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Assessing the energy performance of existing residential buildings (ERB) has been identified as key to improving building energy efficiency and reducing associated greenhouse gas emissions in Canada. However, identifying optimal retrofit packages requires a significant amount of knowledge of building energy modelling, and it is a time-consuming and laborious process. This paper proposed a data-driven framework that combines machine learning, multi-objective optimization, and multi-criteria decision-making techniques to evaluate the energy performance of ERB and thereby formulate optimal retrofit plans. First, an artificial neural network (ANN) was developed to predict the energy performance of a wide range of retrofit packages. A genetic algorithm was employed to determine the best structure and hyperparameters of the ANN model. Then, the energy consumption results were integrated with environmental and economic impact data to evaluate the environmental and economic performance of retrofit packages and thereby identify Pareto optimal solutions. Finally, a multi-criteria decision-making method was used to select the best retrofit packages among the optimal solutions. The proposed framework was validated using data on a typical residential building in British Columbia, Canada. The results indicated that this framework could effectively predict building energy performance and help decision-makers to make an optimal decision when choosing retrofit packages. [ABSTRACT FROM AUTHOR]

During the last years, computer vision-based diagnosis systems have been widely used in several hospitals and dermatology clinics, aiming mostly at the early detection of malignant melanoma tumor, which is among the most frequent types of skin cancer, versus other types of nonmalignant cutaneous diseases. They grow in melanocytes, the cells responsible for pigmentation. This type of cancer is increasing rapidly and its related mortality rate is increasing more modestly, and inversely proportional to the tumor's thickness. The mortality rate can be decreased by earlier detection of suspicious lesions and better prevention. In this work, we are interested in extracting all specific attributes which can be used for computer-aided diagnosis of melanoma. In the first step of the proposed work, we applied the Dull Razor [Lee T et al., Dullrazor: A software approach to hair removal from images, Cancer Control Research, British Columbia Cancer Agency, Vancouver, Canada, Vol. 21, No. 6, pp. 533–543, 1997] technique to images to reduce the influence of small structures, hairs, bubbles, light reflection. In the second step, a new fuzzy level set algorithm is proposed in order to facilitate the medical image segmentation task. It is able to directly evolve from the initial segmentation proposed that uses a spatial fuzzy clustering approach. The controlling parameters of the level set evolution are also estimated from the results of the fuzzy clustering step. This step is essential to characterize the shape of the lesion and also to locate the tumor to be analyzed. In this paper, we have also treated the necessity to extract all the specific attributes used to develop a characterization methodology that enables specialists to take the best possible diagnosis. For this purpose, our proposal relies largely on visual observation of the tumor while dealing with some characteristics as color, texture or form. The method used in this paper is called ABCD. It requires calculating four factors: Asymmetry (A) , Border (B) , Color (C) , and Diversity (D). Finally, these parameters are used to construct a classification module based on artificial neural network for the recognition of malignant melanoma. [ABSTRACT FROM AUTHOR]

Large and easily accessible porphyry Cu deposits have already been identified, exploited, and gradually exhausted. Novel strategies, therefore, are required to identify new, deeply buried deposits. Previous studies have proposed several lithogeochemical and mineralogical approaches for identifying porphyry Cu systems. Most of these methods, however, require significant a priori knowledge of the exploration region and are, generally, of low effectiveness. In this study, machine learning models using Random Forest and Deep Neural Network algorithms are utilized to characterize magma fertility. The two models have first been trained using a large trace‐element data set of magmatic zircon and then validated on unseen data set during the training process. The performance of both models was evaluated using a fivefold cross‐validation technique, which demonstrates that the models provide consistent results and yield good classification accuracy (∼90% on average) with low false positive rates. Feature importance analysis of the models suggests that Eu/Eu*, Eu/Eu*/Y, Ce/Nd, Ce/Ce*, Dy, Hf, and Ti are the important parameters that distinguish fertile and barren zircons. The real‐world applicability of the validated models was evaluated using two well‐characterized porphyry Cu deposits in subduction and postcollisional settings—the Highland Valley porphyry Cu district (south‐central British Columbia, Canada) and the southern Gangdese belt (Tibet, China), respectively. The results demonstrate that our generalized models can discriminate zircon from igneous rocks associated with porphyry Cu deposits from those in nonmineralized systems with high accuracy and independent of geological setting, suggesting that this new approach can be used effectively in greenfield and brownfield exploration. Plain Language Summary: Mineral resources, which are important to the development of human society, are increasingly being consumed as our need for technological advancement increases. With this consumption comes the need to identify additional resources, many of which may be invisible at the Earth's surface. This challenges current exploration methods, requiring novel techniques to be developed to identify variably mineralized rock below Earth's surface. In this study, machine learning methods are trained using a global data set of zircon chemistry to evaluate the prospectivity of porphyry Cu mineralization in magmatic districts. This work demonstrates that machine learning methods provide a robust, accurate, and effective approach to identifying porphyry Cu mineral resources. Key Points: A global data set of magmatic zircon trace‐element chemistry is used to train two separate machine learning algorithmsThe Eu/Eu*, Eu/Eu*/Y, Ce/Nd, and Ce/Ce* ratios, and Dy, Hf, and Ti contents are important for distinguishing fertile and barren zirconsThe performance of the machine learning models is not affected by regional differences and geological setting [ABSTRACT FROM AUTHOR]

