Comparing Machine Learning Regression Techniques for Transmission-Related Storm Outages

2018 ◽  
Author(s):  
Caitlyn E. Clark ◽  
Bryony DuPont
Keyword(s):  
Machine Learning ◽  
Cost Benefit Analysis ◽  
Cost Benefit ◽  
Weather Data ◽  
Support Vector ◽  
Regression Methods ◽  
Data Points ◽  
Regression Techniques ◽  
Local Weather

In this study, we characterize machine learning regression techniques for their ability to predict storm-related transmission outages based on local weather and transmission outage data. To test the machine learning regression techniques, we use data from the central Oregon Coast — which is particularly vulnerable to storm-related transmission outages — for a case study. We test multiple regression methods (linear and polynomial models with varying degrees) as well as support vector regression methods using linear, polynomial, and Radial-Basis-Function kernels. Results indicate relatively poor prediction capability by these methods, but this is attributed to the lack of outage data (characteristic of low-probability, high-risk events), and a cluster of data points representing momentary (<0 seconds) outages. More long-term outage data could lead to better characterization of the models, enabling others to quantify the frequency of storm-related transmission outages based on local weather data. Only by understanding the frequency of these occurrences can a cost-benefit analysis for potential transmission upgrades or generation sources be completed.

Artificial Intelligence in the Hospitality Sector

2021 ◽  
pp. 257-278
Author(s):  
Debdutta Choudhury
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cost Benefit Analysis ◽  
Cost Benefit ◽  
Changing Roles ◽  
Hospitality Sector ◽  
Long Term Impact ◽  
The Cost ◽  
And Robotics

Hospitality is one of the most important sectors of the economy and offers employment to thousands of people. The recent advances in technology has seen that quite a few of the players in this industry have successfully deployed artificial intelligence, machine learning, and robotics. This chapter delves into the details of such deployment in the various processes in this sector and discusses the short-term, medium-term, and long-term impact of these technologies on all the major stakeholders of this industry. The author also looks at the cost benefit analysis of this technologies and concludes that most players sooner, rather than later would be forced by competition to strongly adopt them. The chapter also briefly discusses the changing roles of human employees in this scenario.

scholarly journals Long-term streamflow forecasting for the Cascade Reservoir System of Han River using SWAT with CFS output

2018 ◽  
Vol 50 (2) ◽  
pp. 655-671
Author(s):  
Tian Liu ◽  
Yuanfang Chen ◽  
Binquan Li ◽  
Yiming Hu ◽  
Hui Qiu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Assessment Tool ◽  
Flow Simulation ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Streamflow Forecasting ◽  
Distribution Models ◽  
Han River ◽  
Reservoir System

Abstract Due to the large uncertainties of long-term precipitation prediction and reservoir operation, it is difficult to forecast long-term streamflow for large basins with cascade reservoirs. In this paper, a framework coupling the original Climate Forecasting System (CFS) precipitation with the Soil and Water Assessment Tool (SWAT) was proposed to forecast the nine-month streamflow for the Cascade Reservoir System of Han River (CRSHR) including Shiquan, Ankang and Danjiangkou reservoirs. First, CFS precipitation was tested against the observation and post-processed through two machine learning algorithms, random forest and support vector regression. Results showed the correlation coefficients between the monthly areal CFS precipitation (post-processed) and observation were 0.91–0.96, confirming that CFS precipitation post-processing using machine learning was not affected by the extended forecast period. Additionally, two precipitation spatio-temporal distribution models, original CFS and similar historical observation, were adopted to disaggregate the processed monthly areal CFS precipitation to daily subbasin-scale precipitation. Based on the reservoir restoring flow, the regional SWAT was calibrated for CRSHR. The Nash–Sutcliffe efficiencies for three reservoirs flow simulation were 0.86, 0.88 and 0.84, respectively, meeting the accuracy requirement. The experimental forecast showed that for three reservoirs, long-term streamflow forecast with similar historical observed distribution was more accurate than that with original CFS.

scholarly journals Machine Learning Analytics of Resting-State Functional Connectivity Predicts Survival Outcomes of Glioblastoma Multiforme Patients

