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

Xuanyu Shi, Zhi-Wang Geng, Mingming Peng et al.

CHEN Quanfu, LI Xungui

In recent years, significant changes in hydrological and engineering conditions have been observed in the Yujiang River Basin, and climate anomalies have increased flood control pressures. The distribution of rainstorms and floods within the basin varies significantly, which makes it insufficient to describe flood risk solely from the perspective of flood peak or flood volume, and further study is necessitated. This paper takes the runoff data (1947—2015) from the Guigang Station as the research object and extracts flood sequences through the annual maximum (AM) method and the peaks over threshold (POT) method. Three common Archimedean Copula functions are employed to establish the multivariate joint distribution of flood variables, and their joint return periods and co-occurrence return periods are compared and analyzed. Results show that the optimal marginal distributions respectively obtained through the AM method and the POT method differ, with Clayton Copula being the optimal fitting function for the AM method and Frank Copula for the POT method. While the joint and co-occurrence return periods calculated through both methods show little difference, under the same return period, the POT method yields more reliable results for flood peak and flood volume. The findings can provide a valuable reference for decision-making in aspects of flood mitigation, engineering construction, and management in the Yujiang River Basin.

Vladyslav Bulhakov, Giordano d'Aloisio, Claudio Di Sipio et al.

The introduction of large language models (LLMs) has enhanced automation in software engineering tasks, including in Model Driven Engineering (MDE). However, using general-purpose LLMs for domain modeling has its limitations. One approach is to adopt fine-tuned models, but this requires significant computational resources and can lead to issues like catastrophic forgetting. This paper explores how hyperparameter tuning and prompt engineering can improve the accuracy of the Llama 3.1 model for generating domain models from textual descriptions. We use search-based methods to tune hyperparameters for a specific medical data model, resulting in a notable quality improvement over the baseline LLM. We then test the optimized hyperparameters across ten diverse application domains. While the solutions were not universally applicable, we demonstrate that combining hyperparameter tuning with prompt engineering can enhance results across nearly all examined domain models.

Timo Kehrer, Robert Haines, Guido Juckeland et al.

Anecdotal evidence suggests that Research Software Engineers (RSEs) and Software Engineering Researchers (SERs) often use different terminologies for similar concepts, creating communication challenges. To better understand these divergences, we have started investigating how SE fundamentals from the SER community are interpreted within the RSE community, identifying aligned concepts, knowledge gaps, and areas for potential adaptation. Our preliminary findings reveal opportunities for mutual learning and collaboration, and our systematic methodology for terminology mapping provides a foundation for a crowd-sourced extension and validation in the future.

Lekshmi Murali Rani, Richard Berntsson Svensson, Robert Feldt

The integration of AI for Requirements Engineering (RE) presents significant benefits but also poses real challenges. Although RE is fundamental to software engineering, limited research has examined AI adoption in RE. We surveyed 55 software practitioners to map AI usage across four RE phases: Elicitation, Analysis, Specification, and Validation, and four approaches for decision making: human-only decisions, AI validation, Human AI Collaboration (HAIC), and full AI automation. Participants also shared their perceptions, challenges, and opportunities when applying AI for RE tasks. Our data show that 58.2% of respondents already use AI in RE, and 69.1% view its impact as positive or very positive. HAIC dominates practice, accounting for 54.4% of all RE techniques, while full AI automation remains minimal at 5.4%. Passive AI validation (4.4 to 6.2%) lags even further behind, indicating that practitioners value AI's active support over passive oversight. These findings suggest that AI is most effective when positioned as a collaborative partner rather than a replacement for human expertise. It also highlights the need for RE-specific HAIC frameworks along with robust and responsible AI governance as AI adoption in RE grows.

Daniel Mendez, Paris Avgeriou, Marcos Kalinowski et al.

Empirical Software Engineering has received much attention in recent years and became a de-facto standard for scientific practice in Software Engineering. However, while extensive guidelines are nowadays available for designing, conducting, reporting, and reviewing empirical studies, similar attention has not yet been paid to teaching empirical software engineering. Closing this gap is the scope of this edited book. In the following editorial introduction, we, the editors, set the foundation by laying out the larger context of the discipline for a positioning of the remainder of this book.

