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The increased usage of the environmental monitoring system and sensors, installed on a day-to-day basis to explore information and monitor the cities’ environment and pollution conditions, are in demand. Sensor networking advancement with quality and quantity of environmental data has given rise to increasing techniques and methodologies supporting spatiotemporal data interactive visualisation analyses. Moreover, Visualisation (Vis) and Visual Analytics (VA) of spatiotemporal data have become essential for research, policymakers, and industries to improve energy efficiency, environmental management, and cities’ air pollution planning. A platform covering Vis and VA of spatiotemporal data collected from a city helps to portray such techniques’ potential in exploring crucial environmental inside, which is still required. Therefore, this work presents Vis and VA interface for the spatiotemporal data represented in terms of location, including time, and several measured attributes like Particular Matter (PM) PM2.5 and PM10, along with humidity, and wind (speed and direction) to assess the detailed temporal patterns of these parameters in Stuttgart, Germany. The time series are analysed using the unsupervised HDBSCAN clustering on a series of (above mentioned) parameters. Furthermore, with the in-depth sensors nature understanding and trends, Machine Learning (ML) approach called Transformers Network predictor model is integrated, that takes successive time values of parameters as input with sensors’ locations and predict the future dominant (highly measured) values with location in time as the output. The selected parameters variations are compared and analysed in the spatiotemporal frame to provide detailed estimations on how average conditions would change in a region over the time. This work would help to get a better insight into the urban system and enable the sustainable development of cities by improving human interaction with the spatiotemporal data. Hence, the increasing environmental problems for big industrial cities could be alarmed and reduced for the future with proposed work.

Environmental investment prediction has attracted much attention in the last few years. However, there are still great challenges in investment prediction modeling, e.g., 1) effective environmental indicators must be accurately selected to avoid the curse of dimensionality; 2) effective environmental data must be reasonably selected to downsize the scale of historical data; 3) the higher interpretability and lower complexity of prediction models must be considered. To address the above three challenges, a new environmental investment prediction model using fuzzy rule-based system (FRBS), evidential reasoning (ER) approach, and subtractive clustering (SC) algorithm is proposed in the present work, called FRBS-ERSC. In this new model, the FRBS is the core component for the modeling of environmental investment prediction and therefore provides good interpretability and complexity to environmental managers. Meanwhile, the ER approach is used as an improvement technique of the FRBS to combine the strengths of different feature selection methods for better indicator selection, and the SC algorithm is used as another improvement technique of the FRBS to select effective environmental data. An empirical case of environmental investment prediction is studied based on data on 31 provinces in China ranged from 2005 to 2018. The experimental results show that the proposed FRBS-ERSC not only provides interpretable and scalable environmental investment prediction based on effective indicator selection and data selection, but also produces satisfactory accuracy compared to some existing models.

With the continuous improvement of people’s quality of life, air quality issues have become one of the topics of daily concern. How to achieve accurate predictions of air quality in a variety of complex situations is the key to the rapid response of local governments. This paper studies two problems: (1) how to predict the air quality of any monitoring station based on the existing weather and environmental data while considering the spatiotemporal correlation among monitoring stations and (2) how to maintain the accuracy and stability of the forecast even when the available data is severely insufficient. A prediction model combining Long Short-Term Memory networks (LSTM) and Graph Attention (GAT) mechanism is proposed to solve the first problems. A metalearning algorithm for the prediction model is proposed to solve the second problem. LSTM is used to characterize the temporal correlation of historical data and GAT is used to characterize the spatial correlation among all the monitoring stations in the target city. In the case of insufficient training data, the proposed metalearning algorithm can be used to transfer knowledge from other cities with abundant training data. Through testing on public data sets, the proposed model has obvious advantages in accuracy compared with baseline models. Combining with the metalearning algorithm, it gives a much better performance in the case of insufficient training data.

This study explores how a citizen-led environmental data initiative first emerged as bottom-up data activism and was then co-opted by a state-sponsored environmental campaign in China. This researc...

