A hybrid machine learning model for predicting crater width formed by explosions of natural gas pipelines

Despite the existence of industry models for estimating the crater width formed by the explosion of natural gas pipelines, their applicability is still limited since the complex formation mechanisms. In this work, a novel hybrid model was developed to predict crater width formed by explosions of natural gas pipelines, using artificial neural networks (ANN) as the fundamental predictor. Based on the historical accident records, the proposed hybrid model was trained by the pipeline parameter, the operating condition, the installation parameter, and the crater width. A novel nature-inspired optimization algorithm, i.e., the Lévy-Weighted Quantum particle swarm optimization (LWQPSO) algorithm, was proposed to optimize the ANN model's parameters. Three machine learning models were developed for comparative reasons to predict the crater width. The use of precision and error analysis indicators assesses prediction performance. The results show that the proposed hybrid model (LWQPSO-ANN) has high prediction accuracy and stability, which outperforms QPSO-ANN-based benchmark hybrid models and the model without an optimizer (Support Vector Machine, SVM). The parameter sensitivities of the proposed algorithm, including the maximum number of iterations, population size and contraction-expansion coefficient, were determined. The proposed hybrid model is expected to support the quantitative risk assessment (QRA), Right-of-Way (ROW) definition and the inherently safer design of the underground parallel pipelines.     

New approaches to modelling fish-habitat relationships

Ecologists often develop models that describe the relationship between faunal communities and their habitat. Coral reef fishes have been the focus of numerous such studies, which have used a wide range of statistical tools to answer an equally wide range of questions. Here, we apply a series of both conventional statistical techniques (linear and generalized additive regression models) and novel machine-learning techniques (the support vector machine and three ensemble techniques used with regression trees) to predict fish species richness, biomass, and diversity from a range of habitat variables. We compare the techniques in terms of their predictive performance, and we compare a subset of the models in terms of the influence each habitat variable has for the predictions. Prediction errors are estimated by cross-validation, and variable importance is assessed using permutations of individual variable values. For predictions of species richness and diversity the tree-based models generally and the random forest model specifically are superior (produce the lowest errors). These model types are all able to model both nonlinear and interaction effects. The linear model, unable to model either effect type, performs the worst (produces the highest errors). For predictions of biomass, the generalized additive model is superior, and the support vector machine performs the worst. Depth range, the difference between maximum and minimum water depth at a given site, is identified as the most important variable in the majority of models predicting the three fish community variables. However, variable importance is highly dependent upon model type, which leads to questions regarding the interpretation of variable importance and its proper use as an indicator of causality. The representation of ecological relationships by tree-based ensemble learners will improve predictive performance, and provide a new avenue for exploring ecological relationships, both statistical and causal.