Ran Gao, Juncai Gao, Linlin Chu

The transport–diffusion problem of point-source solutes in water environmental flows is an important issue in environmental fluid mechanics, with significant theoretical and practical implications for sustainable development and the ecological management and environmental protection of water. This study presents a model for instantaneously released multi-point-source solutes, utilizing the separation of variables method and Duhamel’s principle to solve classical mathematical physics equations. The zeroth-order and first-order concentration moment equations, which are crucial for predicting the cross-sectional average concentration of instantaneously released point-source solutes, are systematically addressed. The accuracy of the analytical results is confirmed by comparing them with the relevant literature. Furthermore, a general discussion is provided based on the study’s findings (including an ideal physical model of Couette flow), and an analytical solution (a recursive relationship) for higher-order concentration moments is deduced. Finally, this study quantitatively discusses downstream environmental ecological effects by examining the movement of released point-source solute centroids in the river, illustrating that the time needed for the released point-source solute to have an environmental–ecological impact downstream of the river is dependent on the initial release location. Under the specified engineering parameters, for the release location at the bottom boundary point of the channel (z0 = 0 m), the midpoint (z0 = 5 m), and the water-surface point (z0 = 10 m), the time for additional displacement of released solute centroid to reach the asymptotic value in three cases is 4.0 h, 1.0 h, and 4.5 h; the asymptotic values are approximately −0.087 km, 0.012 km, and 0.055 km, respectively. These results not only correspond with the conclusions of previous research but also provide a more extensive range of numerical results. This study establishes the groundwork for theoretical research on more complex water environmental flow models and provides a theoretical basis for engineering computations aimed at contributing to the environmental management of rivers and lakes.

Marcelo Macedo Guimarães, Mariluce Paes de Souza

Objective: The general objective of this research is to analyze the integration of forests into the water-energy-food nexus in a Conservation Unit in the Brazilian Amazon, aiming to verify how traditional populations, in whose areas they inhabit, use two natural and non-wood resources for their survival.   Theoretical Framework: The approach to the water-energy-food nexus emerged in the last decade around concerns such as global trends in population growth, economic growth and climate changes that place pressure on water, energy, food and forest resources.   Method: The adopted methodology uses a qualitative approach, given the importance of understanding how the water, energy, food and forest resources in the Lago do Cuniã Extractive Reserve are explored. An exploratory-descriptive type of research, with a central concern in carrying out a preparatory analysis of a known context, meanwhile little explored in the Amazon. The collection of data was carried out by means of interviews with the application of questionnaire and application of non-participant observation techniques.   Results and Discussion: The results demonstrate that studies with a nexus approach with four elements provide no understanding of the exploration of sustainable development in the environment of conservation units. Furthermore, the study reveals that maintaining forest resources improves water quality not only, but also controls erosion and resilience against droughts and floods; Contributing to the energy supply, in addition, contributes to the safety of feeding the residents of the conservation unit.   Research Implications: For public policy makers, the results demonstrate the need for management practices for the conservation of forests, protection of the quantity and quality of existing rivers and lakes in the conservation unit and protection of aquatic and agricultural ecosystems.   Originality/Value: The originality of the article is in the analysis of the role of addressing water, energy and food within an area of ​​environmental protection in the Amazon, and by including the element forest it was possible to find out its triangulation with the other elements and their implications for economic, social and economic factors. you set the mood.

