Spatiotemporal Conditions Research Articles

Adaptive control systems for dual axis tracker using clear sky index and output power forecasting based on ML in overcast weather conditions

The use of artificial intelligence in renewable energy systems increases energy generation and improves energy system management. The control system of many solar trackers is designed for maximum radiation power conditions and shows decent performance indicators, but during rapidly changing weather conditions or cloudy days, the performance of the solar trackers is reduced due to moving parts and low irradiance. Some studies show that the horizontal configuration produces more energy with scattered solar radiation than solar tracking systems. This work shows the possibility of using solar tracking systems under different weather conditions and cloudy days. To achieve the goals, a new adaptive control system for dual-axis solar trackers with astronomical tracking was developed, which differs from traditional controls in the use of horizontal configurations under certain weather conditions. The assessment of spatio-temporal weather conditions was carried out using the Clear Sky Index (CSI) and was complemented by forecasting the panel's power output. The study found that at 0.4 CSI values, the horizontal configuration exhibits higher power output than solar tracking systems, providing the potential to use the threshold for adaptive control. The developed system is more efficient by 18.3 %, 14.9 %, and 10.01 % than the horizontal configuration, single-axis, and dual-axis solar trackers.

THE SITUATION OF COMMITTING FRAUD UNDER EXTRAORDINARY LEGAL REGIMES

In the article, based on the analysis of forensic literature, the situation of committing criminal offenses is defined as a system of physical, material and social conditions of the environment chosen by the criminal or objectively surrounding him, in which the criminal offense occurs. It was noted that the quantitative and qualitative composition of the structural elements of the situation of committing criminal offenses depend on the types of criminal offenses. It is noted that fraudsters often adapt to typical factors in a similar environment, as a result of which a typical pattern of criminal activity appears. Regarding the situation and conditions of criminal offenses committed by fraud, it is noted that they are committed under the influence of certain economic, legal, social, political, psychological and other factors that create the basis for the unimpeded implementation of deception and contribute to the concealment of illegal actions. It was emphasized that the effectiveness of the entire investigation in the criminal proceedings as a whole often depends on how the investigator correctly analyzes the spatio-temporal characteristics and conditions in which the fraudsters operated.

Comparative analysis of quantum two-way time transfer and quantum round-trip time transfer in consistent spatiotemporal settings

In this study, we conducted experiments using a single energy-time entangled biphoton source to compare the performance of quantum two-way time transfer (Q-TWTT) and quantum round-trip time transfer (Q-RTTT) under consistent spatiotemporal conditions. By conducting experiments with different fiber links of 11.3 km, 22.4 km, and 55.6 km, while ensuring uniform photon counts received by the single-photon detectors, we measured standard deviations (SDs) and stabilities of the time offsets. The measured SDs for Q-TWTT and Q-RTTT were 0.46 ps and 0.65 ps over the 11.3 km fiber, 1.14 ps and 1.3 ps over the 22.4 km fiber, 3.98 ps and 4.39 ps over the 55.6 km fiber, respectively. These results show good agreement with theoretical predictions, and the smaller SDs for Q-TWTT can be directly attributed to protocol-specific factors related to system symmetry. The long-term time stabilities of Q-TWTT and Q-RTTT were evaluated in terms of time deviation (TDEV). At an average time of 12680 s, the measured TDEVs were 0.49 ps and 0.63 ps for the 11.3 km fiber, 0.59 ps and 0.7 ps for the 22.4 km fiber, 1.01 ps and 1.36 ps for the 55.6 km fiber, respectively. The results validate that Q-TWTT exhibits superior time transfer performance compared to Q-RTTT, highlighting the advantages of Q-TWTT in practical applications.

Two Perspectives on Amplified Warming over Tropical Land Examined in CMIP6 Models

