39 research outputs found
Gap Analysis Based Decision Support Methodology to Improve Level of Service of Water Services
One of water utility’s managerial challenges is to make a balance in between two distinctive managerial goals, cost-effective provision of water service and improving customer satisfaction of water service. As management priorities of the water utility perspective do not reconcile from the customer’s perspective, this gap challenges the sustainable provision of water service. In this study, the new methodology based on a gap analysis was proposed to improve the Overall Level of Service (O-LOS) of water service. Two new indexes (Gap Index [GI] and the Efficiency Index [EI]) were developed to improve the O-LOS and minimize the gap between the customers and the service providers. The methodology proposed in this study is effective in supporting the water utility decisions on budget allocation to make a balance in between the customers’ demand and the service providers’ needs
Large expert-curated database for benchmarking document similarity detection in biomedical literature search
Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe
Detection of convective initiation using Himawari-8 Advanced Himawari Imager data and random forest
Estimation of daily maximum and minimum air temperature using satellite data in mega city scale areas
A deep learning based prediction of Arctic sea ice concentration using satellite and reanalysis data
Arctic sea ice is one of the key factors closely related to climate change and energy balance. The sea ice concentration (SIC) can be used to explore the spatial distribution of sea ice through time. Arctic SIC is highly related to surrounding environments such as atmosphere, ocean, and climate change. Thus, the prediction of Arctic SIC can provide the crucial information related to the dynamic environment of the Arctic and Earth systems. Previous studies have suggested several approaches such as physical and statistical models to predict Arctic SIC. Due to the high complexity of Arctic environments, it is hard to figure out the interaction between sea ice and the atmosphere and/or ocean in detail. Thus, this study suggests a data-driven model using machine learning approaches for predicting SIC. Convolutional neural networks (CNN) and random forest (RF) were used to predict monthly SIC with multiple satellite products and reanalysis data. During the melting season (Jun. to Sep.) of past 16 years (2002-2017), monthly mean data of SIC (NOAA OISST v2), ice temperature (MODIS), and other atmospheric and oceanic factors (ECMWF ERA-interim reanalysis data) were used to predict SIC of the next month. Both models were built with an extensive dataset of Arctic SIC and evaluated using the cross-validation by year. Over the melting season, the performance of CNN was better than RF (4.7% and 7.5% of RMSE, respectively). In the literature, a challenging problem exists when predicting SIC around the ice edge due to its high variability over time. Around the ice edge (latitude < 80??), the CNN model showed a significantly improved result than RF