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2018/09/28 14:00 Dr. Moslem Imani（Department of Geomatics, NCKU）
Seminar
Poster： ╱ Post date：2018-09-25NCU IHOS Seminar Announcement
Speaker：Dr. Moslem Imani
Place：S-325, Science Building 1
Abstract：
Title：Prediction of Sea Level Rise and Adaptation Strategies
Speaker：Dr. Moslem Imani
Department of Geomatics, NCKU
Time：9/28（Fri.）14：00
Place：S-325, Science Building 1
Abstract：
Climate change is one of the major challenges of our time and adds considerable stress to our societies and to the environment. Sea level change is an important consequence of climate change, which occur over a broad range of temporal and spatial scales. Sea level rise significantly affects socioeconomic activities through coastal erosion and inundation of low-lying areas. Rising sea levels also amplify the threat and magnitude of storm surges in coastal areas. The threats will continue to increase over time as sea levels rise and the magnitude of storms increase. To address these issues, the adaptation plan should be developed to prevent damage and losses to infrastructure, resources and homes in future. It is important to develop flexible adaptation plans, rather than relying on a single sea-level rise value or scenario. This is because there is a wide range of possible coastal futures with ongoing sea-level rise, particularly heading into next century. To properly design mitigation and adaptation strategies, an accurate estimate of sea level rise is then required. Water level forecasting is also important for the planning, maintenance and operations of water resources. Computer science and statistics have improved modeling approaches for discovering patterns in water resources time series data. Much effort has been devoted over the past several decades on the development and improvement of time series prediction models. Over the past several decades, considerable effort has been devoted to the development and improvement of time series prediction models using statistical approaches and computer science. Given the stochastic behavior of most natural systems, artificial intelligence techniques, such as artificial neural network, and machine learning techniques, have been developed to model sea level variations and other hydrological parameters. Furthermore, application of reliable sea level datasets such as satellite derived measurements enhance the accuracy and reliability of prediction models and consequently proper water management scenarios.
Last modification time：2018-09-25 PM 2:16