Abstract Climate change projections show amplified warming associated with dry conditions over tropical land. We compare two perspectives explaining this amplified warming: one based on tropical atmospheric dynamics and the other focusing on soil moisture and surface fluxes. We first compare the full spatiotemporal distribution of changes in key variables in the two perspectives under a quadrupling of CO2 using daily output from the CMIP6 simulations. Both perspectives center around the partitioning of the total energy/energy flux into the temperature and humidity components. We examine the contribution of this temperature/humidity partitioning in the base climate and its change under warming to rising temperatures by deriving a diagnostic linearized perturbation model that relates the magnitude of warming to 1) changes in the total energy/energy flux, 2) the base-climate temperature/humidity partitioning, and 3) changes in the partitioning under warming. We show that the spatiotemporal structure of warming in CMIP6 models is well predicted by the inverse of the base-climate partition factor, which we term the base-climate sensitivity: conditions that are drier in the base climate have a higher base-climate sensitivity and experience more warming. On top of this relationship, changes in the partition factor under intermediate (between wet and dry) surface conditions further enhance or dampen the warming. We discuss the mechanistic link between the two perspectives by illustrating the strong relationships between lower-tropospheric temperature lapse rates, a key variable for the atmospheric perspective, and surface fluxes, a key component of the land surface perspective. Significance Statement Understanding what conditions give rise to the largest magnitude of warming in response to rising CO2 concentrations is not only scientifically important but also critical from a climate impact standpoint. Two main perspectives, one focusing on atmospheric dynamics and the other focusing on land surface processes, have been proposed to explain the stronger warming associated with drier conditions in the tropics. Here, we compare and contrast these two perspectives. We demonstrate that amplified warming in CMIP6 models can largely be predicted from base-climate dryness alone in both perspectives but is further modified based on changes in the partitioning of energy between temperature and moisture. We highlight the spatiotemporal conditions where assumptions in the two perspectives hold and where deviations occur within CMIP6 climate models.

Spatio-temporal assessment of multi-scalar meteorological and hydrological droughts over Bundelkhand, India

Drought, a recurrent natural hazard, significantly impacts agricultural productivity, water resources, and socio-economic development, especially in semi-arid regions like Bundelkhand in India. Here, frequent droughts challenge the livelihoods of its predominantly agrarian population. These challenges, compounded by poverty, land degradation, and resource conflicts, have profound implications for local communities and the attainment of Sustainable Development Goals (SDGs). Understanding the spatiotemporal conditions of drought is crucial for effective planning and management. This study evaluates meteorological and hydrological drought attributes at a high resolution within the region, using the Standardized Precipitation Index (SPI) and the Streamflow Drought Index (SDI) across a range of temporal scales. Findings indicate moderate droughts are more probable than severe or extreme droughts, with SPI-12 showing up to 20% probability over some grids, implying drought occurrence once in five years. Trend analysis reveals decreasing SPI trends in certain northern districts during monsoon months, suggesting more frequent droughts in vulnerable areas. The study also finds higher drought probabilities with longer SPI durations, highlighting significant temporal and spatial variability. Additionally, the applicability of SDI using GloFAS discharge data is evaluated, offering an alternative in the absence of gauge data. The combined SPI and SDI analysis underscores higher and more extensive drought probabilities for longer periods, emphasizing the need for multi-temporal scale assessment for effective drought management. This study enhances understanding of drought characteristics in the region, aiding policymaking for drought management and supporting evidence-based strategies to achieve SDGs, particularly those related to water security, climate resilience, and sustainable development.

Multiplexed Microfluidic Platform for Parallel Bacterial Chemotaxis Assays.

The sensing of and response to ambient chemical gradients by microorganisms via chemotaxis regulates many microbial processes fundamental to ecosystem function, human health, and disease. Microfluidics has emerged as an indispensable tool for the study of microbial chemotaxis, enabling precise, robust, and reproducible control of spatiotemporal chemical conditions. Previous techniques include combining laminar flow patterning and stop-flow diffusion to produce quasi-steady chemical gradients to directly probe single-cell responses or loading micro-wells to entice and ensnare chemotactic bacteria in quasi-steady chemical conditions. Such microfluidic approaches exemplify a trade-off between high spatiotemporal resolution of cell behavior and high-throughput screening of concentration-specific chemotactic responses. However, both aspects are necessary to disentangle how a diverse range of chemical compounds and concentrations mediate microbial processes such as nutrient uptake, reproduction, and chemorepulsion from toxins. Here, we present a protocol for the multiplexed chemotaxis device (MCD), a parallelized microfluidic platform for efficient, high-throughput, and high-resolution chemotaxis screening of swimming microbes across a range of chemical concentrations. The first layer of the two-layer polydimethylsiloxane (PDMS) device comprises a serial dilution network designed to produce five logarithmically diluted chemostimulus concentrations plus a control from a single chemical solution input. Laminar flow in the second device layer brings a cell suspension and buffer solution into contact with the chemostimuli solutions in each of six separate chemotaxis assays, in which microbial responses are imaged simultaneously over time. The MCD is produced via standard photography and soft lithography techniques and provides robust,repeatable chemostimulus concentrations across each assay in the device. This microfluidic platform provides a chemotaxis assay that blends high-throughput screening approaches with single-cell resolution to achieve a more comprehensive understanding of chemotaxis-mediated microbial processes. Key features • Microchannel master molds are fabricated using photolithography techniques in a clean room with a mask aligner to fabricate multilevel feature heights. • The microfluidic device is fabricated from PDMS using standard soft lithography replica molding from the master molds. • The resulting microchannel requires a one-time calibration of the driving inlet pressures, after which devices from the same master molds have robust performance. • The microfluidic platform is optimized and tested for measuring chemotaxis of swimming prokaryotes.

