Pub Date : 2024-08-13DOI: 10.1016/j.envsoft.2024.106181
The US National Fire Danger Rating System (USNFDRS) supports wildfire management decisions nationwide, but it has not been updated since 1988. Here we implement new fuel moisture models, and we simplify the fuel models while maintaining the overall USNFDRS structure. Modeled and measured live fuel moisture content values were highly correlated ( with defaults and when species and location optimized). We also consolidated fuel models to five fuel types that eliminated significant index cross-correlation. Index seasonality compared between old (V2) and new USNFDRS models (v4) across six US National Forests was very similar ( 0.97). V4 was as good or better than V2 at predicting fire days in 92% of the cases tested and V4 effectively predicted wildfire days and large fire ignition days (AUCs 0.647 to 0.915). USNFDRS V4 can adequately depict spatial and temporal wildland fire potential and it can be adapted for worldwide use.
{"title":"Modernizing the US National Fire Danger Rating System (version 4): Simplified fuel models and improved live and dead fuel moisture calculations","authors":"","doi":"10.1016/j.envsoft.2024.106181","DOIUrl":"10.1016/j.envsoft.2024.106181","url":null,"abstract":"<div><p>The US National Fire Danger Rating System (USNFDRS) supports wildfire management decisions nationwide, but it has not been updated since 1988. Here we implement new fuel moisture models, and we simplify the fuel models while maintaining the overall USNFDRS structure. Modeled and measured live fuel moisture content values were highly correlated (<span><math><mrow><msup><mrow><mi>r</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>=</mo><mn>0</mn><mo>.</mo><mn>629</mn></mrow></math></span> with defaults and <span><math><mrow><msup><mrow><mi>r</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>=</mo><mn>0</mn><mo>.</mo><mn>693</mn></mrow></math></span> when species and location optimized). We also consolidated fuel models to five fuel types that eliminated significant index cross-correlation. Index seasonality compared between old (V2) and new USNFDRS models (v4) across six US National Forests was very similar (<span><math><mrow><mi>ρ</mi><mo>=</mo></mrow></math></span> 0.97). V4 was as good or better than V2 at predicting fire days in 92% of the cases tested and V4 effectively predicted wildfire days and large fire ignition days (AUCs 0.647 to 0.915). USNFDRS V4 can adequately depict spatial and temporal wildland fire potential and it can be adapted for worldwide use.</p></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1364815224002421/pdfft?md5=0bad9d72fff3df2a680583db6650ac7a&pid=1-s2.0-S1364815224002421-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142047904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-12DOI: 10.1016/j.envsoft.2024.106179
Dynamically simulating leaf area index assists in modelling the feedbacks between eco-hydrologic and climatic processes. The particular challenge for Australia is the prevalence of arid and semi-arid ecosystems where water availability plays a crucial role in vegetation productivity. To understand whether existing LAI models can capture plant dynamics under changing climates, we tested two competing models across Australia's different climate zones: a conceptual eco-hydrologic model that applies water use efficiency term to relate LAI to water uptake, and a deep learning approach. An initial virtual catchment experiment with deep learning showed that it only uses information from potential evapotranspiration. For future climates, the conceptual model captured a negative trend and increasing variance in LAI, which is plausible given projected rainfall changes, while deep learning did not. Our study demonstrated an example of ‘right answer for the wrong reasons’, and the importance of incorporating knowledge of water-carbon coupling for appropriate scenarios.
