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Disaster Advances

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Disaster Advances

Coral Reef in Center of Coral Biodiversity (Coral Triangle): The Pulau Lirang, Southwest Moluccas (MBD)

Oktiyas Muzaky Luthfi*, Muhammad Arif Asadi and Teguh Agustiadi

Southwest Moluccas was given three important ecosystems in a tropical area, they were mangrove, seagrass and coral reef. Through Serambi Tanah Air expedition we succeed collecting and mapping those resources. Type of coral reef in the Pulau Lirang was fringing reef that cycled the island. Six stations were decided to obtain coral reef data. We used point intercept transect 2 x 50 m in each station in varied depth between 3-7 m.

The result of the study showed that the average of life coral cover in the Pulau Lirang was 33.75% with the highest coverage in station 6 as 54%. The highest coral dead cover was found in station 3 and it was 40.5% in coverage. Competition among sessile organism, strong current and sedimentation was suggested as a threat in this island.

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Seismic response of buildings with Soil structure interaction – A new design methodology

Palaniappan Meyyappan

Structures are usually designed on the assumption that the structures are linearly elastic and they remain linearly elastic when subjected to dynamic excitation. In this paper, a new design methodology is presented by incorporating NEHRP provisions into IS 1893-2002 codal provisons for evaluating the seismic response of buildings by considering soil structure interaction (SSI) effects. A sample problem of two storey building frame is taken to study the SSI effects of small and large strain levels of soils for different zones and also for different types of soils. It is observed that by considering SSI effects, seismic responses of buildings are increased up to 50 - 60% in time period and reduction in base shear around 10%.

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Resilience of Soil and Structure of Lahor Dam by means of Seismic Vulnerability Index Data

Sunaryo*, Adi Susilo and Arief Andy Soebroto

Research entitled resilience of soil and structure of Lahor Dam by means of seismic vulnerability index data has been carried out to obtain a knowledge of the resilience of the Lahor dam caused by the earthquake. The acquisition of microseismic data that will be used to calculate the value of the seismic vulnerability index is carried out at the location points distributed in the body of the dam and the exhaust side at a number of 35 measuring points in the form of a grid with a distance between grid points around 100m. The acquired data is then processed by the HVSR method to obtain amplification factors (A0), dominant frequency (f0), dominant period (T0) and seismic vulnerability index (Kg).

Based on the processing and interpretation results, the values A0 1.13 - 6.57 are located at the points MZL31 and MZL29 with an average value of A0 is 3.22. For f0, values of 0.63Hz - 19.05Hz are obtained at points MZL05 and MZL28 with an average value of 4.36Hz. For T0, values of 0.05s - 1.58s are achieved at points MZL05 and MZL28 with an average value of 0.59s, whereas for Kg obtained, values of 0.27 - 17.31 are located in MZL31 and MZL09 with an average value of 5.68. This data shows the weak resilience of soil and structure of the Lahor dam is in the zone of the body on the exhaust side which is characterized by a high Kg value with lithology of alluvial sediment and landfill.

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Development of Rainfall-Runoff Model using FFBPNN and LRNN for Silchar City- A Case Study

Annayat Wajahat*, Sundar Briti Sil and Gupta Ajay

For highly nonlinear and complex problems like rainfall run-off models, artificial neural networks (ANNS) are highly developed empirical models available and are increasingly common in the analysis of hydrology and water resource problems. The aim of this study was to develop rainfall run-off models for Barak basin. In this study, feed-forward back propagation neural network (FFBPNN) and layer recurrent neural network (LRNN) are used to predict runoff during monsoon period. The performance of both the models (FFBPNN, LRNN) for run-off over 33 years of data (1979-2011) has been evaluated for two watersheds namely Katakhal and Sonai of Barak basin.

Two neural networks with the evaluation criteria mean square error (MSE), root mean square error (RMSE) and coefficient of regression (R2) FFBPNN perform best with architecture 3-9-1 following Logsig function. This study confirms that FFBPNN and LRNN are useful techniques for predicting run-off in both of the watersheds of Barak Basin.

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