1. Movement is the primary means by which animals obtain resources and avoid hazards. Most movement exhibits directional bias that is related to environmental features (taxis), such as the location of food patches, predators, ocean currents, or wind. Numerous behaviours with directional bias can be characterized by maintaining orientation at an angle relative to the environmental stimuli (menotaxis), including navigation relative to sunlight or magnetic fields and energy-conserving flight across wind. However, no statistical methods exist to flexibly classify and characterise such directional bias. 2. We propose a biased correlated random walk model that can identify menotactic behaviours by predicting turning angle as a trade-off between directional persistence and directional bias relative to environmental stimuli without making a priori assumptions about the angle of bias. We apply the model within the framework of a multi-state hidden Markov model (HMM) and describe methods to remedy information loss associated with coarse environmental data to improve the classification and parameterization of directional bias. 3. Using simulation studies, we illustrate how our method more accurately classifies behavioural states compared to conventional correlated random walk HMMs that do not incorporate directional bias. We illustrate the application of these methods by identifying cross wind olfactory foraging and drifting behaviour mediated by wind-driven sea ice drift in polar bears (Ursus maritimus) from movement data collected by satellite telemetry. 4. The extensions we propose can be readily applied to movement data to identify and characterize behaviours with directional bias toward any angle, and open up new avenues to investigate more mechanistic relationships between animal movement and the environment.

PurposeGiven the climatic context and economic challenge of Ghana in its developmental strides, energy use of office buildings continues to be a task on the economy. Therefore, the study was about finding measures that could reduce cooling loads in 10 office buildings. The paper presents the outcome of a long-term study of the thermal conditions in a selected number of office buildings in Accra and Kumasi, Ghana.Design/methodology/approachThrough long-term monitoring of environmental data, the buildings were consequently modelled in a simulation application. Thereafter, a validation of the simulation models (using regression coefficients, r2 of 0.53–0.90) was undertaken towards finding measures to reduce cooling loads.FindingsThe results showed various potentials of efficient lighting, thermal mass, night ventilation, insulation to attic floors, efficient glazing, blind deployments, etc. in reducing cooling loads in the range of 2–17.5%. By combining the potential measures to study their synergistic effects on the loads, 35, 39 and 38% improvements were achieved for the low-rise, multi-storey and fully glazed office buildings.Originality/valueThese potential measures ought to be incorporated in the design, specification, construction and operation of Ghanaian office buildings to reduce the burden on the economy and the environment. Now more than ever, there is the need for climatic regions to come up with empirical data that could help relieve the world's economies from the post-pandemic stress.

This work was supported by the Coordination for the Improvement of Higher Education Personnel (BEX:13438/13-1), the Leverhulme Trust Research Project Grant (RPG-2018-258); the Discovery grant from the Natural Sciences and Engineering Research Council of Canada the Polish National Science Centre (UMO-2019/35/O/ST6/04127).

The moisture content of dead forest fuel is an important indicator of risk levels of forest fires and prediction of fire spread. Moisture distribution is important to determine wild fire rating. However, it is often difficult to predict moisture distribution because of a complex terrain, changeable environments and low cover of commercial communication signals inside the forest. This study proposes a moisture content prediction system composed of environmental data collected using a long range radio frequency band 433 MHz wireless sensor network and data processing for moisture prediction based on a BP (back-propagation) neural network. In the fall of 2019, twenty nodes for the collection of environmental data were placed in four forest stands of Maoershan National Forest for a month; 7440 sets of data including temperature, humidity, wind speed and air pressure were obtained. Half the data were used as a training set, the other as a testing set for a BP neural network. The results show that the average absolute error between the predicted value and the real value of moisture content of fuels of Larix gmelini, Betula platyphylla, Juglans mandshurica, and Quercus mongolica stands was 0.94%, 0.21%, 0.86%, 0.97%, respectively. The prediction accuracy was relatively high. The proposed distributed moisture content prediction method has the advantages of wide coverage and good real-time performance; at the same time, it is not limited by commercial signals and so it is especially suitable for forest fire prediction in remote mountainous areas.

