AI-based runoff simulation based on remote sensing observations: A case study of two river basins in the United States and Canada

Data-driven techniques are used extensively for hydrologic time-series prediction. We created various data-driven models (DDMs) based on machine learning: long short-term memory (LSTM), support vector regression (SVR), extreme learning machines, and an artificial neural network with backpropagation, to define the optimal approach to predicting streamflow time series in the Carson River (California, USA) and Montmorency (Canada) catchments. The moderate resolution imaging spectroradiometer (MODIS) snow-coverage dataset was applied to improve the streamflow estimate. In addition to the DDMs, the conceptual snowmelt runoff model was applied to simulate and forecast daily streamflow. The four main predictor variables, namely snow-coverage (S-C), precipitation (P), maximum temperature (T_max), and minimum temperature (T_min), and their corresponding values for each river basin, were obtained from National Climatic Data Center and National Snow and Ice Data Center to develop the model. The most relevant predictor variable was chosen using the support vector machine-recursive feature elimination feature selection approach. The results show that incorporating the MODIS snow-coverage dataset improves the models' prediction accuracies in the snowmelt-dominated basin. SVR and LSTM exhibited the best performances (root mean square error = 8.63 and 9.80) using monthly and daily snowmelt time series, respectively. In summary, machine learning is a reliable method to forecast runoff as it can be employed in global climate forecasts that require high-volume data processing.     

Nanoparticles Addition in Coir‐Basalt‐Innegra Fibers Reinforced Bio-synthetic Epoxy Composites

Journal of Polymers and the Environment - In the present investigation, the influence of coir micro-particles and titanium carbide (TiC) nanofillers on mechanical characteristics and thermal...     

Off-grid: community energy and the pursuit of self-sufficiency in British Columbia's remote and First Nations communities

Remote or off-grid communities in Canada primarily rely on diesel generators for the provision of their electricity. Often surrounded by potential renewable resources, they are characterised as the low-hanging fruit of greenhouse gas mitigation strategies. While much is said about the promises of community energy projects, as well as technologies and policy mechanisms for addressing the needs of these communities, little attention has been paid to what communities, themselves, might want for their energy projects and what the implications of those desires might be for both technology development and community energy policies. This paper aims to fill this gap by exploring the on-going energy pursuits of a number of remote First Nations communities in British Columbia. It identifies a desire for community self-sufficiency as a primary motivation for engaging with energy projects on the part of the communities and discusses the various meanings and implications of self-sufficiency in the context of community energy projects. These meanings and implications primarily include the two dimensions of material self-sufficiency and political self-determination, the latter of which suggests a view of community energy projects as processes of decolonisation among First Nation communities in British Columbia. It then suggests that the pursuit of this goal is somewhat incongruent with the approach that government and industry have taken in addressing community energy, especially the way in which remote communities are viewed as the low-hanging fruit of various sustainability projects.     

New bacterial strain of the genus Ochrobactrum with glyphosate-degrading activity

Thirty bacterial strains with various abilities to utilize glyphosate as the sole phosphorus source were isolated from farm soils using the glyphosate enrichment cultivation technique. Among them, a strain showing a remarkable glyphosate-degrading activity was identified by biochemical features and 16S rRNA sequence analysis as Ochrobactrum sp. (GDOS). Herbicide (3 mM) degradation was induced by phosphate starvation, and was completed within 60 h. Aminomethylphosphonic acid was detected in the exhausted medium, suggesting glyphosate oxidoreductase as the enzyme responsible for herbicide breakdown. As it grew even in the presence of glyphosate concentrations as high as 200 mM, Ochrobactrum sp. could be used for bioremediation purposes and treatment of heavily contaminated soils.     

Heavy metal contamination in feathers of Western Reef Heron (Egretta gularis) and Siberian gull (Larus heuglini) from Hara biosphere reserve of Southern Iran

The main objectives of article were monitor the metal concentrations of cadmium, lead, copper, zinc, cobalt, and iron, in the feather of shorebirds species, Western Reef Heron (Egretta gularis) and Siberian Gull (Larus heuglini) from Hara Biosphere Reserve of Southern Iran; and identify any relationships between species. Assaying heavy metals by using Shimadzu AA 680 flame atomic absorption spectrophotometer, the results were given as micrograms per gram dry weight. The concentrations of heavy metal were found to follow the order of iron>zinc>copper>lead>cadmium>cobalt for E. gularis and of iron>zinc>lead>copper>cadmium>cobalt for L. heuglini. Also, they showed that the cadmium, copper, and cobalt concentrations were higher in females than in the males, while the lead concentration for E. gularis and L. heuglini was higher in males. The cadmium concentration means they were measured as1.16 and 1.37 μg/g for E. gularis and L. heuglini, respectively, whereas the lead concentration means 7.04 and 5.48 μg/g for E. gularis and L. heuglini, respectively. The concentrations of nonessential trace elements in E. gularis and L. heuglini were generally comparable to values reported in other studies. The average levels of lead we observed in these birds were greater than 5 μg/g dry weight in the feather that is known to be associated with adverse behavioral or reproductive effects. Data analysis showed that there was a positive correlation between copper and zinc (P?相似文献   

Bio-indicators in cadmium toxicity: Role of HSP27 and HSP70

Environmental Science and Pollution Research - Heat shock proteins (HSPs) are a family of proteins that are expressed by cells in reply to stressors. The changes in concentration of HSPs could be...     

Design and cognitive evaluation of 6 dynamic lane departure warning symbols

Objective: Lane departure, caused by inattention, distraction, drowsiness, or any unusual driver behavior, is a typical risk threatening the driver as well as other road users. Accurate perception of such situations through effective warnings would help drivers to avoid serious consequences. With regard to critical functions of warning symbols for risk communication, the present study focused on providing effective and easily perceivable symbols, compatible with human cognitive capabilities. Thus, the main purpose of the present study was to design and cognitively appraise 6 newly designed dynamic symbols, candidates for a new type of lane departure warning system.

Methods: Simplicity, familiarity, concreteness, meaningfulness, and semantic closeness were the major assessment criteria, defining cognitive features by the earlier researchers in the field. A total number of 187 driving license applicants, with a mean age of 20.58 years (SD = 3.20), participated in the present survey. The participants rated cognitive features of the 6 dynamic symbols along a 0–100 scale.

Results: Significant main effect of the element factor type of the designed symbols on rating cognitive features revealed that the existence of car element was the best predictor for illustrating lane departure. The interaction of both element factor and location of element factor significantly affected the ratings. However, the location of element factor did not solely have any strong effect on the ratings. The results also demonstrated that semantic closeness received the highest overall mean score across symbols (M = 61.80), especially within the symbols that include the car element (M = 75.67). Moreover, a significant difference was observed between the average ratings of the cognitive features, despite the fact that a significant correlation was found between cognitive features.

Conclusion: The most considerable result of the current study was the match between the symbol with the highest ratings and the International Organization for Standardization (ISO)-related icon in appearance. Because previous studies demonstrated a strong correlation between comprehension scores of the symbol and both semantic closeness and meaningfulness, high-level comprehensibility of the best ranked symbol is expected.