scholarly journals Fishing trip cost modeling using generalized linear model and machine learning methods – A case study with longline fisheries in the Pacific and an application in Regulatory Impact Analysis

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257027
Author(s):  
Hing Ling Chan ◽  
Minling Pan
Keyword(s):  
Machine Learning ◽  
Pacific Ocean ◽  
Linear Model ◽  
Generalized Linear Model ◽  
Predictive Power ◽  
Longline Fishery ◽  
Central Pacific ◽  
Fishing Trip ◽  
Modeling Approach ◽  
Longline Fisheries

Fishing trip cost is an important element in evaluating economic performance of fisheries, assessing economic effects from fisheries management alternatives, and serving as input for ecosystem and bioeconomic modeling. However, many fisheries have limited trip-level data due to low observer coverage. This article introduces a generalized linear model (GLM) utilizing machine learning (ML) techniques to develop a modeling approach to estimate the functional forms and predict the fishing trip costs of unsampled trips. GLM with Lasso regularization and ML cross-validation of model are done simultaneously for predictor selection and evaluation of the predictive power of a model. This modeling approach is applied to estimate the trip-level fishing costs using the empirical sampled trip costs and the associated trip-level fishing operational data and vessel characteristics in the Hawaii and American Samoa longline fisheries. Using this approach to build models is particularly important when there is no strong theoretical guideline on predictor selection. Also, the modeling approach addresses the issue of skewed trip cost data and provides predictive power measurement, compared with the previous modeling efforts in trip cost estimation for the Hawaii longline fishery. As a result, fishing trip costs for all trips in the fishery can be estimated. Lastly, this study applies the estimated trip cost model to conduct an empirical analysis to evaluate the impacts on trip costs due to spatial regulations in the Hawaii longline fishery. The results show that closing the Western and Central Pacific Ocean (WCPO) could induce an average 14% increase in fishing trip costs, while the trip cost impacts of the Eastern Pacific Ocean (EPO) closures could be lower.

Machine learning and generalized linear model techniques to predict aboveground biomass in Amazon rainforest using LiDAR data

2020 ◽  
Vol 14 (03) ◽  
Author(s):  
Mateus Schuh ◽  
José Augusto Spiazzi Favarin ◽  
Juliana Marchesan ◽  
Elisiane Alba ◽  
Elias Fernando Berra ◽  
...  
Keyword(s):  
Machine Learning ◽  
Linear Model ◽  
Aboveground Biomass ◽  
Generalized Linear Model ◽  
Amazon Rainforest ◽  
Lidar Data

scholarly journals Predicting Fatalities in Air Accidents using CHAID XGBoost Generalized Linear Model Neural Network and Ensemble Models of Machine Learning

2020 ◽  
Vol 9 (3S) ◽  
pp. 35-39
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Linear Model ◽  
Generalized Linear Model ◽  
Historical Data ◽  
Random Trees ◽  
Ensemble Model ◽  
Human Beings ◽  
Hidden Layer ◽  
Learning Principles

The study examines the historical data of about 4700 air crashes all over the world since the first recorded air crash of 1908. Given the immense impact on human beings as well as companies, the study aimed at utilizing Machine Learning principles for predicting fatalities. The train-test partition used was 75-25. Employing the IBM SPSS Modeler, the machine learning models used included CHAID model, Neural Network, Generalized Linear Model, XGBoost, Random Trees and the Ensemble model to predict fatalities in air crashes. The best results (90.6% accuracy) were achieved through Neural Network with one hidden layer. The results presented also include comparison of the predicted versus observed results for the test data.

Machine learning based on extended generalized linear model applied in mixture experiments

2019 ◽  
pp. 1-15
Author(s):  
Gilberto Rodrigues Liska ◽  
Marcelo Ângelo Cirillo ◽  
Fortunato Silva de Menezes ◽  
Julio Silvio de Sousa Bueno Filho
Keyword(s):  
Machine Learning ◽  
Linear Model ◽  
Generalized Linear Model ◽  
Mixture Experiments

scholarly journals Evaluation of the Impacts of Climate Change on Albacore Distribution in the South Pacific Ocean by Using Ensemble Forecast

2021 ◽  
Vol 8 ◽  
Author(s):  
Yi-Jay Chang ◽  
Jhen Hsu ◽  
Po-Kai Lai ◽  
Kuo-Wei Lan ◽  
Wen-Pei Tsai
Keyword(s):  
Climate Change ◽  
Pacific Ocean ◽  
Relative Abundance ◽  
General Circulation ◽  
South Pacific ◽  
The South ◽  
Central Pacific ◽  
South Pacific Ocean ◽  
Rcp 8.5 ◽  
Longline Fisheries

South Pacific albacore (Thunnus alalunga) is a highly migratory tuna species widely distributed throughout 0°–50°S in the South Pacific Ocean. Climate-driven changes in the oceanographic condition largely influence the albacore distribution, relative abundance, and the consequent availability by the longline fisheries. In this study, we examined the habitat preference and spatial distribution of south Pacific albacore using a generalized additive model fitted to the longline fisheries data from the Western and Central Pacific Fisheries Commission (WCPFC) and Inter-American Tropical Tuna Commission (IATTC). Future projections of albacore distributions (2020, 2050, and 2080) were predicted by using an ensemble modeling approach produced from various atmosphere-ocean general circulation models and anthropogenic emission scenarios (i.e., RCP 4.5 and RCP 8.5) to reduce the uncertainty in the projected changes. The dissolved oxygen concentration at 100 meters (DO100) and sea surface temperature (SST) were found to have the most substantial effects on the potential albacore distribution that the albacore preferred in the habitat with DO100 of 0.2–0.25 mmol L–1 and SST of 13–22°C. This study suggested that the northern boundary of albacore preferred habitat is expected to shift southward by about 5° latitudes, and the relative abundance is expected to gradually increase in the area south of 30°S from 2020 to 2080 for both RCP scenarios, especially with a higher degree of change for the RCP 8.5. Moreover, the albacore relative abundance is projected to decrease in the most exclusive economic zones (EEZs) of countries and territories in the South Pacific Ocean by 2080. These findings could lend important implications on the availability of tuna resources to the fisheries and subsequent evaluation of tuna conservation and management under climate change.