2021 ◽  
Vol 12 ◽  
Author(s):  
Bidhan Lamichhane ◽  
Andy G. S. Daniel ◽  
John J. Lee ◽  
Daniel S. Marcus ◽  
Joshua S. Shimony ◽  
...  
Keyword(s):  
Machine Learning ◽  
Glioblastoma Multiforme ◽  
Functional Connectivity ◽  
Resting State ◽  
Learning Analytics ◽  
Support Vector ◽  
Resting State Functional Connectivity ◽  
Term Survival ◽  
Long Term Survival

Glioblastoma multiforme (GBM) is the most frequently occurring brain malignancy. Due to its poor prognosis with currently available treatments, there is a pressing need for easily accessible, non-invasive techniques to help inform pre-treatment planning, patient counseling, and improve outcomes. In this study we determined the feasibility of resting-state functional connectivity (rsFC) to classify GBM patients into short-term and long-term survival groups with respect to reported median survival (14.6 months). We used a support vector machine with rsFC between regions of interest as predictive features. We employed a novel hybrid feature selection method whereby features were first filtered using correlations between rsFC and OS, and then using the established method of recursive feature elimination (RFE) to select the optimal feature subset. Leave-one-subject-out cross-validation evaluated the performance of models. Classification between short- and long-term survival accuracy was 71.9%. Sensitivity and specificity were 77.1 and 65.5%, respectively. The area under the receiver operating characteristic curve was 0.752 (95% CI, 0.62–0.88). These findings suggest that highly specific features of rsFC may predict GBM survival. Taken together, the findings of this study support that resting-state fMRI and machine learning analytics could enable a radiomic biomarker for GBM, augmenting care and planning for individual patients.

scholarly journals MEASURING THE EFFICIENCY OF SOCIAL ENTREPRENEURSHIP

2020 ◽  
pp. 193-198
Author(s):  
A. N. Timokhovich ◽  
O. I. Nikuradze
Keyword(s):  
Social Entrepreneurship ◽  
Cost Benefit Analysis ◽  
Cost Benefit ◽  
Social Value ◽  
Social Entrepreneurs ◽  
Long Term Effect ◽  
The Social ◽  
Common Problems ◽  
Definition Of

The problems of measuring the efficiency of social entrepreneurship have been affected. The aim of the study is to identify the most relevant methods for measuring social value and evaluating the effects that arise as a result of the activities of social organizations. Various interpretations of the definition of the term “social entrepreneurship” have been given in the article. The main elements of the process of social entrepreneurship, features of the goal setting and risks of activities in the study area have been emphasized. The stages of planning activities in the field of social entrepreneurship have been described. The most common problems of measurements and evaluation of social effects that social entrepreneurs have to deal with in the process of carrying out activities related to the implementation of social projects: difficulty in achieving a quantitative evaluation, difficulty in predicting the long-term effect of activities, limitations on costs, time resources, indicators of accuracy and interpretation of results have been revealed. Problems in forecasting the effectiveness of social projects have been identified. The main methods that can be used by social entrepreneurs and organizations for measuring the social value and assessing impact of ongoing activities (method of cost-benefit analysis, method of social accounting, method of social return on investment, method of analysis of the main resources of efficiency) have been analysed. Recommendations for social entrepreneurs have been formulated.

scholarly journals Support Vector Regression Method for Regional Economic Mid- and Long-Term Predictions Based on Wireless Network Communication

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Lingyu Dong
Keyword(s):  
Machine Learning ◽  
Communication Network ◽  
Support Vector Regression ◽  
Wireless Network ◽  
Regression Method ◽  
Network Communication ◽  
Support Vector ◽  
Network Coverage ◽  
Data Packets

In recent years, wireless sensor network technology has continued to develop, and it has become one of the research hotspots in the information field. People have higher and higher requirements for the communication rate and network coverage of the communication network, which also makes the problems of limited wireless mobile communication network coverage and insufficient wireless resource utilization efficiency become increasingly prominent. This article is aimed at studying a support vector regression method for long-term prediction in the context of wireless network communication and applying the method to regional economy. This article uses the contrast experiment method and the space occupancy rate algorithm, combined with the vector regression algorithm of machine learning. Research on the laws of machine learning under the premise of less sample data solves the problem of the lack of a unified framework that can be referred to in machine learning with limited samples. The experimental results show that the distance between AP1 and AP2 is 0.4 m, and the distance between AP2 and Client2 is 0.6 m. When BPSK is used for OFDM modulation, 2500 MHz is used as the USRP center frequency, and 0.5 MHz is used as the USRP bandwidth; AP1 can send data packets. The length is 100 bytes, the number of sent data packets is 100, the gain of Client2 is 0-38, the receiving gain of AP2 is 0, and the receiving gain of AP1 is 19. The support vector regression method based on wireless network communication for regional economic mid- and long-term predictions was completed well.