International audience; Understanding the geographic distribution of species is a key concern in conservation. By pairing species occurrences with environmental features, researchers can model the relationship between an environment and the species which may be found there. To advance research in this area, a large-scale machine learning competition called GeoLifeCLEF 2022 was organized. It relied on a dataset of 1.6 million observations from 17K species of animals and plants. These observations were paired with high-resolution remote sensing imagery, land cover data, and altitude, in addition to traditional lowresolution climate and soil variables. The main goal of the challenge was to better understand how to leverage remote sensing data to predict the presence of species at a given location. This paper presents an overview of the competition, synthesizes the approaches used by the participating groups, and analyzes the main results. In particular, we highlight the ability of remote sensing imagery and convolutional neural networks to improve predictive performance, complementary to traditional approaches.

This study provides a critical and interdisciplinary review of the archaeological record of the Aeolian Islands (Italy), from their earliest settlement in the mid-sixth millennium BC (Middle Neolithic) to the establishment of trans-Mediterranean networks at the end of the second millennium BC (Final Bronze Age). We combine archaeological, archaeometric, bioarchaeological and environmental data to explore the interplay between different prehistoric practices and their spatial settings, revisiting old evidence and presenting new data. The resulting picture reveals different levels of interaction and the critical role of these small island communities in establishing and/or facilitating networks at the local and (inter)regional scale. Ceramic networks allow us to trace connections between the islands and their neighbours, underscoring the centrality of the island of Lipari, which is further supported by the spatial analysis of the settlement data, in particular concerning the growing web of intervisibility between contemporary settlements on the Aeolian Islands, Sicily and Calabria. We also highlight significant environmental factors, such as arid phases and volcanic events, and assess their impact in light of the islanders’ responses, underscoring their long-term adaptability to the challenges of insularity. The study is supported by a new and up-to-date database of 50 prehistoric sites, incorporating unpublished results of ongoing archaeological investigations by the authors.

The preventive protection of cultural relics is the important topic of cultural relics protection research. Aiming at the shortage of cultural relics data analysis and disease prediction, in this paper, a wavelet correlation analysis relevance vector machine regression method is proposed, which can accurately predict the disease of immovable cultural relics by using the monitored environmental data and the corresponding disease degree of cultural relics. Firstly, the correlation of multivariate time series of immovable cultural relics is quantitatively obtained by using wavelet correlation analysis, and the validity of characteristic variables of cultural relics disease is identified. Then, according to the effective characteristic variables, the relevance vector machine prediction model is constructed. Finally, the good performance of the method is verified by using the environmental monitoring data of the rock mass fracture in the North Qianfo cliff of Dafo Temple in Binzhou City of Shaanxi Province in China. The experimental results show that the proposed method is more effective than the traditional disease prediction methods based on back propagation neural network, support vector machine, principal component analysis relevance vector machine, and random forest for immovable cultural relics. This method is universal and easy to implement for multi-source data prediction of immovable cultural relics diseases. It not only provides ideas for data analysis of the Internet of Things for cultural relics protection, but also gives a scientific theoretical reference for the preventive protection of cultural relics.

UAV path planning for remote sensing aims to find the best-fitted routes to complete a data collection mission. UAVs plan the routes and move through them to remotely collect environmental data from particular target zones by using sensory devices such as cameras. Route planning may utilize machine learning techniques to autonomously find/select cost-effective and/or best-fitted routes and achieve optimized results including: minimized data collection delay, reduced UAV power consumption, decreased flight traversed distance and maximized number of collected data samples. This paper utilizes a reinforcement learning technique (location and energy-aware Q-learning) to plan UAV routes for remote sensing in smart farms. Through this, the UAV avoids heuristically or blindly moving throughout a farm, but this takes the benefits of environment exploration–exploitation to explore the farm and find the shortest and most cost-effective paths into target locations with interesting data samples to collect. According to the simulation results, utilizing the Q-learning technique increases data collection robustness and reduces UAV resource consumption (e.g., power), traversed paths, and remote sensing latency as compared to two well-known benchmarks, IEMF and TBID, especially if the target locations are dense and crowded in a farm.