动态模拟叶面积指数有助于模拟生态-水文和气候过程之间的反馈。澳大利亚面临的特殊挑战是干旱和半干旱生态系统的普遍存在,在这些生态系统中,水的供应对植被生产力起着至关重要的作用。为了了解现有的 LAI 模型能否捕捉到气候不断变化下的植物动态,我们在澳大利亚的不同气候区测试了两种相互竞争的模型:一种是概念性生态水文模型,该模型应用水分利用效率术语将 LAI 与水分吸收联系起来;另一种是深度学习方法。深度学习的初始虚拟集水区实验表明,它只使用了潜在蒸散量的信息。对于未来气候,概念模型捕捉到了 LAI 的负趋势和不断增加的差异,考虑到预测的降雨量变化,这是合理的,而深度学习却捕捉不到。我们的研究展示了一个 "错误原因的正确答案 "的例子,以及将水碳耦合知识纳入适当情景的重要性。
{"title":"Modelling vegetation dynamics for future climates in Australian catchments: Comparison of a conceptual eco-hydrological modelling approach with a deep learning alternative","authors":"","doi":"10.1016/j.envsoft.2024.106179","DOIUrl":"10.1016/j.envsoft.2024.106179","url":null,"abstract":"<div><p>Dynamically simulating leaf area index assists in modelling the feedbacks between eco-hydrologic and climatic processes. The particular challenge for Australia is the prevalence of arid and semi-arid ecosystems where water availability plays a crucial role in vegetation productivity. To understand whether existing LAI models can capture plant dynamics under changing climates, we tested two competing models across Australia's different climate zones: a conceptual eco-hydrologic model that applies water use efficiency term to relate LAI to water uptake, and a deep learning approach. An initial virtual catchment experiment with deep learning showed that it only uses information from potential evapotranspiration. For future climates, the conceptual model captured a negative trend and increasing variance in LAI, which is plausible given projected rainfall changes, while deep learning did not. Our study demonstrated an example of ‘right answer for the wrong reasons’, and the importance of incorporating knowledge of water-carbon coupling for appropriate scenarios.</p></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1364815224002408/pdfft?md5=a17aa7bf042ec562a0e4f5935767b5b9&pid=1-s2.0-S1364815224002408-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142043584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-12DOI: 10.1016/j.envsoft.2024.106184
This paper introduces the web application-type Graphical User Interface that has been developed and also presents an application example. The introduced simulator conducts hydrodynamics and ecosystems in coastal and estuarine areas. It consists of (1) a hydrodynamic model that can simulate the current velocity, water temperature, salinity, and water level; (2) an ecosystem model that can simulate dissolved oxygen, phytoplankton, zooplankton, nutrients, fish, and bivalves; and (3) a benthic ecosystem model that can simulate elution. Web GUI is the first web system of aquatic environment simulation system that can both prepare calculation conditions and visualize them. Another significant feature is that it requires no installation and can be easily used by anyone to perform calculations. Thus, the proposed system helps fill the expertise gap experienced by potential users of the model. The use of standard systems, such as those discussed in this study, will facilitate evidence-based policymaking (EBPM).
{"title":"Web application of an integrated simulation for aquatic environment assessment in coastal and estuarine areas","authors":"","doi":"10.1016/j.envsoft.2024.106184","DOIUrl":"10.1016/j.envsoft.2024.106184","url":null,"abstract":"<div><p>This paper introduces the web application-type Graphical User Interface that has been developed and also presents an application example. The introduced simulator conducts hydrodynamics and ecosystems in coastal and estuarine areas. It consists of (1) a hydrodynamic model that can simulate the current velocity, water temperature, salinity, and water level; (2) an ecosystem model that can simulate dissolved oxygen, phytoplankton, zooplankton, nutrients, fish, and bivalves; and (3) a benthic ecosystem model that can simulate elution. Web GUI is the first web system of aquatic environment simulation system that can both prepare calculation conditions and visualize them. Another significant feature is that it requires no installation and can be easily used by anyone to perform calculations. Thus, the proposed system helps fill the expertise gap experienced by potential users of the model. The use of standard systems, such as those discussed in this study, will facilitate evidence-based policymaking (EBPM).</p></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1364815224002457/pdfft?md5=50affda9fd41d1b57556dae1043a0eff&pid=1-s2.0-S1364815224002457-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142077005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-12DOI: 10.1016/j.envsoft.2024.106178
Methane is the second most abundant greenhouse gas after carbon dioxide. Anthropogenic sources are the dominant emitters of methane. The poor spatial resolution of satellite imagery, high interclass similarity, the multi-scalar nature of features, and the dominance of background limit the performance of the previous approaches. Further, the reliance on high-resolution imagery limits the cost-effective global application of the works introduced in the literature. To resolve this, the present work proposes a novel method for methane source classification based on open-source multi-spectral satellite imagery of Sentinel-1 and 2. The work utilizes deep dual-scale convolutions with scaled dot product self-attention calculated across the 15 composite bands of Sentinel-1 and 2 data. The incorporation of non-RGB bands along with the RGB bands further enables the model to learn the spectral differences essential for the classification. The experimental results witness the superior performance of the developed method against other considered state-of-the-art methods.