Cereal yield forecasting combining satellite drought-based indices, regional climate and weather data using machine learning approaches in Morocco

2021 ◽  
Author(s):  
El houssaine Bouras ◽  
Lionel Jarlan ◽  
Salah Er-Raki ◽  
Riad Balaghi ◽  
Abdelhakim Amazirh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Regional Climate ◽  
Model Development ◽  
Machine Learning Algorithms ◽  
Weather Data ◽  
Drought Indices ◽  
Support Vector ◽  
Learning Approaches ◽  
Climate Data ◽  
Yield Forecasting

&lt;p&gt;Cereals are the main crop in Morocco. Its production exhibits a high inter-annual due to uncertain rainfall and recurrent drought periods. Considering the importance of this resource to the country's economy, it is thus important for decision makers to have reliable forecasts of the annual cereal production in order to pre-empt importation needs. In this study, we assessed the joint use of satellite-based drought indices, weather (precipitation and temperature) and climate data (pseudo-oscillation indices including NAO and the leading modes of sea surface temperature -SST- in the mid-latitude and in the tropical area) to predict cereal yields at the level of the agricultural province using machine learning algorithms (Support Vector Machine -SVM-, Random forest -FR- and eXtreme Gradient Boost -XGBoost-) in addition to Multiple Linear Regression (MLR). Also, we evaluate the models for different lead times along the growing season from January (about 5 months before harvest) to March (2 months before harvest). The results show the combination of data from the different sources outperformed the use of a single dataset; the highest accuracy being obtained when the three data sources were all considered in the model development. In addition, the results show that the models can accurately predict yields in January (5 months before harvesting) with an R&amp;#178; = 0.90 and RMSE about 3.4 Qt.ha&lt;sup&gt;-1&lt;/sup&gt;. &amp;#160;When comparing the model&amp;#8217;s performance, XGBoost represents the best one for predicting yields. Also, considering specific models for each province separately improves the statistical metrics by approximately 10-50% depending on the province with regards to one global model applied to all the provinces. The results of this study pointed out that machine learning is a promising tool for cereal yield forecasting. Also, the proposed methodology can be extended to different crops and different regions for crop yield forecasting.&lt;/p&gt;

scholarly journals Alternatives to groundwater abstraction as a measure to stop land subsidence: a case study of Semarang, Indonesia

2019 ◽  
Vol 125 ◽  
pp. 01003 ◽  
Author(s):  
Wesley Beek ◽  
Bart Letitre ◽  
H. Hadiyanto ◽  
S. Sudarno
Keyword(s):  
Cost Benefit Analysis ◽  
Cost Benefit ◽  
Drinking Water Treatment ◽  
Decision Makers ◽  
Groundwater Abstraction ◽  
Short Term ◽  
Treatment Services

The Water as Leverage project aims to lay a blueprint for urban coastal areas around the world that are facing a variety of water-related issues. The blueprint is based upon three real case studies in Bangladesh, India and Indonesia. The case of Indonesia focuses on Semarang, a city that faces issues like flooding, increased water demand, and a lack of wastewater treatment. In this report I summarise the different techniques available to tackling these issues. Along with this I provide a cost-benefit analysis to support decision makers. For a short term it is recommended to produce industrial water from (polluted) surface water as a means to offer an alternative to groundwater abstraction. On a long term it is recommended to install additional wastewater and drinking water treatment services to facilitate better hygiene and a higher quality of life.

A cost-benefit analysis of peer coaching for overhead lift use in the long-term care sector in Canada

2015 ◽  
Vol 73 (5) ◽  
pp. 308-314 ◽  
Author(s):  
Emile Tompa ◽  
Roman Dolinschi ◽  
Hasanat Alamgir ◽  
Anna Sarnocinska-Hart ◽  
Jaime Guzman
Keyword(s):  
Peer Coaching ◽  
Long Term Care ◽  
Cost Benefit Analysis ◽  
Cost Benefit ◽  
Benefit Analysis ◽  
Term Care
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