scholarly journals The sub-annual calibration of hydrological models considering climatic intra-annual variations

2017 ◽  
Author(s):  
Binru Zhao ◽  
Huichao Dai ◽  
Dawei Han ◽  
Guiwen Rong
Keyword(s):  
Time Scale ◽  
Temporal Dynamics ◽  
Model Performance ◽  
Optimal Time ◽  
Temperature Pattern ◽  
Climate Conditions ◽  
Fcm Algorithm ◽  
Calibration Scheme ◽  
Annual Variations ◽  
Improve Model

Abstract. Changing climate leads to change of temporal dynamics of hydrological systems by affecting the catchment conditions. Considering climatic variations when calibrating a hydrological model can improve model performance, which allows parameter sets to vary according to sub-periods with different climate conditions. This study has explored climatic intra-annual variations by using two classification approaches to recognize the sub-periods with similar climatic patterns, Calendar-Based Grouping (CBG) method and Fuzzy C-Means (FCM) algorithm. The model performances of the sub-annual calibration schemes based on these two approaches are compared using the conceptual model IHACRES. The effect of time scales on sub-annual calibration schemes was also studied. Results indicate that the sub-annual calibration scheme based on CBG method performs better than that based on Rainfall-dominated FCM algorithm, since the CBG method has a better performance in recognizing temperature pattern, and the main source of catchment change is from the change of vegetation, which is mainly affected by temperature in the study site. The optimal time scale is dependent on the sub-annual calibration scheme, with bimonthly for CBG method and Temperature-dominated FCM algorithm and seasonal for Rainfall-dominated FCM algorithm. Overall, when using sub-annual calibration schemes, the selection of the partitioning method and time scale is very important to model performances

scholarly journals Combining multiple statistical methods to evaluate the performance of process-based vegetation models across three forest stands

2017 ◽  
Vol 63 (4) ◽  
pp. 153-172 ◽  
Author(s):  
Joanna A. Horemans ◽  
Alexandra Henrot ◽  
Christine Delire ◽  
Chris Kollas ◽  
Petra Lasch-Born ◽  
...  
Keyword(s):  
Time Scale ◽  
Singular Spectrum Analysis ◽  
Model Performance ◽  
Model Errors ◽  
Forest Stands ◽  
Climate Conditions ◽  
Management Actions ◽  
Statistical Measures ◽  
Net Ecosystem Carbon Exchange ◽  
Vegetation Models

AbstractProcess-based vegetation models are crucial tools to better understand biosphere-atmosphere exchanges and ecophysiological responses to climate change. In this contribution the performance of two global dynamic vegetation models, i.e. CARAIB and ISBACC, and one stand-scale forest model, i.e. 4C, was compared to long-term observed net ecosystem carbon exchange (NEE) time series from eddy covariance monitoring stations at three old-grown European beech (Fagus sylvatica L.) forest stands. Residual analysis, wavelet analysis and singular spectrum analysis were used beside conventional scalar statistical measures to assess model performance with the aim of defining future targets for model improvement. We found that the most important errors for all three models occurred at the edges of the observed NEE distribution and the model errors were correlated with environmental variables on a daily scale. These observations point to possible projection issues under more extreme future climate conditions. Recurrent patterns in the residuals over the course of the year were linked to the approach to simulate phenology and physiological evolution during leaf development and senescence. Substantial model errors occurred on the multi-annual time scale, possibly caused by the lack of inclusion of management actions and disturbances. Other crucial processes defined were the forest structure and the vertical light partitioning through the canopy. Further, model errors were shown not to be transmitted from one time scale to another. We proved that models should be evaluated across multiple sites, preferably using multiple evaluation methods, to identify processes that request reconsideration.

scholarly journals Towards Robust Representations of Spatial Networks Using Graph Neural Networks

2021 ◽  
Vol 11 (15) ◽  
pp. 6918
Author(s):  
Chidubem Iddianozie ◽  
Gavin McArdle
Keyword(s):  
Machine Learning ◽  
Model Performance ◽  
Network Models ◽  
Data Representation ◽  
Spatial Networks ◽  
Neural Network Models ◽  
Improve Model ◽  
Graph Neural Networks ◽  
Spatial Entities ◽  
Improve Model Performance

The effectiveness of a machine learning model is impacted by the data representation used. Consequently, it is crucial to investigate robust representations for efficient machine learning methods. In this paper, we explore the link between data representations and model performance for inference tasks on spatial networks. We argue that representations which explicitly encode the relations between spatial entities would improve model performance. Specifically, we consider homogeneous and heterogeneous representations of spatial networks. We recognise that the expressive nature of the heterogeneous representation may benefit spatial networks and could improve model performance on certain tasks. Thus, we carry out an empirical study using Graph Neural Network models for two inference tasks on spatial networks. Our results demonstrate that heterogeneous representations improves model performance for down-stream inference tasks on spatial networks.