Bayesian model of tilling wheat confronting climatic and sustainability challenges.

Conventional farming poses threats to sustainable agriculture in growing food demands and increasing flooding risks. This research introduces a Bayesian Belief Network (BBN) to address these concerns. The model explores tillage adaptation for flood management in soils with varying organic carbon (OC) contents for winter wheat production. Three real soils, emphasizing texture and soil water properties, were sourced from the NETMAP soilscape of the Pang catchment area in Berkshire, United Kingdom. Modified with OC content at four levels (1, 3, 5, 7%), they were modeled alongside relevant variables in a BBN. The Decision Support System for Agrotechnology Transfer (DSSAT) simulated datasets across 48 cropping seasons to parameterize the BBN. The study compared tillage effects on wheat yield, surface runoff, and GHG-CO2 emissions, categorizing model parameters (from lower to higher bands) based on statistical data distribution. Results revealed that NT outperformed CT in the highest parametric category, comparing probabilistic estimates with reduced GHG-CO2 emissions from "7.34 to 7.31%" and cumulative runoff from "8.52 to 8.50%," while yield increased from "7.46 to 7.56%." Conversely, CT exhibited increased emissions from "7.34 to 7.36%" and cumulative runoff from "8.52 to 8.55%," along with reduced yield from "7.46 to 7.35%." The BBN model effectively captured uncertainties, offering posterior probability distributions reflecting conditional relationships across variables and offered decision choice for NT favoring soil carbon stocks in winter wheat (highest among soils "NT.OC-7%PDPG8," e.g., 286,634 kg/ha) over CT (lowest in "CT.OC-3.9%PDPG8," e.g., 5,894 kg/ha). On average, NT released minimum GHG- CO2 emissions to "3,985 kgCO2eqv/ha," while CT emitted "7,415 kgCO2eqv/ha." Conversely, NT emitted "8,747 kgCO2eqv/ha" for maximum emissions, while CT emitted "15,356 kgCO2eqv/ha." NT resulted in lower surface runoff against CT in all soils and limits runoff generations naturally for flood alleviation with the potential for customized improvement. The study recommends the model for extensive assessments of various spatiotemporal conditions. The research findings align with sustainable development goals, e.g., SDG12 and SDG13 for responsible production and climate actions, respectively, as defined by the Agriculture and Food Organization of the United Nations.

Organs on chips: fundamentals, bioengineering and applications.

Human body constitutes unique biological system containing specific fluid mechanics and biomechanics. Traditional cell culture techniques of 2D and 3D do not recapitulate these specific natures of the human system. In addition, they lack the spatiotemporal conditions of representing the cells. Moreover, they do not enable the study of cell-cell interactions in multiple cell culture platforms. Therefore, establishing biological system of dynamic cell culture was of great interest. Organs on chips systems were fabricated proving their concept to mimic specific organs functions. Therefore, it paves the way for validating new drugs and establishes mechanisms of emerging diseases. It has played a key role in validating suitable vaccines for Coronavirus disease (COVID-19). Herein, the concept of organs on chips, fabrication methodology and their applications are discussed.

A fine-grained investigation on the predictors of urban green space growth in Nanjing

ABSTRACT Existing studies have primarily examined urban green space (UGS) evolution in isolation from the urban development context, neglecting the dynamicity of the factors affecting UGS growth in different spatiotemporal conditions and urban development levels. Taking Nanjing as the study site, we constructed spatial lag models using multi-source data and investigated the patterns and predictors of UGS growth across diverse spatiotemporal conditions in high-density environments. The model results show that: (1) although good natural conditions can support UGS development, this effect is greatly weakened in fast urban expansion as other land uses show higher priority in land occupation; (2) industrial and public service lands and commercial lands show a negative effect on UGS growth in the early and late urbanization, respectively, while residential lands demonstrated sustained negative effect in all the urbanization phases; (3) the effect of the total population and population structure varies in different urban development phases; (4) at an advanced urban development level, the areas close to city centre have better opportunities to install new UGSs. These findings offer useful references to the decision makers and planners, especially for their work on the UGS layout optimization within the high-density areas.