Environmental data such as pollutants, temperature, and humidity are data that have a role in the agricultural sector in predicting rainfall conditions. In fact, pollutant data is common to be used as a proxy to see the density of industry and transportation. With this need, it is necessary to have automated data from outside websites that are able to provide data faster than satellite confirmation. Data sourced from IQair, can be used as a benchmark or confirmative data for weather and environmental statistics in Indonesia. Data is taken by scraping method on the website. Scraping is done on the API available on the website. Scraping is divided into 2 stages, the first is to determine the location in Indonesia, the second is to collect statistics such as temperature, humidity, and pollutant data (AQI). The module used in python is the scrapy module, where the crawling is effective starting from May 2020. The data is recorded every three hours for all regions of Indonesia and directly displayed by the Power BI-based dashboard. We also illustrated that AQI data can be used as a proxy for socio-economic activity and also as an indicator in monitoring green growth in Indonesia.

Biological invasions, the establishment and spread of non‐native species in new regions, can have extensive economic and environmental consequences. Increased global connectivity accelerates introduction rates, while climate and land‐cover changes may decrease the barriers to invasive populations spread. A detailed knowledge of the invasion history, including assessing source populations, routes of spread, number of independent introductions, and the effects of genetic bottlenecks and admixture on the establishment success, adaptive potential, and further spread, is crucial from an applied perspective to mitigate socioeconomic impacts of invasive species, as well as for addressing fundamental questions on the evolutionary dynamics of the invasion process. Recent advances in genomics together with the development of geographic information systems provide unprecedented large genetic and environmental datasets at global and local scales to link population genomics, landscape ecology, and species distribution modeling into a common framework to study the invasion process. Although the factors underlying population invasiveness have been extensively reviewed, analytical methods currently available to optimally combine molecular and environmental data for inferring invasive population demographic parameters and predicting further spreading are still under development. In this review, we focus on the few recent insect invasion studies that combine different datasets and approaches to show how integrating genetic, observational, ecological, and environmental data pave the way to a more integrative biological invasion science. We provide guidelines to study the evolutionary dynamics of invasions at each step of the invasion process, and conclude on the benefits of including all types of information and up‐to‐date analytical tools from different research areas into a single framework.

Background Despite global intervention efforts, malaria remains a major public health concern in many parts of the world. Understanding geographic variation in malaria patterns and their environmental determinants can support targeting of malaria control and development of elimination strategies. Methods We used remotely sensed environmental data to analyze the influences of environmental risk factors on malaria cases caused by Plasmodium falciparum and Plasmodium vivax from 2014 to 2017 in two geographic settings in Ethiopia. Geospatial datasets were derived from multiple sources and characterized climate, vegetation, land use, topography, and surface water. All data were summarized annually at the sub-district (kebele) level for each of the two study areas. We analyzed the associations between environmental data and malaria cases with Boosted Regression Tree (BRT) models. Results We found considerable spatial variation in malaria occurrence. Spectral indices related to land cover greenness (NDVI) and moisture (NDWI) showed negative associations with malaria, as the highest malaria rates were found in landscapes with low vegetation cover and moisture during the months that follow the rainy season. Climatic factors, including precipitation and land surface temperature, had positive associations with malaria. Settlement structure also played an important role, with different effects in the two study areas. Variables related to surface water, such as irrigated agriculture, wetlands, seasonally flooded waterbodies, and height above nearest drainage did not have strong influences on malaria. Conclusion We found different relationships between malaria and environmental conditions in two geographically distinctive areas. These results emphasize that studies of malaria-environmental relationships and predictive models of malaria occurrence should be context specific to account for such differences.

