scholarly journals Peer Review #1 of "Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables (v0.2)"

2018 ◽  
Keyword(s):  
Random Forest ◽  
Peer Review ◽  
Predictive Modeling ◽  
Generic Framework ◽  
Temporal Variables ◽  
Spatio Temporal

scholarly journals Influence of Random Forest Hyperparameterization on Short-Term Runoff Forecasting in an Andean Mountain Catchment

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 238
Author(s):  
Pablo Contreras ◽  
Johanna Orellana-Alvear ◽  
Paul Muñoz ◽  
Jörg Bendix ◽  
Rolando Célleri
Keyword(s):  
Random Forest ◽  
Model Performance ◽  
Early Warning Systems ◽  
Point Of View ◽  
Lead Times ◽  
Physical Parameters ◽  
Runoff Forecasting ◽  
Spatio Temporal ◽  
Improved Model ◽  
Search Approach

The Random Forest (RF) algorithm, a decision-tree-based technique, has become a promising approach for applications addressing runoff forecasting in remote areas. This machine learning approach can overcome the limitations of scarce spatio-temporal data and physical parameters needed for process-based hydrological models. However, the influence of RF hyperparameters is still uncertain and needs to be explored. Therefore, the aim of this study is to analyze the sensitivity of RF runoff forecasting models of varying lead time to the hyperparameters of the algorithm. For this, models were trained by using (a) default and (b) extensive hyperparameter combinations through a grid-search approach that allow reaching the optimal set. Model performances were assessed based on the R2, %Bias, and RMSE metrics. We found that: (i) The most influencing hyperparameter is the number of trees in the forest, however the combination of the depth of the tree and the number of features hyperparameters produced the highest variability-instability on the models. (ii) Hyperparameter optimization significantly improved model performance for higher lead times (12- and 24-h). For instance, the performance of the 12-h forecasting model under default RF hyperparameters improved to R2 = 0.41 after optimization (gain of 0.17). However, for short lead times (4-h) there was no significant model improvement (0.69 < R2 < 0.70). (iii) There is a range of values for each hyperparameter in which the performance of the model is not significantly affected but remains close to the optimal. Thus, a compromise between hyperparameter interactions (i.e., their values) can produce similar high model performances. Model improvements after optimization can be explained from a hydrological point of view, the generalization ability for lead times larger than the concentration time of the catchment tend to rely more on hyperparameterization than in what they can learn from the input data. This insight can help in the development of operational early warning systems.

Field hockey from the performance analyst’s perspective: A systematic review

2021 ◽  
pp. 174795412110089
Author(s):  
Felicity Lord ◽  
David B Pyne ◽  
Marijke Welvaert ◽  
Jocelyn K Mara
Keyword(s):  
Systematic Review ◽  
Performance Analysis ◽  
Best Practice ◽  
Holistic Approach ◽  
Field Hockey ◽  
Sport Science ◽  
Analysis Techniques ◽  
Analysis Methods ◽  
Temporal Variables ◽  
Spatio Temporal

Field hockey is an evolving sport, but it is unclear whether performance analysis techniques are reflective of current best practice. The objective of this review was to identify performance analysis methods used in field hockey, assess their practicality, and provide recommendations on their implementation in the field. A systematic search of the databases SPORTDiscus, Web of Science, Scopus, MEDLINE and PubMed was performed. Key words addressed performance analysis methods and field hockey, with all other disciplines of sport science excluded. A total of 8 articles were identified from the systematic review. Three studies explored patterns of play in relation to goal scoring opportunities, two articles examined penalty corner strategies and three compared specific actions in hockey. The limited performance analysis research in field hockey has focused on game actions in patterns of play. However, greater insights may be gained by analysing hockey using a holistic approach that incorporates spatio-temporal variables and player-opposition interactions. There is an opportunity to employ novel performance analysis techniques in hockey which provide more practical and effective approaches for analysing strategies and tactics.

scholarly journals mLoc-mRNA: predicting multiple sub-cellular localization of mRNAs using random forest algorithm coupled with feature selection via elastic net

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Prabina Kumar Meher ◽  
Anil Rai ◽  
Atmakuri Ramakrishna Rao
Keyword(s):  
Random Forest ◽  
Cellular Localization ◽  
Learning Algorithm ◽  
Elastic Net ◽  
Messenger Rnas ◽  
Computational Tool ◽  
Temporal Gene Expression ◽  
Single Location ◽  
Spatio Temporal ◽  
Localization Prediction