Evaluation of Mass Movement Hazard in the Shoreline of the Intertidal Complex of El Grove (Pontevedra, Galicia)

Knowledge of hazard conditions due to mass movements is one of the non-structural measures for risk management, urban planning, and protection of natural resources. To obtain this type of mapping, a spatial construction was started by correlating the historical movements with the inherent variables of the terrain by means of the bivariate statistical method, which assigns densities or weights of evidence to estimate the degree of susceptibility. This model was combined with the triggering factors (rainfall and earthquake) to determine the spatiotemporal conditions (hazard). From this procedure, it was obtained that the susceptibility model presents 34% (32.33 km2) of the total area in the high and very high categories, especially in the regions of Mount Siradella and Mount Faro. The validation of the present model obtained a value of 0.945 with the ROC curve. For the hazard condition, 34.1% (32.06 km2) of the study area was found to be in the high and very high category, especially in the municipalities of El Grove, Sanxenxo, and A Illa de Arousa, which have the greatest extension. The present evaluation is an advance in the knowledge of the risk and the actions that can be derived, as in turn, this type of study is an easy tool to obtain due to its low cost and information processing.

Modeling water hyacinth (Eichhornia crassipes) distribution in Lake Tana, Ethiopia, using machine learning

Aquatic invasive plant, water hyacinth poses serious environmental and socioeconomic challenges. Understanding and predicting the spatiotemporal distribution of this species is important for reducing its environmental impact. Therefore, the present study aimed to model the distribution of water hyacinths in an important ecological region (Lake Tana) of Ethiopia using four machine learning models. We used 11 variables obtained from Sentinel-1 SAR bands, Sentinel-2A bands and indices, and bioclimate data sources. The models use 458 presence and 458 randomly generated pseudoabsence data as response variables and employ a tenfold bootstrap sampling method. The area under the curve (AUC), receiver operator curve (ROC), true skill statistics (TSS), coefficient of rank correlation (COR), sensitivity, specificity, and kappa coefficient were used to evaluate the models. The findings demonstrate that the random forest model outperforms the other models, with AUC values of 0.93 and 0.95, TSS values of 0.77 and 0.82, and kappa values of 0.76 and 0.82 in the wet and dry seasons, respectively. B12 (16% and 20%), NDWI (15% and 12%), mean annual temperature (13% and 14%), and B5 (11% and 12%) were found to be the most relevant variables during the wet and dry seasons, respectively. Water hyacinths have greater spatial coverage during the wet season than during the dry season because of high rainfall, high water levels and nutrient runoff. We can conclude that to detect and predict the spatiotemporal conditions of water hyacinth accurately, integrating Sentinel image indices and bands with bioclimatic variables and using machine learning models are crucial.

Unraveling the interaction of co-encapsulated Saccharomyces cerevisiae and Metarhizium brunneum in calcium alginate-based attract-and-kill beads.

Attract-and-kill (AK) beads are biological, microbial insecticides developed as an alternative to synthetic soil insecticides. For wireworm control, beads are based on calcium alginate/starch co-encapsulating the carbon dioxide (CO2) producing yeast Saccharomyces cerevisiae H205 as the attract component, and the entomopathogenic fungus Metarhizium brunneum CB15-III as the kill component. However, the physicochemical processes inside beads during co-cultivation are still unclear. Here we reveal for the first time the spatiotemporal conditions of oxygen and pH inside AK beads measured with microelectrodes and describe the impact of S. cerevisiae on CO2 and conidia formation. Measurements revealed a steep oxygen gradient already 2 days after co-encapsulation, with an internal hypoxic zone. Encapsulating either S. cerevisiae or M. brunneum already decreased the average pH from 5.5 to 4.7 and 4.6, respectively. However, on day 3, co-cultivation lead to temporal strong acidification of beads down to pH 3.6 which followed the maximum CO2 productivity and coincided with the maximum conidiation rate. Decreasing the yeast load decreased the total CO2 productivity to half, and the conidial production by 93%, while specific productivities normalized to 1% yeast load increased eight-fold and three-fold, respectively, with day 3 being an exception. Our findings indicate a general beneficial interaction between M. brunneum and S. cerevisiae, but also suggest competition for resources. These findings will contribute to develop innovative co-formulations with maximum efficiency to save application rates and costs. © 2024 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Exploring competitiveness of taxis to ride-hailing services from a multidimensional spatio-temporal perspective: A case study in Beijing, China