Purpose Our purpose is to discuss contemporary and pertinent considerations related to mineral-supplementation strategies for sheep grazing marginal rangelands in extensively managed production systems. Sources Grey literature, peer-reviewed literature, and data (published and unpublished) from co-authors were used. Synthesis Precision trace-mineral nutrition of sheep that are grazing extensive production systems requires a comprehensive understanding of the sheep and the grazing environment. Generally, extensive sheep production systems are found in remote geographical regions composed of marginal rangelands not suitable for cultivated crop farming or improved forages. Sheep production is subject to the accessibility and availability of grazable forage, which may vary greatly within and across years. Sheep common to extensive systems include mostly wool-, meat-, and some hair-type sheep. Furthermore, contemporary sheep have changed with regard to mature BW, dietary intake, and prolificacy. Accordingly, it is important to consider both landscape and animal challenges when developing trace-mineral nutrition programs. Conclusions and Applications Dietary mineral heterogeneity of marginal rangelands has made precision mineral supplementation of sheep challenging. Knowledge of plant differences, plant phenology temporal changes, metabolic mineral antagonism, and soil geochemical mapping can facilitate prediction of site-specific mineral shortfalls. Furthermore, an appreciation of recent genetic improvement of sheep breeds common in extensive production systems can enable producers to accurately estimate specific mineral requirements respective of breed and production stage. Future research efforts that use contemporary sheep genotypes and emerging trace-mineral sources with site-specific environmental data are critical to further refine mineral nutrition management of sheep managed in extensive systems.

Characterization of microbial assemblages via environmental DNA metabarcoding is increasingly being used in routine monitoring programs due to its sensitivity and cost-effectiveness. Several programs have been developed recently which infer functional profiles from 16S rRNA gene data using hidden-state prediction (HSP) algorithms. These might offer an economic and scalable alter-native to shotgun metagenomics. To date, HSP-based methods have seen limited use for benthic marine surveys and their performance in these environments remains unevaluated. In this study, 16S rRNA metabarcoding was applied to sediment samples collected at 0 and ≥ 1200 m from Norwegian salmon farms, and three metabolic inference approaches (PAPRICA, PICRUSt2 and TAX4FUN2) evaluated against metagenomics and environmental data. While metabarcoding and metagenomics recovered a comparable functional diversity, the taxonomic composition differed be-tween approaches, with genera richness up to 20× higher for metabarcoding. Comparisons between the sensitivity (highest true positive rates) and specificity (lowest true negative rates) of HSP-based programs in detecting functions found in metagenomics data ranged, respectively, from 0.52 and 0.60 to 0.76 and 0.79. However, little correlation was observed between the relative abundance of their specific functions. Functional beta-diversity of HSP-based data was strongly associated with that of metagenomics (r ≥ 0.86 for PAPRICA and TAX4FUN2) and responded similarly to the impact of fish farm activities. Our results demonstrate that although HSP-based metabarcoding approaches provide a slightly different functional profile than metagenomics, partly due to recovering a distinct community, they represent a cost-effective and valuable tool for characterizing and assessing the effects of fish farming on benthic ecosystems.

Artificial intelligence (AI) methods are successfully used to investigate several biological problems. The present survey of the literature is aimed at identifying the applications of AI methods (machine learning, deep learning, and computer vision) to symbiotic organisms including the associations of both macro and microorganisms. It was relevant to extend this review to applications in metagenomic and image data analysis, those being the dominant methods in the study of environmental impacts to symbiosis. The survey involved 161 papers from 2020-2021to draw conclusions. This analysis revealed that the application of AI methods is crucial for both metagenomic and image data (camera and remote sensing data) analysis for taxon identification, interaction, and phenotyping, and biomonitoring studies in almost all symbiotic associations from bacteria, lichens to coral reefs. The survey further revealed that AI methods have been adopted for inferring the environmental impacts because they can overcome some of the difficulties associated with modelling highly dimensional and nonlinear environmental data with complex interactions. These methods proved to be especially useful if the sample size was small, has mixed data types, or missing values and can be applied to reduce computation time. Different supervised and unsupervised machine learning algorithms such as random forest, support vector machines, k-mers, etc. are being applied at every step in metagenomic data analysis, from the taxonomic assignment, binning to gene and functional annotation, and are gradually replacing the manual methods with automated AI-based annotation. Similarly, the applications of deep learning and computer vision algorithms e.g., convolutional neural network are progressing steadily towards automation of image annotation in the monitoring of marine and terrestrial symbiosis. However, before we can use these methods to their full potential in understanding the multidimensional effects of environmental factors including the impact of anthropogenic activities, there are several gap areas. For example, the identification of novel and cryptic species, limited reference databases, and big data challenges, need to be addressed with further research. There needs to be better communication between biologists and machine learning research communities so that good quality labelled data is generated in future experiments.