V. Repin, K. V. Varfolomeeva, A. Biblin et al.

Sites of peaceful nuclear explosions pose a potential radiation hazard to the territories of the Russian Federation, primarily due to the possible release of radioactivity from the explosion cavity into aquifers and onto the earth's surface. Therefore, it is essential to conduct regular monitoring of anthropogenic radionuclides in drinking water sources in settlements located near the sites of peaceful nuclear explosions. Tritium serves as an indicator of the potential release of other anthropogenic radionuclides. Monitoring its levels in water bodies in regions where peaceful nuclear explosions were perfomed, and comparing this data with that from Roshydromet across the Russian Federation, allows for an assessment of the reliability of the engineering barriers between the central explosion zone and the environment with respect to preventing radionuclide migration into aquifers. One method for evaluating the reliability of these barriers is the assessment of tritium specific activity in drinking water sources. This article presents results of the study involving 220 water samples collected from drinking water sources (wells, boreholes, springs, central water supply systems) and surface waters within 167 settlements across 17 subjects of the Russian Federation, where 50 peaceful nuclear explosions were conducted between 1965 and 1988. The samples were collected between May and September 2024 in the settlements within a 30 km radius of a peaceful nuclear explosion site. Measurements of tritium specific activity were performed using the Quantulus 1220-003 alpha-beta spectrometric radiometer. The research revealed that the specific activity of tritium in underground water sources is significantly lower (Student's test p<0,05) than in surface waters. The average specific activity levels of tritium in boreholes, rivers, and lakes were 3.0, 3.45, and 4.31 Bq/kg, respectively. The specific activity of tritium in drinking water sources within the regions of peaceful nuclear explosions was found to be at the background levels recorded by Roshydromet, ranging from 1.1 to 5 Bq/kg.

HAN Guang

As a set of automated process has not yet been formed for constructing reservoir capacity curves using remote sensing technology, it is proposed to determine the optimal threshold with the inflection point method of the cumulative frequency curve based on the water body index method. The edge of the water body is optimized by the regional seed growth algorithm, so as to construct a complete set of automated process to quickly construct the reservoir capacity curves. With Sentinel-2 remote sensing images as the data source and Chegu Reservoir in Wuan City, Handan City, Hebei Province as the research object, this paper uses five land and water image metrics, namely, NDWI, MNDWI, RWI, MBWI, and AWEInsh to extract the area of the water body, construct the reservoir capacity curve, and compare it with the results of the measurements in 2017. The results are as follows. The extraction accuracy of NDWI and RWI water body index is high. The average error of Chegu water body area extraction is 2.5%. The relative error is -7.3%–5.8% and -6.2%–4.7%, and the <italic>R</italic>² is 0.993 36 and 0.990 49, respectively. The range of the relative error of the reservoir capacity is -9.4% to -1.8% and -9.3% to -1.7%, respectively. The study shows that the cumulative frequency curve inflection point method is highly applicable and stable in determining the optimal threshold of water body index, with a simple and feasible principle. The regional seed growth algorithm can effectively eliminate the discontinuity at the edge of the water body, reduce systematic error, and further improve the accuracy. The cumulative frequency curve inflection point method provides a new idea for remote sensing water body extraction, serving as a reference for the batch construction of national small and medium-sized reservoir capacity curve extraction.

Yu Xu, You-peng Xu, Qiang Wang et al.

Suzhou City, located in the Yangtze River Delta in China, is prone to flooding due to a complex combination of natural factors, including its monsoon climate, low elevation, and tidally influenced position, as well as intensive human activities. The Large Encirclement Flood Control Project (LEFCP) was launched to cope with serious floods in the urban area. This project changed the spatiotemporal pattern of flood processes and caused spatial diversion of floods from the urban area to the outskirts of the city. Therefore, this study developed a distributed flood simulation model in order to understand this transition of flood processes. The results revealed that the LEFCP effectively protected the urban areas from floods, but the present scheduling schemes resulted in the spatial diversion of floods to the outskirts of the city. With rainstorm frequencies of 10.0% to 0.5%, the water level differences between two representative water level stations (Miduqiao (MDQ) and Fengqiao (FQ)) located inside and outside the LEFCP area, ranged from 0.75 m to 0.24 m and from 1.80 m to 1.58 m, respectively. In addition, the flood safety margin at MDQ and the duration with the water level exceeding the warning water level at FQ ranged from 0.95 m to 0.43 m and from 4 h to 22 h, respectively. Rational scheduling schemes for the hydraulic facilities of the LEFCP in extreme precipitation cases were developed according to flood simulations under seven scheduling scenarios. This helps to regulate the spatial flood diversion caused by the LEFCP during extreme precipitation.

Oleksandr Kosenkov, Michael Unterkalmsteiner, Jannik Fischbach et al.