{"title":"An explainable MHSA enabled deep architecture with dual-scale convolutions for methane source classification using remote sensing","authors":"","doi":"10.1016/j.envsoft.2024.106178","DOIUrl":"10.1016/j.envsoft.2024.106178","url":null,"abstract":"<div><p>Methane is the second most abundant greenhouse gas after carbon dioxide. Anthropogenic sources are the dominant emitters of methane. The poor spatial resolution of satellite imagery, high interclass similarity, the multi-scalar nature of features, and the dominance of background limit the performance of the previous approaches. Further, the reliance on high-resolution imagery limits the cost-effective global application of the works introduced in the literature. To resolve this, the present work proposes a novel method for methane source classification based on open-source multi-spectral satellite imagery of Sentinel-1 and 2. The work utilizes deep dual-scale convolutions with scaled dot product self-attention calculated across the 15 composite bands of Sentinel-1 and 2 data. The incorporation of non-RGB bands along with the RGB bands further enables the model to learn the spectral differences essential for the classification. The experimental results witness the superior performance of the developed method against other considered state-of-the-art methods.</p></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141998605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-11DOI: 10.1016/j.envsoft.2024.106186
Hydrologists claim high-quality experimental data are required to improve the understanding of hydrological processes. Though accurate devices for measuring hydrological processes are available, the on-site deployment and operation of effective monitoring networks face many relevant issues caused by the peculiar characteristics of hydrological systems. In this manuscript, we present a self-developed system for monitoring events-based hydrological processes comprising a dense network with both soil moisture and water level gauges connected by NB-IoT technology integrated into a cloud system for near real-time gathering of information. We designed, built and calibrated the sensors and integrated them into a cloud system. We deployed them in two monitoring networks and gathered the data from several experimental runs (battery lifecycles). Results showed the suitability of the sensors and the network to properly monitor the processes solving the initial relevant issues mainly derived from connectivity issues and battery duration.
{"title":"Cloud-based system for monitoring event-based hydrological processes based on dense sensor network and NB-IoT connectivity","authors":"","doi":"10.1016/j.envsoft.2024.106186","DOIUrl":"10.1016/j.envsoft.2024.106186","url":null,"abstract":"<div><p>Hydrologists claim high-quality experimental data are required to improve the understanding of hydrological processes. Though accurate devices for measuring hydrological processes are available, the on-site deployment and operation of effective monitoring networks face many relevant issues caused by the peculiar characteristics of hydrological systems. In this manuscript, we present a self-developed system for monitoring events-based hydrological processes comprising a dense network with both soil moisture and water level gauges connected by NB-IoT technology integrated into a cloud system for near real-time gathering of information. We designed, built and calibrated the sensors and integrated them into a cloud system. We deployed them in two monitoring networks and gathered the data from several experimental runs (battery lifecycles). Results showed the suitability of the sensors and the network to properly monitor the processes solving the initial relevant issues mainly derived from connectivity issues and battery duration.</p></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1364815224002470/pdfft?md5=13f29f5b5e5bc51e3c80cb4402b5983a&pid=1-s2.0-S1364815224002470-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142135738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-10DOI: 10.1016/j.envsoft.2024.106185
The Delaware River Basin (DRB) in the Mid-Atlantic region of the United States is an institutionally complex water resources system that provides drinking water for 13.5 million people, plus water for energy, industry, recreation, and ecosystems. This paper introduces Pywr-DRB, an open-source Python model exploring the impacts of reservoir operations, transbasin diversions, and minimum flow targets on water availability and drought risk in the DRB. Pywr-DRB draws on streamflow estimates from emerging data resources, bridging advances in large-scale hydrologic modeling with an improved representation of the basin's evolving water infrastructure and management institutions. Our detailed model diagnostic assessment demonstrates that Pywr-DRB provides substantial improvements over sole use of hydrologic models in capturing the DRB's dynamics. We also explore how water management alters model-derived risk estimates for low flows and water demand shortfalls. Our approach to diagnostic benchmarking and water systems modeling is broadly applicable to other major basins.