Vector control is an effective strategy for reducing vector‐borne disease transmission, but requires knowledge of vector habitat use and dispersal patterns. Our goal was to improve this knowledge for the tsetse species Glossina pallidipes, a vector of human and animal African trypanosomiasis, which are diseases that pose serious health and socioeconomic burdens across sub‐Saharan Africa. We used random forest regression to (i) build and integrate models of G. pallidipes habitat suitability and genetic connectivity across Kenya and northern Tanzania and (ii) provide novel vector control recommendations. Inputs for the models included field survey records from 349 trap locations, genetic data from 11 microsatellite loci from 659 flies and 29 sampling sites, and remotely sensed environmental data. The suitability and connectivity models explained approximately 80% and 67% of the variance in the occurrence and genetic data and exhibited high accuracy based on cross‐validation. The bivariate map showed that suitability and connectivity vary independently across the landscape and was used to inform our vector control recommendations. Post hoc analyses show spatial variation in the correlations between the most important environmental predictors from our models and each response variable (e.g., suitability and connectivity) as well as heterogeneity in expected future climatic change of these predictors. The bivariate map suggests that vector control is most likely to be successful in the Lake Victoria Basin and supports the previous recommendation that G. pallidipes from most of eastern Kenya should be managed as a single unit. We further recommend that future monitoring efforts should focus on tracking potential changes in vector presence and dispersal around the Serengeti and the Lake Victoria Basin based on projected local climatic shifts. The strong performance of the spatial models suggests potential for our integrative methodology to be used to understand future impacts of climate change in this and other vector systems.

Objective There is a lack of evidence on how to best integrate patient-generated health data (PGHD) into electronic health record (EHR) systems in a way that supports provider needs, preferences, and workflows. The purpose of this study was to investigate provider preferences for the graphical display of pediatric asthma PGHD to support decisions and information needs in the outpatient setting.Methods In December 2019, we conducted a formative evaluation of information display prototypes using an iterative, participatory design process. Using multiple types of PGHD, we created two case-based vignettes for pediatric asthma and designed accompanying displays to support treatment decisions. Semi-structured interviews and questionnaires with six participants were used to evaluate the display usability and determine provider preferences.Results We identified provider preferences for display features, such as the use of color to indicate different levels of abnormality, the use of patterns to trend PGHD over time, and the display of environmental data. Preferences for display content included the amount of information and the relationship between data elements.Conclusion Overall, provider preferences for PGHD include a desire for greater detail, additional sources, and visual integration with relevant EHR data. In the design of PGHD displays, it appears that the visual synthesis of multiple PGHD elements facilitates the interpretation of the PGHD. Clinicians likely need more information to make treatment decisions when PGHD displays are introduced into practice. Future work should include the development of interactive interface displays with full integration of PGHD into EHR systems.

Ecologists and fisheries managers are interested in monitoring economically important marine fish species and using this data to inform management strategies. Determining environmental factors that best predict changes in these populations, particularly under rapid climate change, are a priority. I illustrate the application of the least squares-based spline estimation and group LASSO (LSSGLASSO) procedure for selection of coefficient functions in single index varying coefficient models (SIVCMs) on an ecological data set that includes spatiotemporal environmental covariates suspected to play a role in the catches and weights of six groundfish species. Temporal trends in variable selection were apparent, though the selection of variables was largely unrelated to common North Pacific climate indices. These results indicate that the strength of an environmental variable’s effect on a groundfish population may change over time, and not necessarily in-step with known low-frequency patterns of ocean-climate variability commonly attributable to large-scale regime shifts in the North Pacific. My application of the LSSGLASSO procedure for SIVCMs to deep water species using environmental data from various sources illustrates how variable selection with a flexible model structure can produce informative inference for remote and hard-to-reach animal populations.

