On behalf of Mr. Koichiro Matsuura, Director-General of UNESCO, it is my great pleasure to welcome you all to this international scientific conference. Drylands are often considered fragile ecosystems, yet they have a remarkable resilience to stress. They are home to unique and well-adapted plant and animal species that we need to conserve. Some of the world’s greatest cultures and belief systems have originated in drylands. On the other hand, desertification and land degradation in drylands often result in poverty and cause environmental refugees to abandon their homes. These problems can only be addressed in a holistic manner, based on sound scientific research and findings. Solutions to the problems of dryland degradation need to be communicated as widely as possible through education at all levels. These are many reasons why UNESCO – within its mandate of science, education, culture and communication – took the intiative to organize this conference. And we are glad that so many partners have responded to our call. UNESCO considers this conference as its main contribution to the observance of the International Year of Deserts and Desertification in 2006. We have deliberately chosen the title ‘The Future of Drylands’ as we feel it is time to redefine our priorities for science, education and governance in the drylands based on 50 years of scientific research in arid and semi-arid zones. In fact UNESCO has one of the longest traditions, within the UN system, of addressing dryland problems from an interdisciplinary, scientific point of view. In 1955, the ‘International Arid Land Meetings’ were held in Socorro, New Mexico (USA). They were organized by the American Association for the Advancement of Science (AAAS), sponsored by UNESCO and supported by the Rockefeller Foundation. One important output of the International Arid Land Meetings was a book entitled The Future of Drylands, edited by Gilbert F. White and published in

United Nations Educational, Scientific and Cultural Organization (UNESCO)

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Engaged Scholarship: A Guide for Organizational and Social Research

Despite hospitality and tourism researchers’ recent attempts on examining different aspects of online word-of-mouth [WOM], its impact on hotel sales remains largely unknown in the existing literature. To fill this void, we conduct a study to empirically investigate the impact of online consumer-generated reviews on hotel room sales. Utilizing data collected from the largest travel website in China, we develop a fixed effect log-linear regression model to assess the influence of online reviews on the number of hotel room bookings. Our results indicate a significant relationship between online consumer reviews and business performance of hotels.

The impact of online user reviews on hotel room sales [Summary]

https://link.springer.com/content/pdf/10.1057/jors.1979.126.pdf

Forecasting Economic Time Series

Information and Communication Technologies in Tourism

In the hospitality industry, demand forecast accuracy is highly impacted by booking cancellations, which makes demand-management decisions difficult and risky. In attempting to minimize losses, hot...

/pdf/big-data-in-hotel-revenue-management-exploring-cancellation-555tshullp.pdf

Big Data in Hotel Revenue Management: Exploring Cancellation Drivers to Gain Insights Into Booking Cancellation Behavior:

Booking cancellations negatively contribute to the production of accurate forecasts, which comprise a critical tool in the hospitality industry. Research has shown that with today’s computational power and advanced machine learning algorithms it is possible to build models to predict bookings cancellation likelihood. However, the effectiveness of these models has never been evaluated in a real environment. To fill this gap and investigate how these models can be implemented in a decision support system and its impact on demand-management decisions, a prototype was built and deployed in two hotels. The prototype, based on an automated machine learning system designed to learn continuously, lead to two important research contributions. First, the development of a training method and weighting mechanism designed to capture changes in cancellations patterns over time and learn from previous days’ predictions hits and errors. Second, the creation of a new measure – Minimum Frequency – to measure the precision of predictions over time. From a business standpoint, the prototype demonstrated its effectiveness, with results exceeding 84% in accuracy, 82% in precision, and 88% in Area Under the Curve (AUC). The system allowed hotels to predict their net demand and thus making better decisions about which bookings to accept and reject, what prices to make, and how many rooms to oversell. The systematic prediction of bookings with high probability of being canceled allowed hotels to reduce cancellations by 37 percentage points by acting to avoid their cancellation.

/pdf/an-automated-machine-learning-based-decision-support-system-1cypd2fwix.pdf

An automated machine learning based decision support system to predict hotel booking cancellations

Booking cancellations have a substantial impact in demand-management decisions in the hospitality industry. Cancellations limit the production of accurate forecasts, a critical tool in terms of revenue management performance. To circumvent the problems caused by booking cancellations, hotels implement rigid cancellation policies and overbooking strategies, which can also have a negative influence on revenue and reputation. Using data sets from four resort hotels and addressing booking cancellation prediction as a classification problem in the scope of data science, authors demonstrate that it is possible to build models for predicting booking cancellations with accuracy results in excess of 90%.  This demonstrates that despite what was assumed by Morales and Wang (2010) it is possible to predict with high accuracy whether a booking will be canceled. Results allow hotel managers to accurately predict net demand and build better forecasts, improve cancellation policies, define better overbooking tactics and thus use more assertive pricing and inventory allocation strategies.

http://tmstudies.net/index.php/ectms/article/download/1000/pdf_51

Predicting hotel booking cancellations to decrease uncertainty and increase revenue

Hotel booking demand datasets

An accurate short-term load forecasting (STLF) is one of the most critical inputs for power plant units’ planning commitment. STLF reduces the overall planning uncertainty added by the intermittent production of renewable sources; thus, it helps to minimize the hydrothermal electricity production costs in a power grid. Although there is some research in the field and even several research applications, there is a continual need to improve forecasts. This research proposes a set of machine learning (ML) models to improve the accuracy of 168 h forecasts. The developed models employ features from multiple sources, such as historical load, weather, and holidays. Of the five ML models developed and tested in various load profile contexts, the Extreme Gradient Boosting Regressor (XGBoost) algorithm showed the best results, surpassing previous historical weekly predictions based on neural networks. Additionally, because XGBoost models are based on an ensemble of decision trees, it facilitated the model’s interpretation, which provided a relevant additional result, the features’ importance in the forecasting.

/pdf/short-term-electricity-load-forecasting-with-machine-3n4vrd80nk.pdf

Nuno Antonio

Papers

Big Data in Hotel Revenue Management: Exploring Cancellation Drivers to Gain Insights Into Booking Cancellation Behavior:

An automated machine learning based decision support system to predict hotel booking cancellations

Predicting hotel booking cancellations to decrease uncertainty and increase revenue

Hotel booking demand datasets

Short-Term Electricity Load Forecasting with Machine Learning