<?xml version="1.0" encoding="UTF-8"?>
<feed xmlns="http://www.w3.org/2005/Atom" xmlns:dc="http://purl.org/dc/elements/1.1/">
  <title>DSpace Collection:</title>
  <link rel="alternate" href="http://hdl.handle.net/20.500.12323/5150" />
  <subtitle />
  <id>http://hdl.handle.net/20.500.12323/5150</id>
  <updated>2026-04-04T03:30:37Z</updated>
  <dc:date>2026-04-04T03:30:37Z</dc:date>
  <entry>
    <title>Long-Range Dependence and Sea Level Forecasting</title>
    <link rel="alternate" href="http://hdl.handle.net/20.500.12323/5152" />
    <author>
      <name>Ercan, Ali</name>
    </author>
    <author>
      <name>Kavvas, M. Levent</name>
    </author>
    <author>
      <name>Abbasov, Rovshan K.</name>
    </author>
    <id>http://hdl.handle.net/20.500.12323/5152</id>
    <updated>2021-10-11T10:43:14Z</updated>
    <published>2013-01-01T00:00:00Z</published>
    <summary type="text">Title: Long-Range Dependence and Sea Level Forecasting
Authors: Ercan, Ali; Kavvas, M. Levent; Abbasov, Rovshan K.
Abstract: ​This study shows that the Caspian Sea level time series possess long range dependence even after removing linear trends, based on analyses of the Hurst statistic, the sample autocorrelation functions, and the periodogram of the series. Forecasting performance of ARMA, ARIMA, ARFIMA and Trend Line-ARFIMA (TL-ARFIMA) combination models are investigated. The forecast confidence bands and the forecast updating methodology, provided for ARIMA models in the literature, are modified for the ARFIMA models. Sample autocorrelation functions are utilized to estimate the differencing lengths of the ARFIMA models. The confidence bands of the forecasts are estimated using the probability densities of the residuals without assuming a known distribution.&#xD;
&#xD;
There are no long-term sea level records for the region of Peninsular Malaysia and Malaysia’s Sabah-Sarawak northern region of Borneo Island. In such cases the Global Climate Model (GCM) projections for the 21st century can be downscaled to the Malaysia region by means of regression techniques</summary>
    <dc:date>2013-01-01T00:00:00Z</dc:date>
  </entry>
  <entry>
    <title>Community-Based Disaster Risk Management in Azerbaijan</title>
    <link rel="alternate" href="http://hdl.handle.net/20.500.12323/5151" />
    <author>
      <name>Abbasov, Rovshan</name>
    </author>
    <id>http://hdl.handle.net/20.500.12323/5151</id>
    <updated>2021-10-11T10:32:18Z</updated>
    <published>2018-01-01T00:00:00Z</published>
    <summary type="text">Title: Community-Based Disaster Risk Management in Azerbaijan
Authors: Abbasov, Rovshan
Abstract: Current natural and social conditions in Azerbaijan make children very vulnerable&#xD;
to natural hazards. The preparedness levels of schools and communities in rural&#xD;
areas are rather low, increasing the risk of natural disasters. Over the last 20 years,&#xD;
floods and earthquakes have caused considerable material loss in communities&#xD;
where children are not well protected. These losses are not only a manifestation of&#xD;
natural conditions, but also reveal the low preparedness levels of schools and&#xD;
communities. This book illustrates the main factors of vulnerability and gives a&#xD;
clear picture about the possible interventions to reduce disaster risks both in schools&#xD;
and communities. A new methodology for child-centered vulnerability assessments&#xD;
both in school and community levels has been developed. This methodology can be&#xD;
used to assess the level of vulnerability of schools and communities. A newly&#xD;
prepared training manual will help practitioners conduct trainings for government&#xD;
and community organizations. While the book is focused on a specific region, the&#xD;
suggested approach is generic and can be used elsewhere.</summary>
    <dc:date>2018-01-01T00:00:00Z</dc:date>
  </entry>
</feed>