{"title":"Pywr-DRB: An open-source Python model for water availability and drought risk assessment in the Delaware River Basin","authors":"","doi":"10.1016/j.envsoft.2024.106185","DOIUrl":"10.1016/j.envsoft.2024.106185","url":null,"abstract":"<div><p>The Delaware River Basin (DRB) in the Mid-Atlantic region of the United States is an institutionally complex water resources system that provides drinking water for 13.5 million people, plus water for energy, industry, recreation, and ecosystems. This paper introduces Pywr-DRB, an open-source Python model exploring the impacts of reservoir operations, transbasin diversions, and minimum flow targets on water availability and drought risk in the DRB. Pywr-DRB draws on streamflow estimates from emerging data resources, bridging advances in large-scale hydrologic modeling with an improved representation of the basin's evolving water infrastructure and management institutions. Our detailed model diagnostic assessment demonstrates that Pywr-DRB provides substantial improvements over sole use of hydrologic models in capturing the DRB's dynamics. We also explore how water management alters model-derived risk estimates for low flows and water demand shortfalls. Our approach to diagnostic benchmarking and water systems modeling is broadly applicable to other major basins.</p></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1364815224002469/pdfft?md5=91c5b1e93b9f2f09503b058effe2fca6&pid=1-s2.0-S1364815224002469-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141991093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-10DOI: 10.1016/j.envsoft.2024.106182
Attaining resource security in the water, energy, food, and ecosystem (WEFE) sectors, the WEFE nexus, is paramount. This necessitates the use of quantitative modelling, which presents many challenges, as this is a complex system acting at the intersection of the physical- and social sciences. However, as WEFE data is becoming more widely available, data-driven methods of modelling this system are becoming increasingly viable. Here, we discuss two main problems in WEFE nexus modelling: system identification and control. System identification uses Machine Learning algorithms to obtain dynamical models from data and have shown promise in many disciplines with similar characteristics as the nexus. Meanwhile, control algorithms manipulate a system to achieve objectives and are becoming instrumental in shaping nexus policy. Despite the promise of these algorithms, data-driven modelling is a vast and daunting field, and so here we provide an introductory overview of this field, with emphasis on nexus applications.