Context: Regulatory acts are a challenging source when eliciting, interpreting, and analyzing requirements. Requirements engineers often need to involve legal experts who, however, may often not be available. This raises the need for approaches to regulatory Requirements Engineering (RE) covering and integrating both legal and engineering perspectives. Problem: Regulatory RE approaches need to capture and reflect both the elementary concepts and relationships from a legal perspective and their seamless transition to concepts used to specify software requirements. No existing approach considers explicating and managing legal domain knowledge and engineering-legal coordination. Method: We conducted focus group sessions with legal researchers to identify the core challenges to establishing a regulatory RE approach. Based on our findings, we developed a candidate solution and conducted a first conceptual validation to assess its feasibility. Results: We introduce the first version of our Artifact Model for Regulatory Requirements Engineering (AM4RRE) and its conceptual foundation. It provides a blueprint for applying legal (modelling) concepts and well-established RE concepts. Our initial results suggest that artifact-centric RE can be applied to managing legal domain knowledge and engineering-legal coordination. Conclusions: The focus groups that served as a basis for building our model and the results from the expert validation both strengthen our confidence that we already provide a valuable basis for systematically integrating legal concepts into RE. This overcomes contemporary challenges to regulatory RE and serves as a basis for exposure to critical discussions in the community before continuing with the development of tool-supported extensions and large-scale empirical evaluations in practice.

Mehil B Shah, Mohammad Masudur Rahman, Foutse Khomh

Deep learning (DL) techniques have achieved significant success in various software engineering tasks (e.g., code completion by Copilot). However, DL systems are prone to bugs from many sources, including training data. Existing literature suggests that bugs in training data are highly prevalent, but little research has focused on understanding their impacts on the models used in software engineering tasks. In this paper, we address this research gap through a comprehensive empirical investigation focused on three types of data prevalent in software engineering tasks: code-based, text-based, and metric-based. Using state-of-the-art baselines, we compare the models trained on clean datasets with those trained on datasets with quality issues and without proper preprocessing. By analysing the gradients, weights, and biases from neural networks under training, we identify the symptoms of data quality and preprocessing issues. Our analysis reveals that quality issues in code data cause biased learning and gradient instability, whereas problems in text data lead to overfitting and poor generalisation of models. On the other hand, quality issues in metric data result in exploding gradients and model overfitting, and inadequate preprocessing exacerbates these effects across all three data types. Finally, we demonstrate the validity and generalizability of our findings using six new datasets. Our research provides a better understanding of the impact and symptoms of data bugs in software engineering datasets. Practitioners and researchers can leverage these findings to develop better monitoring systems and data-cleaning methods to help detect and resolve data bugs in deep learning systems.