Abstract Background Localization of messenger RNAs (mRNAs) plays a crucial role in the growth and development of cells. Particularly, it plays a major role in regulating spatio-temporal gene expression. The in situ hybridization is a promising experimental technique used to determine the localization of mRNAs but it is costly and laborious. It is also a known fact that a single mRNA can be present in more than one location, whereas the existing computational tools are capable of predicting only a single location for such mRNAs. Thus, the development of high-end computational tool is required for reliable and timely prediction of multiple subcellular locations of mRNAs. Hence, we develop the present computational model to predict the multiple localizations of mRNAs. Results The mRNA sequences from 9 different localizations were considered. Each sequence was first transformed to a numeric feature vector of size 5460, based on the k-mer features of sizes 1–6. Out of 5460 k-mer features, 1812 important features were selected by the Elastic Net statistical model. The Random Forest supervised learning algorithm was then employed for predicting the localizations with the selected features. Five-fold cross-validation accuracies of 70.87, 68.32, 68.36, 68.79, 96.46, 73.44, 70.94, 97.42 and 71.77% were obtained for the cytoplasm, cytosol, endoplasmic reticulum, exosome, mitochondrion, nucleus, pseudopodium, posterior and ribosome respectively. With an independent test set, accuracies of 65.33, 73.37, 75.86, 72.99, 94.26, 70.91, 65.53, 93.60 and 73.45% were obtained for the respective localizations. The developed approach also achieved higher accuracies than the existing localization prediction tools. Conclusions This study presents a novel computational tool for predicting the multiple localization of mRNAs. Based on the proposed approach, an online prediction server “mLoc-mRNA” is accessible at http://cabgrid.res.in:8080/mlocmrna/. The developed approach is believed to supplement the existing tools and techniques for the localization prediction of mRNAs.

scholarly journals Spatio-temporal modelling for non-stationary point referenced data

2021 ◽  
Author(s):  
Lindsay Morris
Keyword(s):  
Random Forest ◽  
Goodness Of Fit ◽  
Clustering Algorithm ◽  
Dependence Structure ◽  
Geostatistical Model ◽  
Study Region ◽  
Global Estimate ◽  
Covariance Functions ◽  
Power Of The Test ◽  
Spatio Temporal

<p><b>Spatial and spatio-temporal phenomena are commonly modelled as Gaussian processes via the geostatistical model (Gelfand & Banerjee, 2017). In the geostatistical model the spatial dependence structure is modelled using covariance functions. Most commonly, the covariance functions impose an assumption of spatial stationarity on the process. That means the covariance between observations at particular locations depends only on the distance between the locations (Banerjee et al., 2014). It has been widely recognized that most, if not all, processes manifest spatially nonstationary covariance structure Sampson (2014). If the study domain is small in area or there is not enough data to justify more complicated nonstationary approaches, then stationarity may be assumed for the sake of mathematical convenience (Fouedjio, 2017). However, relationships between variables can vary significantly over space, and a ‘global’ estimate of the relationships may obscure interesting geographical phenomena (Brunsdon et al., 1996; Fouedjio, 2017; Sampson & Guttorp, 1992). </b></p> <p>In this thesis, we considered three non-parametric approaches to flexibly account for non-stationarity in both spatial and spatio-temporal processes. First, we proposed partitioning the spatial domain into sub-regions using the K-means clustering algorithm based on a set of appropriate geographic features. This allowed for fitting separate stationary covariance functions to the smaller sub-regions to account for local differences in covariance across the study region. Secondly, we extended the concept of covariance network regression to model the covariance matrix of both spatial and spatio-temporal processes. The resulting covariance estimates were found to be more flexible in accounting for spatial autocorrelation than standard stationary approaches. The third approach involved geographic random forest methodology using a neighbourhood structure for each location constructed through clustering. We found that clustering based on geographic measures such as longitude and latitude ensured that observations that were too far away to have any influence on the observations near the locations where a local random forest was fitted were not selected to form the neighbourhood. </p> <p>In addition to developing flexible methods to account for non-stationarity, we developed a pivotal discrepancy measure approach for goodness-of-fit testing of spatio-temporal geostatistical models. We found that partitioning the pivotal discrepancy measures increased the power of the test.</p>

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