{"title":"An introduction to data-driven modelling of the water-energy-food-ecosystem nexus","authors":"","doi":"10.1016/j.envsoft.2024.106182","DOIUrl":"10.1016/j.envsoft.2024.106182","url":null,"abstract":"<div><p>Attaining resource security in the <strong>w</strong>ater, <strong>e</strong>nergy, <strong>f</strong>ood, and <strong>e</strong>cosystem (WEFE) sectors, the WEFE nexus, is paramount. This necessitates the use of quantitative modelling, which presents many challenges, as this is a complex system acting at the intersection of the physical- and social sciences. However, as WEFE data is becoming more widely available, data-driven methods of modelling this system are becoming increasingly viable. Here, we discuss two main problems in WEFE nexus modelling: system identification and control. System identification uses Machine Learning algorithms to obtain dynamical models from data and have shown promise in many disciplines with similar characteristics as the nexus. Meanwhile, control algorithms manipulate a system to achieve objectives and are becoming instrumental in shaping nexus policy. Despite the promise of these algorithms, data-driven modelling is a vast and daunting field, and so here we provide an introductory overview of this field, with emphasis on nexus applications.</p></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1364815224002433/pdfft?md5=5cfc6d90e65be6d19815e087d8b6f5c8&pid=1-s2.0-S1364815224002433-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142012956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-10DOI: 10.1016/j.envsoft.2024.106180
The present study demonstrates the capability of an inversion modelling scheme so-called the “triangle” to retrieve spatiotemporal estimates of surface energy fluxes and soil surface moisture (SSM) at high resolution using ASTER satellite imagery synergistically with SimSphere land biosphere model. In addition, as a further objective of this study is to examine the use of the technique for retrieving the Evaporative (EF) and the Non-Evaporative (NEF) Fractions as representations of the daytime average fluxes. The applicability of the investigated technique, is demonstrated for sixteen calendar days of year 2011 using in-situ data acquired from nine CarboEurope sites representing a variety of climatic, topographic and environmental conditions. Results indicated a close agreement between all the inverted parameters and the corresponding in-situ data. SSM predicted maps showed a small bias of 0.08 vol vol−1, a scatter of 0.18 vol vol−1 and a RMSD of 0.19 vol vol−1. The predicted LE fluxes showed a relatively low overall agreement (RMSD = 65.10 Wm-2), whereas for H flux reported RMSD was 85.02 Wm-2. The results also confirmed the ability of the investigated technique to provide meaningful estimates of the NEF and EF. All in all, the present study findings were at least comparable, or of higher accuracy, to those reported in other similar verification studies of the “triangle” using both high resolution (airborne) and low resolution (satellite) data. To our knowledge, this study represents the first comprehensive evaluation of the performance of this particular methodological implementation at a European setting combining the SimSphere SVAT model and ASTER EO datasets.
{"title":"Extending our understanding on the retrievals of surface energy fluxes and surface soil moisture from the “triangle” technique","authors":"","doi":"10.1016/j.envsoft.2024.106180","DOIUrl":"10.1016/j.envsoft.2024.106180","url":null,"abstract":"<div><p>The present study demonstrates the capability of an inversion modelling scheme so-called the “triangle” to retrieve spatiotemporal estimates of surface energy fluxes and soil surface moisture (SSM) at high resolution using ASTER satellite imagery synergistically with SimSphere land biosphere model. In addition, as a further objective of this study is to examine the use of the technique for retrieving the Evaporative (EF) and the Non-Evaporative (NEF) Fractions as representations of the daytime average fluxes. The applicability of the investigated technique, is demonstrated for sixteen calendar days of year 2011 using in-situ data acquired from nine CarboEurope sites representing a variety of climatic, topographic and environmental conditions. Results indicated a close agreement between all the inverted parameters and the corresponding in-situ data. SSM predicted maps showed a small bias of 0.08 vol vol<sup>−1</sup>, a scatter of 0.18 vol vol<sup>−1</sup> and a RMSD of 0.19 vol vol<sup>−1</sup>. The predicted LE fluxes showed a relatively low overall agreement (RMSD = 65.10 Wm<sup>-2</sup>), whereas for H flux reported RMSD was 85.02 Wm<sup>-2</sup>. The results also confirmed the ability of the investigated technique to provide meaningful estimates of the NEF and EF. All in all, the present study findings were at least comparable, or of higher accuracy, to those reported in other similar verification studies of the “triangle” using both high resolution (airborne) and low resolution (satellite) data. To our knowledge, this study represents the first comprehensive evaluation of the performance of this particular methodological implementation at a European setting combining the SimSphere SVAT model and ASTER EO datasets.</p></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142088854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-10DOI: 10.1016/j.envsoft.2024.106183
A general approach for predicting indicator and pathogen decay in surface waters was developed using Bayesian hierarchical modeling, a persistence database, and a two-parameter model form. The resulting hierarchical regression describes general persistence behaviors with target-level intercepts and population-level coefficients. Uncertainty factors calculated with the approach suggest fecal indicator bacteria (FIB) and pathogenic bacteria persist similarly in surface waters, but median virus and protozoa persistence metrics may be 2–3 times greater than FIB in similar conditions. The two-parameter model underpinning the approach was used to identify drivers of these differences. Virus decay rates were shown to taper off more quickly than FIB, whereas protozoa were associated with longer initial periods of minimal decay. Despite the low accuracy of the hierarchical model compared to models fit to individual datasets, this approach addresses a critical gap for water management decision-making as site-specific and pathogen-specific persistence data are uncommon in water monitoring practices.
