issues and giving constructive feedback for rectifying them, while CodeLlama's results for validation were not as encouraging. We repeated the generation exercise for four distinct use cases to study the time saved by employing LLMs for SRS generation. The experiment demonstrates that LLMs may facilitate a significant reduction in development time for entry-level software engineers. Hence, we conclude that the LLMs can be gainfully used by software engineers to increase productivity by saving time and effort in generating, validating and rectifying software requirements.

Gopal Chandra Ghosh, Tapos Kumar Chakraborty, Nipa Shekder et al.

This study investigated the groundwater quality and its associated human health risks in the urban and peri-urban areas of Jashore, Bangladesh, where groundwater samples were collected from 67 randomly selected tube wells. The concentration of arsenic, iron, and manganese was analyzed by atomic absorption spectroscopy (AAS). The water quality index indicates that about 89 and 43% of groundwater samples are not consumable for the urban and peri-urban areas, respectively. All of the source water is significantly contaminated with Escherichia coli for urban (31 ± 17.77 CFU/100 mL) and peri-urban areas (76.12 ± 35.17 CFU/100 mL), where about 67 and 57% of water source has intermediate and high microbial risk of E. coli for urban and peri-urban areas, respectively. Children and adults face unacceptable non-carcinogenic health risks for the urban area (4.13–10.67 for adults; 9.65–24.91 for children) and peri-urban area (1.05–5.58 for adults; 2.46–13.03 for children) via oral ingestion. Both groups (e.g. children = 4.25E-03 to 1.10E-02 and adult = 1.82E-03 to 4.71E-03 for urban regions; children = 1E-03 to 5E-03 and adult = 4.29E0-04 to 2.14E-03 for peri-urban regions) face undesirable carcinogenic risks from arsenic. In addition, children are suspected to have 2.33 times higher non-carcinogenic and carcinogenic health risks than adults. HIGHLIGHTS About 89 and 43% of groundwater samples are not drinkable for the urban and peri-urban areas, respectively.; About 67 and 57% of water source has intermediate and high microbial risk by Escherichia coli for urban and peri-urban areas, respectively.; Children are suspected to have 2.33 times higher non-carcinogenic and carcinogenic health risks than adults.;

Trieu T. Hieu, Ta Q. Chieu, Dinh N. Quang et al.

Abstract Time‐series water level prediction during natural disasters, for example, typhoons and storms, is crucial for both flood control and prevention. Utilizing data‐driven models that harness deep learning (DL) techniques has emerged as an attractive and effective approach to water level prediction. This paper proposed an innovative data‐driven methodology using DL network architectures of Gated Recurrent Unit (GRU), Long Short‐Term Memory (LSTM), and Bidirectional Long‐Short Term Memory (Bi‐LSTM) to predict the water level at the Le Thuy station in the Kien Giang River. These models were implemented and validated based on hourly rainfall and water level observations at meteo‐hydrological stations. Three combinations of input variables with different time leads and time lags were established to evaluate the forecast capability of three proposed models by using five metrics, that is, R2, MAE, RMSE, Max Error Value, and Max Error Time. The results revealed that the LSTM model outperformed the Bi‐LSTM and GRU models, when water level and rainfall observations for one‐time lag at three stations were used to predict the water level at the Le Thuy station with 1‐h time lead, with the five metrics registering at 0.999; 3.6 cm; 2.6 cm; 12.9 cm; and −1 h, respectively.

CAI Qilin, XU Danli, WANG Zhaoli et al.

The purpose of this study is to discuss the encounter problem between strong wind and rainstorm in the coastal reservoirs.The Dalongdong Reservoir,located in Taishan of Guangdong Province,was taken as a study case.The P-III,Gamma and GEV functions were used to fit the marginal distributions between three groups of rainfall extremes and one group of maximum wind speed sequence respectively.The values of different return period of the maximum wind speed and maximum rainfall in different periods were also analyzed by Archimedean Copula and Kendall function.The return periods of ‘or’,‘and’,and Kendall return period as well as the most likely design values of wind speed and rainfall were calculated.The results showed that the annual maximum daily rainfall showed a significant uptrend (p<0.05) while the yearly maximum wind speed showed a downtrend.The correlation between wind speed and rainstorm sequence was extremely weak while maximum wind speed had encountered the annual maximum rainfall during the analysis period.The values of wind speed and rainfall based on ‘or’,‘and’,and Kendall return period were smaller than that of univariate marginal distribution,while the values of “or” were larger than those with univariate marginal distribution.The elevation of main dam based on “or” joint distribution of 100a return period was larger than the actual elevation.Accordingly,this study suggested considering the encounter between strong wind and rainstorm when designing the elevation of reservoir dam in the future.