scholarly journals Mapping wind erosion hazard with regression-based machine learning algorithms

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Hamid Gholami ◽  
Aliakbar Mohammadifar ◽  
Dieu Tien Bui ◽  
Adrian L. Collins
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Linear Model ◽  
Generalized Linear Model ◽  
Wind Erosion ◽  
Generalized Additive Model ◽  
Additive Model ◽  
Support Vector ◽  
Erosion Hazard ◽  
Stochastic Gradient Boosting

AbstractLand susceptibility to wind erosion hazard in Isfahan province, Iran, was mapped by testing 16 advanced regression-based machine learning methods: Robust linear regression (RLR), Cforest, Non-convex penalized quantile regression (NCPQR), Neural network with feature extraction (NNFE), Monotone multi-layer perception neural network (MMLPNN), Ridge regression (RR), Boosting generalized linear model (BGLM), Negative binomial generalized linear model (NBGLM), Boosting generalized additive model (BGAM), Spline generalized additive model (SGAM), Spike and slab regression (SSR), Stochastic gradient boosting (SGB), support vector machine (SVM), Relevance vector machine (RVM) and the Cubist and Adaptive network-based fuzzy inference system (ANFIS). Thirteen factors controlling wind erosion were mapped, and multicollinearity among these factors was quantified using the tolerance coefficient (TC) and variance inflation factor (VIF). Model performance was assessed by RMSE, MAE, MBE, and a Taylor diagram using both training and validation datasets. The result showed that five models (MMLPNN, SGAM, Cforest, BGAM and SGB) are capable of delivering a high prediction accuracy for land susceptibility to wind erosion hazard. DEM, precipitation, and vegetation (NDVI) are the most critical factors controlling wind erosion in the study area. Overall, regression-based machine learning models are efficient techniques for mapping land susceptibility to wind erosion hazards.

scholarly journals Open Application of Statistical and Machine Learning Models to Explore the Impact of Environmental Exposures on Health and Disease: An Asthma Use Case

2021 ◽  
Vol 18 (21) ◽  
pp. 11398
Author(s):  
Bo Lan ◽  
Perry Haaland ◽  
Ashok Krishnamurthy ◽  
David B. Peden ◽  
Patrick L. Schmitt ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Linear Model ◽  
Generalized Linear Model ◽  
Environmental Exposures ◽  
Predictor Variables ◽  
Use Case ◽  
Airborne Particulate ◽  
Particulate Exposure ◽  
The Impact

ICEES (Integrated Clinical and Environmental Exposures Service) provides a disease-agnostic, regulatory-compliant approach for openly exposing and analyzing clinical data that have been integrated at the patient level with environmental exposures data. ICEES is equipped with basic features to support exploratory analysis using statistical approaches, such as bivariate chi-square tests. We recently developed a method for using ICEES to generate multivariate tables for subsequent application of machine learning and statistical models. The objective of the present study was to use this approach to identify predictors of asthma exacerbations through the application of three multivariate methods: conditional random forest, conditional tree, and generalized linear model. Among seven potential predictor variables, we found five to be of significant importance using both conditional random forest and conditional tree: prednisone, race, airborne particulate exposure, obesity, and sex. The conditional tree method additionally identified several significant two-way and three-way interactions among the same variables. When we applied a generalized linear model, we identified four significant predictor variables, namely prednisone, race, airborne particulate exposure, and obesity. When ranked in order by effect size, the results were in agreement with the results from the conditional random forest and conditional tree methods as well as the published literature. Our results suggest that the open multivariate analytic capabilities provided by ICEES are valid in the context of an asthma use case and likely will have broad value in advancing open research in environmental and public health.

scholarly journals A generalized linear model for multivariate correlated binary response data on mobility index

2018 ◽  
Vol 52 (1) ◽  
pp. 61-73
Author(s):  
MD NAZIR UDDIN ◽  
MUNNI BEGUM
Keyword(s):  
Linear Model ◽  
Generalized Linear Model ◽  
Joint Modeling ◽  
Binary Response ◽  
Probability Models ◽  
Binary Responses ◽  
Mobility Index ◽  
Modeling Approach ◽  
Correlated Binary Responses ◽  
Response Variables

Dependence in multivariate binary outcomes in longitudinal data is a challenging and an important issue to address. Numerous studies have been performed to test the dependence in binary responses either using conditional or marginal probability models. Since the con- ditional and marginal approach provide inadequate or misleading results, the joint models based on both are implemented for bivariate correlated binary responses. In the current paper, we consider a joint modeling approach and a generalized linear model (GLM) for tri-variate correlated binary responses. The link function of the GLM is used to test the dependence of response variables. The mobility index with two categories, no difficulty and difficulty, over the duration of three waves of Health and Retirement Survey (HRS) is chosen as the binary response variable. Initial analysis with Marshall-Olkin correlation coefficients and logistic regression coefficients provide moderate correlation in mobility indices implying dependence in the response variables. We also found statistically significant dependence among the response variables using the joint modeling approach. The mobility at current wave not only depends on the previous mobility status, but also depends on covariates such as age, gender, and race.

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