As the sharing economy expands in China, the emergence of ride-hailing services has diminished the market share of the taxi industry. As a regulated and publicly convenient service with a dedicated customer base, traditional taxi industry needs to improve its own competitiveness and maintain its market share. However, the specific circumstances under which taxis can gain a competitive edge over ride-hailing services are not well-understood. Aiming to uncover the competitive potential of taxis in the ride-source market, this study proposes a methodology to explore the Competition and Cooperation Relationship (CCR) between taxis and ride-hailing services on a multidimensional spatio-temporal scale. By taking Beijing, China as a case study, we first compare different impacts of Points of Interest (POI) on the traffic volume of taxis/ride-hailing services through Geographically and Temporally Weighted Regression (GTWR) models, and explore the corresponding times and locations where taxis/ride-hailing services are more likely to attract passengers. Based on the findings that there are strong correlations between traffic volume and spatio-temporal conditions, we establish the Competition-Cooperation Index (CCI) as a quantitative measure to characterize the CCR and then analyze the spatio-temporal distribution of CCI to identify the times and locations where taxis hold advantages in cooperative or competitive relationship relative to ride-hailing services. Furthermore, we investigate the underlying reasons for these patterns, discovering that CCI has a close connection with land use. The results of our analysis show that taxis exhibit competitive advantages over ride-hailing services under some specific circumstances and can further enhance their competitiveness by proposed targeted measures. The findings of this study provide valuable insights for both industries in formulating growth strategies and for governmental agencies in setting policies.

Research on Optimization Strategy of Commercial Street Spatial Vitality Based on Pedestrian Trajectories

Commercial pedestrian streets significantly enhance urban life. Previous research often depends on limited survey data or subjective evaluations, which fail to fully capture the impact of street elements on spatial vitality under diverse spatiotemporal conditions. This study, conducted in Beijing’s Sanlitun commercial district, analyzes pedestrian data from six branch roads collected via WiFi detection to determine how various spatial elements influence the vibrancy of commercial pedestrian streets. The results show a positive correlation between street width, ranging from 5.88 to 10.83 m, and vitality. Furthermore, the influence of different street elements on vitality varies notably. During the daytime, street width is the most effective element for enhancing vitality, with a maximum standardized coefficient of 1.717. However, at night, the associations between advertising area, store type, and vitality diminish. The street recording the highest nighttime vitality peaked at a pedestrian count of 91.7831. The contribution of street width to enhancing nighttime vitality is 2.49 times greater than at noon on non-working days, 2.01 times more than on working days, and 2.92 times higher than the impact of tables and chairs. Therefore, street design should prioritize street width, ground floor interface permeability, number of tables and chairs, store density, advertising area, and store type.

A reasoning method for rice fertilization strategy based on spatiotemporal knowledge graph

AbstractThe lack of multidimensional knowledge means that current reasoning methods for rice fertilization cannot make decisions accurate when faced with complex spatiotemporal conditions in general. Therefore, we propose a reasoning method for rice fertilization strategy based on spatiotemporal knowledge graph. First, we systematically organize multisource expert knowledge about rice fertilization, and construct an ontology for rice fertilization consisting of five core elements: rice variety, planting environment, nutrition diagnosis, fertilization schemes, and time and place. Spatiotemporal differences in rice fertilization knowledge are expressed by assessing spatiotemporal concepts, relations, and state instances. Second, we propose a reasoning method for rice fertilization strategy based on the constructed knowledge graph. This method leverages a certainty factor model for nutrition diagnosis and integrates case‐based and rule‐based reasoning to determine fertilization schemes for different stages. Finally, taking Pucheng County, China, as an example, knowledge from crowd‐sensing data is obtained to construct a knowledge graph using the proposed method. The results demonstrate the method can support the expression and complex reasoning of rice fertilization decisions under different spatiotemporal conditions.