H. Tazoe

Water is a primary resource for the presence of life on earth, and access to clean water is critical for humans and the ecosystem. Only 1% of the world's water supply is used to meet all of humanity's needs, i.e., agricultural, domestic, industrial, municipal, and residential. During the last decades, water quality has been negatively influenced by a continuously increasing population, rapid industrialization, increasing urbanization, and careless utilization of natural resources [1]. Heavy metals, organic matter, pharmaceutical and personal care products, pesticides, radionuclides, plastics, nanoparticles and pathogens are among the pollutants of major concern [2]. Nearly 2.2 billion people globally lack reliable access to safely managed drinking water [3]. Approximately 40% of the lakes and rivers of the planet have been polluted by heavy metals [4]. For example, around 140 million people in 50 countries regularly drink water that contains arsenic with concentrations higher than the World Health Organization (WHO) reference value of 10 μg/L [5]. Sustainable Development Goals (SDGs), which were set up in 2015 by the United Nations General Assembly, ensure availability and sustainable management of water and sanitation for all as Goal 6. The sources of these pollutants can be natural and anthropogenic. Heavy metals, such as Pb, Cr, Cd, Hg, and As, tend to bio-accumulate, which is their overtime increase of concentration in living organisms. Even at very low concentrations they can induce multiple organ damage affecting lungs, kidneys, liver, prostate, esophagus, stomach and skin, and can also cause neurodegenerative disorders and diseases. Natural sources include the interactions with metal containing rocks, normally present in the environment, and volcanic eruptions [6]. The contribution of volcanoes can occur due to large but sporadic emissions, explosive volcanic activity, or continuous low emissions, including geothermal activity and degassing [7]. Anthropogenic sources include those associated with industrial, agricultural and domestic activities [8]. Mining also produces large amounts of heavy metals that are released by mineral extraction and transported through rivers and streams [9]. More than 80 percent of wastewater resulting from human activities is discharged into rivers or sea without any pollution removal. For developing countries, one of the main limitations is a low economic capacity to develop and apply remediation technologies [10]. Anthropogenic activities, urbanization, and industrialization represent key factors in increasing the concentration of these pollutants, particularly in recent decades. It should be taken into account that micro-pollutants are not found in water resources individually. Therefore, this mixture can cause synergistic effects, making it more difficult their detection, quantification, and removal [11]. The need to analyze and manage water quality and supply to sustain human activities and ecosystems is widespread. Monitoring the aquatic environment and applying efficient methods for its protection is impossible without employing adequate chemical analytical methods. The technique to be selected for this purpose should be cost-effective, environmentally friendly, selective, and sensitive enough to detect traces with good precision [12]. The portable X-ray fluorescence (PXRF) equipment has relatively high detection limits [13], although it has a great advantage, which is the non-destructive analysis of liquid and solid samples [14]. Electrochemical techniques, such as the potentiometric, amperometric, voltammetric, coulometric, impedance, and electro-chemiluminescence methods, have their advantages because of their simplicity, low cost and speed. In addition, their derivations of these techniques have produced linear sweep anode (LSASV), square wave anode sweep (SWASV), differential pulse anode sweep (DPASV), cyclic cathodic sweep (CSV), cyclic (CV) voltammetry, and chrono-potentiometry (CP) * Hirofumi Tazoe tazoe@hirosaki-u.ac.jp

WANG Bin, QIAO Shuaishuai, LIU Jifa

To comprehensively understand the impact of climate change on reference crop evapotranspiration (ET₀) in Inner Mongolia,the daily ET₀ of 50 meteorological stations in Inner Mongolia in the past 60 years was calculated using the FAO Penman-Monteith formula,and further investigated spatiotemporal characteristics,interdecadal and cyclical changes of ET₀ in Inner Mongolia.The results showed that the distribution trend of ET₀ gradually decreased from west to east in Inner Mongolia,the multi-year average was 1 018 mm,while the climate tilt rate was 4.2 mm/10a,showing a significant upward trend.The sum of ET₀ in spring,summer and autumn,the growing periods of crops,accounted for about 95% of the total annual value,while the spatial distribution was similar to the annual value.The annual average ET₀ in various climate zones followed a descending order:ultra-arid climate zone,arid climate zone,semi-arid climate zone,dry and semi-humid climate zone,humid and semi-humid climate zone.The variation coefficients of the climate zones were low,showing weak variability,and abrupt changes occurred in different climate zones at the end of the 1990s.The ET₀ change cycles were mainly 59a,14a and 24a.

YE Bangping, DING Yuhong, LIU Jinbao et al.

Taking a small watershed in the Pengzhou mountainous area of Sichuan as the research object,this paper employs historical precipitation data,watershed zoning data,DEM,and other data to calibrate the model parameters on a watershed runoff confluence model based on Navier-Stokes equation (N-S equation) with stable flow fields.The conclusions are as follows:① After model parameter calibration,the diffusion coefficient is 0.12,with an acceleration coefficient of 0.12 and a maximum water velocity of 3;② The analysis of confluence characteristics under different precipitation scenarios shows that with the increasing rainfall,the maximum water depth rises,and the time for maximum increase in water depth and the time for the maximum water depth decrease.The watersheds with the maximum time for a water depth of less than 200 min are mainly the upper watersheds,while those with the maximum time for a water depth of more than 200 min are mainly the main watersheds;③ The assessment reveals that there are no watersheds with high risk levels of mountain floods in the small watershed of Pengzhou mountainous area,17 watersheds with high levels,19 watersheds with relatively high levels,and only one watershed with low levels.

Waldemar Treder, Krzysztof Klamkowski, Katarzyna Wójcik et al.