scholarly journals Population Pharmacokinetics of Doxycycline in Children

2019 ◽  
Vol 63 (12) ◽  
Author(s):  
Elizabeth J. Thompson ◽  
Huali Wu ◽  
Chiara Melloni ◽  
Stephen Balevic ◽  
Janice E. Sullivan ◽  
...  
Keyword(s):  
Model Performance ◽  
Standard Of Care ◽  
Volume Of Distribution ◽  
Clinical Value ◽  
Final Model ◽  
Childhood Infections ◽  
Obesity Status ◽  
Improve Model ◽  
Using Data ◽  
Improve Model Performance

ABSTRACT Doxycycline is a tetracycline-class antimicrobial labeled by the U.S. Food and Drug Administration for children >8 years of age for many common childhood infections. Doxycycline is not labeled for children ≤8 years of age, due to the association between tetracycline-class antibiotics and tooth staining, although doxycycline may be used off-label under severe conditions. Accordingly, there is a paucity of pharmacokinetic (PK) data to guide dosing in children 8 years and younger. We leveraged opportunistically collected plasma samples after intravenous (i.v.) and oral doxycycline doses received per standard of care to characterize the PK of doxycycline in children of different ages and evaluated the effect of obesity and fasting status on PK parameters. We developed a population PK model of doxycycline using data collected from 47 patients 0 to 18 years of age, including 14 participants ≤8 years. We developed a 1-compartment PK model and found doxycycline clearance to be 3.32 liters/h/70 kg of body weight and volume to be 96.8 liters/70 kg for all patients, comparable to values reported in adults. We estimated a bioavailability of 89.6%, also consistent with adult data. Allometrically scaled clearance and volume of distribution did not differ between children 2 to ≤8 years of age and children >8 to ≤18 years of age, suggesting that younger children may be given the same per-kilogram dosing. Obesity status and fasting status were not selected for inclusion in the final model. Additional doxycycline PK samples collected in future studies may be used to improve model performance and maximize its clinical value.

scholarly journals Online Multilingual Hate Speech Detection: Experimenting with Hindi and English Social Media

2020 ◽  
Author(s):  
Neeraj Vashistha ◽  
Arkaitz Zubiaga
Keyword(s):  
Social Media ◽  
Hate Speech ◽  
Model Performance ◽  
Academic Community ◽  
Human Interaction ◽  
Superior Performance ◽  
Competitive Performance ◽  
Speech Detection ◽  
Improve Model ◽  
Use Of The Internet

The exponential increase in the use of the Internet and social media over the last two decades has changed human interaction. This has led to many positive outcomes, but at the same time it has brought risks and harms. While the volume of harmful content online, such as hate speech, is not manageable by humans, interest in the academic community to investigate automated means for hate speech detection has increased. In this study, we analyse six publicly available datasets by combining them into a single homogeneous dataset and classify them into three classes, abusive, hateful or neither. We create a baseline model and we improve model performance scores using various optimisation techniques. After attaining a competitive performance score, we create a tool which identifies and scores a page with effective metric in near-real time and uses the same as feedback to re-train our model. We prove the competitive performance of our multilingual model on two langauges, English and Hindi, leading to comparable or superior performance to most monolingual models.

scholarly journals Evaluating the impact of grid cell properties in spatial discretization of groundwater model for a tropical karst catchment in Rote Island, Indonesia

2016 ◽  
Vol 48 (6) ◽  
pp. 1757-1772 ◽  
Author(s):  
Dua K. S. Y. Klaas ◽  
Monzur Alam Imteaz ◽  
Arul Arulrajah
Keyword(s):  
Estimation Method ◽  
Model Performance ◽  
Grid Cell ◽  
Specific Yield ◽  
Spatial Discretization ◽  
Observation Well ◽  
Improve Model ◽  
Karst Catchment ◽  
The Impact ◽  
Tropical Karst