The missing link for effective informal settlement upgrading: Appropriation shaping the outcome of new infrastructure

Infrastructure investments, a core element of slum upgrading, play a role in improving the livelihoods of over 1 billion slum residents globally. Established planning practices often successfully deliver functional infrastructure but evidence shows that their contribution to improved livelihoods often either is absent or declines sharply after some time. To explain this limited effectiveness, this article identifies the missing link between infrastructure delivery and livelihood improvements as lying in the appropriation process, that is, the uptake and embedding of infrastructures into the daily practices of residents. Recent insights from sociotechnical transitions studies help to conceptualise appropriation. The authors use Munyaka informal settlement in Eldoret town, Kenya as a case to investigate the mechanisms of new infrastructure uptake. Findings indicate that appropriation is a social process that proceeds in three steps: reception, domestication and institutionalisation. This process is driven by the need to maintain or adjust residents’ livelihood practices relative to prevailing socioeconomic and spatiotemporal conditions. The study concludes that appropriation is a significant process that planners should try to anticipate. Prevalent approaches to participation have to be modified accordingly. This is essential for planning to improve livelihoods in slums.

Assessment of Grid and System Supportability Based on Spatio-Temporal Conditions—Novel Key Performance Indicators for Energy System Evaluation

The energy transition introduces new technical standards, laws and regulations regarding the stability and reliability of energy grids and systems. Due to the non-existence of a measuring standard, key performance indicators (KPIs) were developed to enable the measurement and comparison of individual energy grid (namely electricity, heat and gas grid) and system supportabilities while also promoting well-founded decision-making and optimization efforts. Inconsistencies in definitions concerning fundamental energy terms and the correlations between them inhibit the effective usage of the KPIs. Therefore, the overarching issue of the security of energy supply and its related subjects were also approached. The primary subject of this paper is the development of two new KPIs to measure and compare the energy grid and system supportability. These KPIs are based on spatio-temporal conditions in their respective grids. The usage and benefits of the developed KPIs are exemplarily highlighted by analyzing the impact of a scenario with the integration of a large-scale heat pump into the electricity and heat grid. The energy grid supportability is determined for each grid, whereas the energy system supportability takes the interactions of the electricity and heat grid into account. The developed KPIs are intended to enable stakeholders to identify areas with optimization potential in energy grids and systems. Moreover, the KPIs can be used to create a standardized evaluation method for regulatory requirements.

Water quality monitoring of large reservoirs in China based on water color change from 1999 to 2021

Water color is an essential indicator of water quality, and research methods of water color based on remote sensing have become a trend in large-scale water quality monitoring. Large reservoirs are important for regional development. However, large-scale research into the quality of the water is lacking. Accordingly, to study the spatiotemporal water quality conditions of large reservoirs in China, this study conducted an inversion of water color over the past 23 years. Using Landsat series images, the Dynamic Surface Water Extent (DSWE) algorithm, and the Forel-Ule index (FUI), the water surface of 746 large reservoirs in China was sampled and the water color was inverted using the Google Earth Engine (GEE) platform. The FUI local change index was established to analyze changes over time. The results showed that the annual average FUI value of large reservoirs in China was less than 11, showing non-eutrophic and green water bodies. From the water color analysis of 91 large Type I reservoirs, it was found that the water quality of the reservoirs in the Northeast region was poor (FUI greater than 12), with many aquatic plants and algae or highly turbid and greenish-brown waters, mainly for flood control and irrigation. In southern China, there were primarily mesotrophic and oligotrophic reservoirs (FUI not greater than 8) with green or greenish-blue water color, which were mainly used for water supply and power generation. The construction of the southern reservoirs reduced the FUI values by an average of five levels, apparently changing the water color. The FUI local change index revealed that the FUI values of large reservoirs in China showed an overall decreasing trend from 1999 to 2007, stabilized after 2007, and then fluctuated after 2013. This study also found that the water color index of large reservoirs in China is positively correlated with the trophic status and turbidity of water bodies to some extent. Therefore, this study can compensate for the lack of water color remote sensing in large-scale reservoir water quality studies and support large-scale reservoir monitoring and management.

Dam break study and its flood risk in Gurara watershed-Nigeria under varied spatio-temporal conditions by integrating HSPF and HEC–RAS models

Dam break study and its flood risk in Gurara watershed-Nigeria under varied spatio-temporal conditions by integrating HSPF and HEC–RAS models

Spatio-Temporal Contextual Conditions Causality and Spread Delay-Aware Modeling for Traffic Flow Prediction

Spatio-Temporal Contextual Conditions Causality and Spread Delay-Aware Modeling for Traffic Flow Prediction