River discharge data offer a rich source of information for reservoir management and flood control, if modelling can separate out the effects of rainfall, land use, soil type, relief, and weather conditions. In this p...River discharge data offer a rich source of information for reservoir management and flood control, if modelling can separate out the effects of rainfall, land use, soil type, relief, and weather conditions. In this paper, we model river discharge data from the Black Volta River, using Generalised Additive Mixed Models (GAMMs) with a space-time interaction represented via a tensor product of continuous time and discrete space. River discharge data from January 2000 to December 2009 for the four gauge stations along the Black Volta River namely, Lawra, Chache, Bui and Bamboi were obtained from the hydrological services department of Ghana and used for model fitting. Four GAMMs were explored, two with space-time interactions and two without space-time interactions. The comparison of the performance of the models with space-time interactions and those without space-time interactions based on Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) suggests that in this application, the former is better overall and in particular for modelling local variations. Further, a model with space and time main effects performed better compared with one without space and time main effects. After model selection, checking and validation, there is evidence for increasing river discharge from the most upstream gauge station to the most downstream gauge station for the study period.展开更多
Gamma Mixture Model(GaMM)is a useful tool for representing complex distributions.However,estimating the parameters of GaMM faces challenges due to the lack of closed-form solution for the shape parameter.Existing para...Gamma Mixture Model(GaMM)is a useful tool for representing complex distributions.However,estimating the parameters of GaMM faces challenges due to the lack of closed-form solution for the shape parameter.Existing parameter estimation methods face limitations stemming from their reliance on approximate computations,which degrade estimation accuracy,as well as the inherent complexity of numerical calculations,leading to computational inefficiency.To address these limitations and fully consider the multimodal nature of big data,this paper proposes a Mode-Partitioned GaMM(MP-GaMM)estimation method for large-scale multimodal data.The MP-GaMM method explores the spatial distribution characteristics of the data through clustering to partition the data into distinct modes,addresses mode overlap with a tune-up strategy,and employs closed-form estimator for parameter estimation of each mode in parallel.Experimental results demonstrate the rationality and effectiveness of the proposed MP-GaMM method,which outperforms existing methods in both accuracy and computational efficiency.Specifically,MP-GaMM exhibits lower error metrics,higher log-likelihood values and shorter runtime,indicating its capability to provide a more accurate estimation of the model parameters,and more precise characterization of the multimodal nature of large-scale data.展开更多
n this paper, a neural network with a topology of the 16×16 Gamma element is described. A 256×256rearrangeable switching network using Gamma element is proposed to implement high-performance switching.Blocki...n this paper, a neural network with a topology of the 16×16 Gamma element is described. A 256×256rearrangeable switching network using Gamma element is proposed to implement high-performance switching.Blocking problms in telephone switching systems by means of a multi layer array of Gamma are overcome.展开更多
文摘River discharge data offer a rich source of information for reservoir management and flood control, if modelling can separate out the effects of rainfall, land use, soil type, relief, and weather conditions. In this paper, we model river discharge data from the Black Volta River, using Generalised Additive Mixed Models (GAMMs) with a space-time interaction represented via a tensor product of continuous time and discrete space. River discharge data from January 2000 to December 2009 for the four gauge stations along the Black Volta River namely, Lawra, Chache, Bui and Bamboi were obtained from the hydrological services department of Ghana and used for model fitting. Four GAMMs were explored, two with space-time interactions and two without space-time interactions. The comparison of the performance of the models with space-time interactions and those without space-time interactions based on Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) suggests that in this application, the former is better overall and in particular for modelling local variations. Further, a model with space and time main effects performed better compared with one without space and time main effects. After model selection, checking and validation, there is evidence for increasing river discharge from the most upstream gauge station to the most downstream gauge station for the study period.
基金supported by the Natural Science Foundation of Guangdong Province(No.2023A1515011667)the Basic Research Foundation of Shenzhen(No.JCYJ20210324093609026)+1 种基金Guangdong Basic and Applied Basic Research Foundation(No.2023B1515120020)the Science and Technology Major Project of Shenzhen(No.KJZD20230923114809020).
文摘Gamma Mixture Model(GaMM)is a useful tool for representing complex distributions.However,estimating the parameters of GaMM faces challenges due to the lack of closed-form solution for the shape parameter.Existing parameter estimation methods face limitations stemming from their reliance on approximate computations,which degrade estimation accuracy,as well as the inherent complexity of numerical calculations,leading to computational inefficiency.To address these limitations and fully consider the multimodal nature of big data,this paper proposes a Mode-Partitioned GaMM(MP-GaMM)estimation method for large-scale multimodal data.The MP-GaMM method explores the spatial distribution characteristics of the data through clustering to partition the data into distinct modes,addresses mode overlap with a tune-up strategy,and employs closed-form estimator for parameter estimation of each mode in parallel.Experimental results demonstrate the rationality and effectiveness of the proposed MP-GaMM method,which outperforms existing methods in both accuracy and computational efficiency.Specifically,MP-GaMM exhibits lower error metrics,higher log-likelihood values and shorter runtime,indicating its capability to provide a more accurate estimation of the model parameters,and more precise characterization of the multimodal nature of large-scale data.
文摘n this paper, a neural network with a topology of the 16×16 Gamma element is described. A 256×256rearrangeable switching network using Gamma element is proposed to implement high-performance switching.Blocking problms in telephone switching systems by means of a multi layer array of Gamma are overcome.