Xuanzhe Liu, Diandian Gu, Zhenpeng Chen et al.

Deep learning (DL) has become a key component of modern software. In the "big model" era, the rich features of DL-based software substantially rely on powerful DL models, e.g., BERT, GPT-3, and the recently emerging GPT-4, which are trained on the powerful cloud with large datasets. Hence, training effective DL models has become a vital stage in the whole software lifecycle. When training deep learning models, especially those big models, developers need to parallelize and distribute the computation and memory resources amongst multiple devices in the training process, which is known as distributed deep learning training, or distributed training for short. However, the unique challenges that developers encounter in distributed training process have not been studied in the software engineering community. Given the increasingly heavy dependence of current DL-based software on distributed training, this paper aims to fill in the knowledge gap and presents the first comprehensive study on developers' issues in distributed training. To this end, we analyze 1,131 real-world developers' issues about using these frameworks reported on Stack Overflow and GitHub. We construct a fine-grained taxonomy consisting of 30 categories regarding the fault symptoms and summarize common fix patterns for different symptoms. Based on the results, we suggest actionable implications on research avenues that can potentially facilitate the distributed training to develop DL-based software, such as focusing on the frequent and common fix patterns when designing testing or debugging tools, developing efficient testing and debugging techniques for communication configuration along with the synthesis of network configuration analysis, designing new multi-device checkpoint-and-replay techniques to help reproduction, and designing serverless APIs for cloud platforms.

Bo Chen, Chunying Ma, Witold F. Krajewski et al.

The peak-discharge and drainage area power-law relation has been widely used in regional flood frequency analysis for more than a century. The coefficients and can be obtained by nonlinear or log-log linear regression. To illustrate the deficiencies of applying log-transformation in peak-discharge power-law analyses, we studied 52 peak-discharge events observed in the Iowa River Basin in the United States from 2002 to 2013. The results show that: (1) the estimated scaling exponents by the two methods are remarkably different; (2) for more than 80% of the cases, the power-law relationships obtained by log-log linear regression produce larger prediction errors of peak discharge in the arithmetic scale than that predicted by nonlinear regression; and (3) logarithmic transformation often fails to stabilize residuals in the arithmetic domain, it assigns higher weight to data points representing smaller peak discharges and drainage areas, and it alters the visual appearance of the scatter in the data. The notable discrepancies in the scaling parameters estimated by the two methods and the undesirable consequences of logarithmic transformation raise caution. When conducting peak-discharge scaling analysis, especially for prediction purposes, applying nonlinear regression on the arithmetic scale to estimate the scaling parameters is a better alternative.

Nino Krvavica, Igor Ružić

Understanding the response of estuaries to sea-level rise is crucial in developing a suitable mitigation and climate change adaptation strategy. This study investigates the impacts of rising sea levels on salinity intrusion in salt-wedge estuaries. The sea-level rise impacts are assessed in idealized estuaries using simple expressions derived from a two-layer hydraulic theory, and in the Neretva River Estuary in Croatia using a two-layer time-dependent model. The assessment is based on three indicators - the salt-wedge intrusion length, the seawater volume, and the river inflows needed to restore the baseline intrusion. The potential SLR was found to increase all three considered indicators. Theoretical analysis in idealized estuaries suggests that shallower estuaries are more sensitive to SLR. Numerical results for the Neretva River Estuary showed that SLR may increase salt-wedge intrusion length, volume, and corrective river inflow. However, the results are highly non-linear because of the channel geometry, especially for lower river inflows. A theoretical assessment of channel bed slope impacts on limiting a potential intrusion is therefore additionally discussed. This findings emphasize the need to use several different indicators when assessing SLR impacts.

A. Edeoja, J. Tarka

Martin Shepperd

I welcome the contribution from Falessi et al. [1] hereafter referred to as F++ , and the ensuing debate. Experimentation is an important tool within empirical software engineering, so how we select participants is clearly a relevant question. Moreover as F++ point out, the question is considerably more nuanced than the simple dichotomy it might appear to be at first sight. This commentary is structured as follows. In Section 2 I briefly summarise the arguments of F++ and comment on their approach. Next, in Section 3, I take a step back to consider the nature of representativeness in inferential arguments and the need for careful definition. Then I give three examples of using different types of participant to consider impact. I conclude by arguing, largely in agreement with F++, that the question of whether student participants are representative or not depends on the target population. However, we need to give careful consideration to defining that population and, in particular, not to overlook the representativeness of tasks and environment. This is facilitated by explicit description of the target populations.