A machine learning model was developed to support irrigation decisions. The field research was conducted on ‘Gala’ apple trees. For each week during the growing seasons (2009–2013), the following parameters were determined: precipitation, evapotranspiration (Penman–Monteith formula), crop (apple) evapotranspiration, climatic water balance, crop (apple) water balance (AWB), cumulative climatic water balance (determined weekly, ΣCWB), cumulative apple water balance (ΣAWB), week number from full bloom, and nominal classification variable: irrigation, no irrigation. Statistical analyses were performed with the use of the WEKA 3.9 application software. The attribute evaluator was performed using Correlation Attribute Eval with the Ranker Search Method. Due to its highest accuracy, the final analyses were performed using the WEKA classifier package with the J48graft algorithm. For each of the analysed growing seasons, different correlations were found between the water balance determined for apple trees and the actual water balance of the soil layer (10–30 cm). The model made correct decisions in 76.7% of the instances when watering was needed and in 87.7% of the instances when watering was not needed. The root of the classification tree was the AWB determined for individual weeks of the growing season. The high places in the tree hierarchy were occupied by the nodes defining the elapsed time of the growing season, the values of ΣCWB and ΣAWB.

Kelechi G. Kalu, Taylor R. Schorlemmer, Sophie Chen et al.

The primary theory of software engineering is that an organization's Policies and Processes influence the quality of its Products. We call this the PPP Theory. Although empirical software engineering research has grown common, it is unclear whether researchers are trying to evaluate the PPP Theory. To assess this, we analyzed half (33) of the empirical works published over the last two years in three prominent software engineering conferences. In this sample, 70% focus on policies/processes or products, not both. Only 33% provided measurements relating policy/process and products. We make four recommendations: (1) Use PPP Theory in study design; (2) Study feedback relationships; (3) Diversify the studied feedforward relationships; and (4) Disentangle policy and process. Let us remember that research results are in the context of, and with respect to, the relationship between software products, processes, and policies.

Audrey Chatain, Scot C. R. Rafkin, Alejandro Soto et al.

Titan, the largest moon of Saturn, has many lakes on its surface, formed mainly of liquid methane. Like water lakes on Earth, these methane lakes on Titan likely profoundly affect the local climate. Previous studies (Rafkin and Soto 2020, Chatain et al 2022) showed that Titan's lakes create lake breeze circulations with characteristic dimensions similar to the ones observed on Earth. However, such studies used a model in two dimensions; this work investigates the consequences of the addition of a third dimension to the model. Our results show that 2D simulations tend to overestimate the extension of the lake breeze over the land, and underestimate the strength of the subsidence over the lake, due to divergence/convergence geometrical effects in the mass conservation equations. In addition, 3D simulations including a large scale background wind show the formation of a pocket of accelerated wind behind the lake, which did not form in 2D simulations. An investigation of the effect of shoreline concavity on the resulting air circulation shows the formation of wind currents over peninsulas. Simulations with several lakes can either result in the formation of several individual lake breeze cells (during the day), or the emergence of a large merged cell with internal wind currents between lakes (during the night). Simulations of several real-shaped lakes located at a latitude of 74°N on Titan at the autumn equinox show that larger lakes trigger stronger winds, and that some sections of lakes might accumulate enough methane vapor to form a thin fog. The addition of a third dimension, along with adjustments in the parametrizations of turbulence and subsurface land temperature, results in a reduction in the magnitude of the average lake evaporate rate, namely to ~6 cm/Earth year.

Dulaji Hidellaarachchi, John Grundy, Rashina Hoda et al.

Context: Requirements Engineering (RE)-related activities are critical in developing quality software and one of the most human-dependent processes in software engineering (SE). Hence, identifying the impact of diverse human-related aspects on RE is crucial in the SE context. Objective: Our study explores the impact of one of the most influential human aspects, motivation on RE, aiming to deepen understanding and provide practical guidance. Method: By conducting semi-structured interviews with 21 RE-involved practitioners, we developed a theory using socio-technical grounded theory(STGT) that explains the contextual, causal, and intervening conditions influencing motivation in RE-related activities. Result: We identified strategies to enhance motivating situations or mitigate demotivating ones, and the consequences resulting from applying these strategies. Conclusion: Our findings offer actionable insights for software practitioners to manage the influence of motivation on RE and help researchers further investigate its role across various SE contexts in the future.