This paper details the physical and hardware design of a flexible open-source IoT (Internet of Things) platform for environmental sensing. The application is a remote water quality monitoring buoy that can be deployed in calm, shallow near-shore aquatic environments with fresh or brackish water. The system’s development has been informed by experience through conducting multiple actual water quality studies over a prolonged period. The system runs an Arduino Mega 2560 microcontroller using off-the-shelf Adafruit lux and temperature sensors. A light attenuation turbidity sensor is adapted and integrated into the design. A TinySine 3G GSM module transmits data to a server that is displayed via a ThingsBoard IoT dashboard. The system is stable over time, provides reliable remote sensor readings, has low energy consumption, and is powered by renewable energy (up-cycled batteries). The hardware aspires to be general-purpose so that future environmental monitoring applications can repurpose the electronics by adding new compatible sensors and modifying the physical design to match the requirements.

This presentation was part of a session at the SRI conference in Pretoria, June 2022. The session was entitled 'Transnational data interoperability, open workflows and reproducible science in sustainability and environmental change research' organised by the Belmont Forum CRA for Science-driven e-infrastructures innovation (SEI) for the enhancement of transnational, interdisciplinary and transdisciplinary data use in environmental change research. This presentation discusses the progress to date of the PARSEC project, the challenges, pitfalls and successes as they relate to data standards, transnational data interoperability that feed into automated workflows, data persistence, and how this data work leads to open workflows and reproducible science., PARSEC is a project sponsored by the Belmont Forum as part of its Collaborative Research Action (CRA) on Science-Driven e-Infrastructures Innovation (SEI), with funding from FAPESP, the ANR, JST and the NSF, with collaborators from Australia, and support from the synthesis centre CESAB of the French Foundation for Research on Biodiversity.

A quad-maran unmanned vessel is a new type of unmanned surface vessel for the automatic collection of various environmental data in aquaculture fields. We propose a fault detection and control sche...

Large river management is in increasing demand to establish the ecological quality set by sustainability-oriented legislation. However, there is still a lack of research regarding most relevant scales at which pressures should be addressed. In this study, we compared the relative effects of riparian land-cover at 16 different buffer sizes and hydromorphology at four different river lengths on benthic invertebrate (BI) and fish assemblages in large rivers. Environmental data were obtained digitally (applying GIS) on a broad range of abiotic conditions and were related to biological data using direct gradient analyses. Compared to land-cover, hydromorphology showed greater effect on BI and fish, with in-channel habitat quality characteristics explaining most biotic variability. Both assemblages were best explained by hydromorphology inventoried at the largest 5,000 m scale. We found, however, consideration of longer river segments to be more important for explaining fish variability. Riparian land-cover explained similar percentages of biotic variability across most analysed riparian buffers. Nevertheless, a significant effect of riparian length rather than width was identified. Our findings indicate that, in large rivers, hydromorphological and riparian land-cover conditions affect BI and especially fish at generally longer river segments, implying potential ecological benefits of management measures implemented at larger spatial scales.

The safety and health monitoring of dams has attracted increasing attention. In this paper, a novel prediction model based on variational autoencoder (VAE) and temporal attention-based long short-term memory (TALSTM) network is proposed for the long-term deformation of arch dams. In the proposed model, the convolutional neural network-based VAE is applied to extracting the features of environmental data. The TALSTM is employed to construct the relationship between the dam displacement and extracted features. For verification, an arch dam is taken as an example. Through the comparison among nine baseline prediction models, the proposed model is more stable and effective than other prediction models. Furthermore, the proposed model could capture the features of environmental data accurately and provide better prediction results. Therefore, the proposed model is more suitable for engineering applications.