Abstract To assess the effect of three grid cell properties (size, mean slope of the surface and distance between centre of grid and observation well) on groundwater models' performances, a tropical karst catchment characterized by monsoonal season in Rote Island, Indonesia was selected. Here, MODFLOW was used to develop models with five different spatial discretization schemes: 10 × 10 m, 20 × 20 m, 30 × 30 m, 40 × 40 m and 50 × 50 m. Using parameter estimation method, hydraulic conductivity and specific yield values over a selection of pilot points were estimated. The trends of the performances were calculated at each observation well in order to recommend the most appropriate location for observation well placement in terms of topographical characteristic. It is confirmed that the deterioration of model performance is mainly controlled by the increase of distance between well and centre of the cell, and the mean slope of the surface. Results reveal that model performance increases substantially for areas of low slope (<3%) and medium slope (3–10%) for a smaller grid cell size. Therefore, to improve model performance, it is recommended that the observations wells are placed in areas of low and medium slopes.

scholarly journals Improving biomedical named entity recognition with syntactic information

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Yuanhe Tian ◽  
Wang Shen ◽  
Yan Song ◽  
Fei Xia ◽  
Min He ◽  
...  
Keyword(s):  
Named Entity Recognition ◽  
Model Performance ◽  
Entity Recognition ◽  
Input Word ◽  
Named Entity ◽  
Improve Model ◽  
Syntactic Information ◽  
Biomedical Texts ◽  
Improve Model Performance ◽  
Biomedical Named Entity Recognition

Abstract Background Biomedical named entity recognition (BioNER) is an important task for understanding biomedical texts, which can be challenging due to the lack of large-scale labeled training data and domain knowledge. To address the challenge, in addition to using powerful encoders (e.g., biLSTM and BioBERT), one possible method is to leverage extra knowledge that is easy to obtain. Previous studies have shown that auto-processed syntactic information can be a useful resource to improve model performance, but their approaches are limited to directly concatenating the embeddings of syntactic information to the input word embeddings. Therefore, such syntactic information is leveraged in an inflexible way, where inaccurate one may hurt model performance. Results In this paper, we propose BioKMNER, a BioNER model for biomedical texts with key-value memory networks (KVMN) to incorporate auto-processed syntactic information. We evaluate BioKMNER on six English biomedical datasets, where our method with KVMN outperforms the strong baseline method, namely, BioBERT, from the previous study on all datasets. Specifically, the F1 scores of our best performing model are 85.29% on BC2GM, 77.83% on JNLPBA, 94.22% on BC5CDR-chemical, 90.08% on NCBI-disease, 89.24% on LINNAEUS, and 76.33% on Species-800, where state-of-the-art performance is obtained on four of them (i.e., BC2GM, BC5CDR-chemical, NCBI-disease, and Species-800). Conclusion The experimental results on six English benchmark datasets demonstrate that auto-processed syntactic information can be a useful resource for BioNER and our method with KVMN can appropriately leverage such information to improve model performance.

Potential for developing independent daytime/nighttime LUR models based on short-term mobile monitoring to improve model performance

2021 ◽  
Vol 268 ◽  
pp. 115951
Author(s):  
Xiangyu Xu ◽  
Ning Qin ◽  
Zhenchun Yang ◽  
Yunwei Liu ◽  
Suzhen Cao ◽  
...  
Keyword(s):  
Model Performance ◽  
Mobile Monitoring ◽  
Short Term ◽  
Improve Model ◽  
Improve Model Performance

scholarly journals Probing Behavior of Diaphorina citri (Hemiptera: Liviidae) on Valencia Orange Influenced by Sex, Color, and Size

2020 ◽  
Vol 20 (2) ◽  
Author(s):  
Timothy A Ebert ◽  
Michael E Rogers
Keyword(s):  
Body Size ◽  
Model Performance ◽  
Diaphorina Citri ◽  
Probing Behavior ◽  
Valencia Orange ◽  
Improve Model ◽  
Candidatus Liberibacter ◽  
Citrus Huanglongbing ◽  
Liberibacter Asiaticus ◽  
Improve Model Performance

Abstract Candidatus Liberibacter asiaticus Jagoueix, Bové, and Garnier (Rhizobiales: Rhizobiaceae) is transmitted by the psyllid Diaphorina citri Kuwayama and putatively causes Huanglongbing disease in citrus. Huanglongbing has reduced yields by 68% relative to pre-disease yields in Florida. Disease management is partly through vector control. Understanding vector biology is essential in this endeavor. Our goal was to document differences in probing behavior linked to sex. Based on both a literature review and our results, we conclude that there is either no effect of sex or that identifying such an effect requires a sample size at least four times larger than standard methodologies. Including both color and sex in statistical models did not improve model performance. Both sex and color are correlated with body size, and body size has not been considered in previous studies on sex in D. citri in terms of probing behavior. An effect of body size was found wherein larger psyllids took longer to reach ingestion behaviors and larger individuals spent more time-ingesting phloem, but these relationships explained little of the variability in these data. We suggest that the effects of sex can be ignored when running EPG experiments on healthy psyllids.

