Aw Jayawardena

Neural network modelling of coastal algal blooms

An artificial neural network (ANN), a data driven modelling approach, is proposed to predict the algal bloom dynamics of the coastal waters of Hong Kong. The commonly used back-propagation learning algorithm is employed for training the ANN. The modeling is based on (a) comprehensive biweekly water quality data at Tolo Harbour (1982 � /2000); and (b) 4-year set of weekly phytoplankton abundance data at Lamma Island (1996 � /2000). Algal biomass is represented as chlorophyll-a and cell concentration of Skeletonema at the two locations, respectively. Analysis of a large number of scenarios shows that the best agreement with observations is obtained by using merely the time-lagged algal dynamics as the network input. In contrast to previous findings with more complicated neural networks of algal blooms in freshwater systems, the present work suggests the algal concentration in the eutrophic sub-tropical coastal water is mainly dependent on the antecedent algal concentrations in the previous 1 � /2 weeks. This finding is also supported by an interpretation of the neural networks’ weights. Through a systematic analysis of network performance, it is shown that previous reports of predictability of algal dynamics by ANN are erroneous in that ‘future data’ have been used to drive the network prediction. In addition, a novel real time forecast of coastal algal blooms based on weekly data at Lamma is presented. Our study shows that an ANN model with a small number of input variables is able to capture trends of algal dynamics, but data with a minimum sampling interval of 1 week is necessary. However, the sufficiency of the weekly sampling for real time predictions using ANN models needs to be further evaluated against longer weekly data sets as they become available. # 2002 Elsevier Science B.V. All rights reserved.

Geo-Risk Management Practices in the Asia Pacific Region

Over the years, the Asia Pacific Region has suffered more than its fair share of natural disasters. These include floods, typhoons, land slides, storm surges, tsunamis, earthquakes and volcanic eruptions which may be considered as geo-hazards, and others such as wild fires which could be attributed to human action. Historical evidence shows that such disasters have caused loss of lives, property and other economic damages and untold miseries to many millions of people in the region. The mechanisms which trigger these disasters are beyond the control of mankind, at least at the present time, and the only option open to the vulnerable communities is to take pro-active measures to mitigate the potential damages. Before mitigating action can be taken, it is important to understand the risks associated with different types of hazards and to make an assessment which is followed by appropriate risk management practices. Scientific tools are used in the assessment process while a combination of structural and non-structural measures are used in the management stage. This paper attempts to highlight the geohazards that the region has to live with and the measures that have been adopted to minimise potential damages.