利用目标检测和流体动力学模拟解锁伊庇鲁斯山区河流的流动-栖息地关系。

IF 8 1区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES
Christina Papadaki , Dimitris N. Makropoulos , Sergios Lagogiannis , Antonis Kavvadias , Stamatis Zogaris , Elias Dimitriou
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引用次数: 0

摘要

人类活动通过改变非生物和生物因素影响水生生态系统,进而影响生境结构和生物多样性。环境流量,或维持生态系统所需的水流量水平,影响鱼类的栖息地,流量变化影响鱼类的分布、迁徙和行为。本研究将机器学习(ML)算法,特别是Faster基于区域的卷积神经网络(Faster R-CNN)和You Only Look Once (YOLO)模型,与生态流体动力学建模相结合,以评估不同流量条件下鱼类微栖息地的适宜性。在Voidomatis河三个代表性河段的池和河汊之间的过渡地带,以4.1 m3/s的流量取样了16个地点,研究了西巴尔干鳟鱼个体的微生境特征。18种流量情景的水动力模拟结果表明,在流量为14.8 m3/s时生境适宜性最高,对应于最佳水深(~1.0 m)和流速(~0.6 m/s)条件。所得的最大加权可用面积(WUA)反映了维持鱼类生境的最佳水力参数组合。Faster R-CNN和YOLO都能在视觉上嘈杂、浑浊的环境中有效地检测到鱼,在最佳配置下的f1得分都在90%以上。值得注意的是,更快的R-CNN在主要性能指标(mAP50-95)上优于YOLO。虽然水下视频在高流量条件下受到限制,可能会错过一些鱼类的行为,但将其与栖息地-流体动力学建模相结合,可以为微栖息地的使用提供有价值的见解。通过整合基于ml的检测、水动力模型和栖息地适宜性曲线,本研究为评估鱼类栖息地和了解流量变化如何影响栖息地质量提供了一个强大的框架。这些见解对于有效的保护和河流管理战略,确保水生生态系统的可持续性至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Unlocking flow–habitat relationships in mountain rivers of Epirus, Greece using object detection and hydrodynamic simulation

Unlocking flow–habitat relationships in mountain rivers of Epirus, Greece using object detection and hydrodynamic simulation
Human activities impact aquatic ecosystems by altering abiotic and biotic factors, which in turn affect habitat structure and biodiversity. Environmental flows, or the necessary water flow levels to sustain ecosystems, influence fish habitats, with flow variations affecting fish distribution, migration, and behavior. This study integrates machine learning (ML) algorithms, specifically the Faster Region-Based Convolutional Neural Network (Faster R-CNN) and the You Only Look Once (YOLO) model, with ecohydrodynamic modeling to assess fish microhabitat suitability under varying flow conditions. Microhabitat characteristics of individual West balkan trout (Salmo farioides) were studied in transitional zones between pool and riffles across three representative reaches of the Voidomatis River, with 16 locations sampled at a discharge of 4.1 m3/s. Hydrodynamic simulations for 18 discharge scenarios indicated that habitat suitability peaked at a discharge of 14.8 m3/s, corresponding to optimal depth (~1.0 m) and velocity (~0.6 m/s) conditions. The resulting maximum Weighted Usable Area (WUA) reflected the best combination of hydraulic parameters for sustaining fish habitat. Both Faster R-CNN and YOLO effectively detect fish in visually noisy, turbid environments, achieving F1-scores above 90 % in their best configurations. Notably, Faster R-CNN outperforms YOLO across the primary performance metric (mAP50–95). While underwater video is limited under high-flow conditions and may miss some fish behaviors, combining it with habitat-hydrodynamic modeling provides valuable insights into microhabitat use. By integrating ML-based detection, hydrodynamic models, and habitat suitability curves, this research offers a robust framework for assessing fish habitats and understanding how flow variability may impact habitat quality. These insights are vital for effective conservation and river management strategies, ensuring the sustainability of aquatic ecosystems.
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来源期刊
Science of the Total Environment
Science of the Total Environment 环境科学-环境科学
CiteScore
17.60
自引率
10.20%
发文量
8726
审稿时长
2.4 months
期刊介绍: The Science of the Total Environment is an international journal dedicated to scientific research on the environment and its interaction with humanity. It covers a wide range of disciplines and seeks to publish innovative, hypothesis-driven, and impactful research that explores the entire environment, including the atmosphere, lithosphere, hydrosphere, biosphere, and anthroposphere. The journal's updated Aims & Scope emphasizes the importance of interdisciplinary environmental research with broad impact. Priority is given to studies that advance fundamental understanding and explore the interconnectedness of multiple environmental spheres. Field studies are preferred, while laboratory experiments must demonstrate significant methodological advancements or mechanistic insights with direct relevance to the environment.
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