scholarly journals Modelling temporal dynamics of Culicoides Latreille (Diptera: Ceratopogonidae) populations on Reunion Island (Indian Ocean), vectors of viruses of veterinary importance

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Yannick Grimaud ◽  
Hélène Guis ◽  
Frédéric Chiroleu ◽  
Floriane Boucher ◽  
Annelise Tran ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Temporal Dynamics ◽  
Control Strategies ◽  
Host Density ◽  
Environmental Drivers ◽  
Hurdle Model ◽  
Reunion Island ◽  
Annual Variations ◽  
Réunion Island ◽  
Set Up

Abstract Background Reunion Island regularly faces outbreaks of epizootic haemorrhagic disease (EHD) and bluetongue (BT), two viral diseases transmitted by haematophagous midges of the genus Culicoides (Diptera: Ceratopogonidae) to ruminants. To date, five species of Culicoides are recorded in Reunion Island in which the first two are proven vector species: Culicoides bolitinos, C. imicola, C. enderleini, C. grahamii and C. kibatiensis. Meteorological and environmental factors can severely constrain Culicoides populations and activities and thereby affect dispersion and intensity of transmission of Culicoides-borne viruses. The aim of this study was to describe and predict the temporal dynamics of all Culicoides species present in Reunion Island. Methods Between 2016 and 2018, 55 biweekly Culicoides catches using Onderstepoort Veterinary Institute traps were set up in 11 sites. A hurdle model (i.e. a presence/absence model combined with an abundance model) was developed for each species in order to determine meteorological and environmental drivers of presence and abundance of Culicoides. Results Abundance displayed very strong heterogeneity between sites. Average Culicoides catch per site per night ranged from 4 to 45,875 individuals. Culicoides imicola was dominant at low altitude and C. kibatiensis at high altitude. A marked seasonality was observed for the three other species with annual variations. Twelve groups of variables were tested. It was found that presence and/or abundance of all five Culicoides species were driven by common parameters: rain, temperature, vegetation index, forested environment and host density. Other parameters such as wind speed and farm building opening size governed abundance level of some species. In addition, Culicoides populations were also affected by meteorological parameters and/or vegetation index with different lags of time, suggesting an impact on immature stages. Taking into account all the parameters for the final hurdle model, the error rate by Normalized Root mean Square Error ranged from 4.4 to 8.5%. Conclusions To our knowledge, this is the first study to model Culicoides population dynamics in Reunion Island. In the absence of vaccination and vector control strategies, determining periods of high abundance of Culicoides is a crucial first step towards identifying periods at high risk of transmission for the two economically important viruses they transmit.

scholarly journals Using BiLSTM Networks for Context-Aware Deep Sensitivity Labelling on Conversational Data

2020 ◽  
Vol 10 (24) ◽  
pp. 8924
Author(s):  
Antreas Pogiatzis ◽  
Georgios Samakovitis
Keyword(s):  
Conditional Random Fields ◽  
Short Term Memory ◽  
Model Performance ◽  
Design Feature ◽  
Sensitive Information ◽  
Context Aware ◽  
Improve Model ◽  
Unseen Data ◽  
Memory Network ◽  
Series Of Experiments

Information privacy is a critical design feature for any exchange system, with privacy-preserving applications requiring, most of the time, the identification and labelling of sensitive information. However, privacy and the concept of “sensitive information” are extremely elusive terms, as they are heavily dependent upon the context they are conveyed in. To accommodate such specificity, we first introduce a taxonomy of four context classes to categorise relationships of terms with their textual surroundings by meaning, interaction, precedence, and preference. We then propose a predictive context-aware model based on a Bidirectional Long Short Term Memory network with Conditional Random Fields (BiLSTM + CRF) to identify and label sensitive information in conversational data (multi-class sensitivity labelling). We train our model on a synthetic annotated dataset of real-world conversational data categorised in 13 sensitivity classes that we derive from the P3P standard. We parameterise and run a series of experiments featuring word and character embeddings and introduce a set of auxiliary features to improve model performance. Our results demonstrate that the BiLSTM + CRF model architecture with BERT embeddings and WordShape features is the most effective (F1 score 96.73%). Evaluation of the model is conducted under both temporal and semantic contexts, achieving a 76.33% F1 score on unseen data and outperforms Google’s Data Loss Prevention (DLP) system on sensitivity labelling tasks.

Sign in / Sign up

Export Citation Format

Share Document