{"title":"Development and evaluation of a general approach for predicting pathogen decay in surface waters using hierarchical Bayesian modeling","authors":"","doi":"10.1016/j.envsoft.2024.106183","DOIUrl":"10.1016/j.envsoft.2024.106183","url":null,"abstract":"<div><p>A general approach for predicting indicator and pathogen decay in surface waters was developed using Bayesian hierarchical modeling, a persistence database, and a two-parameter model form. The resulting hierarchical regression describes general persistence behaviors with target-level intercepts and population-level coefficients. Uncertainty factors calculated with the approach suggest fecal indicator bacteria (FIB) and pathogenic bacteria persist similarly in surface waters, but median virus and protozoa persistence metrics may be 2–3 times greater than FIB in similar conditions. The two-parameter model underpinning the approach was used to identify drivers of these differences. Virus decay rates were shown to taper off more quickly than FIB, whereas protozoa were associated with longer initial periods of minimal decay. Despite the low accuracy of the hierarchical model compared to models fit to individual datasets, this approach addresses a critical gap for water management decision-making as site-specific and pathogen-specific persistence data are uncommon in water monitoring practices.</p></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142040965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-05DOI: 10.1016/j.envsoft.2024.106170
Underwater fish segmentation technology serves as a crucial foundation for extracting aquatic biological information. However, due to intricate and fluctuating underwater environments, existing segmentation models fail to precisely focus on key image regions. Based on this, the paper developed an underwater fish segmentation model, Receptive Field Expansion Model(RFEM), by enhancing soft attention performance (More attention is directed to fish regions when processing fish pixels). This paper tests ten different attention mechanisms and selects the attention mechanism with better performance indicators to improve it and form an RFEM model. This paper uses two underwater fish data sets to verify the proposed model. The experimental results show the segmentation mean intersection-over-union ratio (MIoU) of RFEM based on dilation convolution reached 88.37%, and the mCPA reached 93.83%, Accuracy reached 96.08%, and F1-score reached 93.74%. It can provide solid technical support for intelligent monitoring such as body length measurement, weight estimation of underwater fish.
{"title":"Segmentation of underwater fish in complex aquaculture environments using enhanced Soft Attention Mechanism","authors":"","doi":"10.1016/j.envsoft.2024.106170","DOIUrl":"10.1016/j.envsoft.2024.106170","url":null,"abstract":"<div><p>Underwater fish segmentation technology serves as a crucial foundation for extracting aquatic biological information. However, due to intricate and fluctuating underwater environments, existing segmentation models fail to precisely focus on key image regions. Based on this, the paper developed an underwater fish segmentation model, Receptive Field Expansion Model(RFEM), by enhancing soft attention performance (More attention is directed to fish regions when processing fish pixels). This paper tests ten different attention mechanisms and selects the attention mechanism with better performance indicators to improve it and form an RFEM model. This paper uses two underwater fish data sets to verify the proposed model. The experimental results show the segmentation mean intersection-over-union ratio (MIoU) of RFEM based on dilation convolution reached 88.37%, and the mCPA reached 93.83%, Accuracy reached 96.08%, and F1-score reached 93.74%. It can provide solid technical support for intelligent monitoring such as body length measurement, weight estimation of underwater fish.</p></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141909452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}