Abstract: A conditional density estimation network (CDEN) is a probabilistic extension of the standard multilayer perceptron neural network (MLP). A CDEN model allows users to estimate parameters of a specified probability density function conditioned upon values of a set of predictors using the MLP architecture. The result is a flexible model for the mean, the variance, exceedance probabilities, prediction intervals, etc. from the specified conditional distribution. Because the CDEN is based on the MLP, nonlinear relationships, including those involving complicated interactions between predictors, can be described by the modeling framework. CDEN models have been applied to a wide range of environmental prediction tasks, such as precipitation downscaling, extreme value analysis in hydrology, wind retrievals from satellites, and air quality forecasting. This paper describes the CaDENCE (Conditional Density Estimation Network Creation and Evaluation) package, which provides routines for creating and evaluating CDEN models in the R programming language. CaDENCE routines are demonstrated on a dataset consisting of suspended sediment concentrations and discharge measurements from the Fraser River at Hope, British Columbia, Canada. [Copyright &y& Elsevier]

The Equity Silver mine site, British Columbia, Canada, is known for its management and treatment of acid rock drainage. The accurate prediction of full-scale seepage flow rate in field conditions is critical for mine site management, flood control, and contaminant treatment strategies. In this study, a machine learning methodology based on artificial neural network (ANN) is proposed to correlate measured seepage flow rate with the weather monitoring data collected from the field of Equity Silver mine. Comparing with traditional numerical and water balance approaches, the ANN approach generally requires much fewer investigations on hydrogeological mechanisms or definitions of parameters and equations. The measured seepage flow rates from the Bessemer Dump and the weather monitoring data during 1998–2017 are used for training the proposed ANN. The calculated results from the trained ANN show good agreement with the measurements. In addition, the proposed ANN approach is further refined by including time tags of the monitoring data in the input layer, which enable the ANN to investigate the effects of long-term weather trends, climate change, and dynamics inside waste rock piles on seepage flow. The prediction results for the following year (2017–2018) from the refined ANN and the original ANN are compared, which shows the improvement of the refined ANN approach. [ABSTRACT FROM AUTHOR]

An approach is presented to estimate corridor travel time in urban areas using available data of a nearby corridor. The purpose is to improve the efficiency of real-time traveller information systems when the number of data collection sensors is limited. Field travel time data were collected for two corridors in downtown Vancouver, British Columbia. The association between the travel times of the two corridors was found significant. Models were then developed to estimate travel times on one corridor using data of the other corridor. The developed models included regression, Artificial Neural Network (ANN), Neuro-fuzzy, and K-Nearest Neighbours (KNN). The estimation accuracy was considered satisfactory as the Mean Absolute Percentage Error (MAPE) of all models ranged between 13.7% and 17.6%. It was concluded that the concept of estimating travel time from nearby corridors is promising. The type of modelling technique had a little impact on the results with the KNN method producing slightly better results [ABSTRACT FROM AUTHOR]

The purpose of this study is to develop a semidistributed, physically based hydrologic model (SDPB_HM) for mountainous watersheds areas. Most of the model's required parameters were acquired using remote sensing and digital terrain elevation data. A three-stage computer classifier model was built based on the error back-propagation artificial neural network approach (EBPANN) and refined to classify the land cover types of the study area. In addition, the reflection properties of the land cover types were used to improve the technique of estimating the net radiation in Morton's evapotranspiration model. A procedure was proposed for discretizing the watershed areas, aimed to increase the homogeneity and minimize the calculation time. The SDPBHM was applied to the Albert River Basin in the Rocky Mountains in British Columbia, Canada. The Albert River meteorological data from October 1986 to September 1987 was used to calibrate the model parameters. In addition, the SDPB_HM was validated using the meteorological data of a case study from October 1987 to September 1988 and from October 1988 to September 1989. Comparison between the simulated and the observed flow at the outlet of river showed a good agreement during these periods. [ABSTRACT FROM AUTHOR]