Neural network and genetic programming for modelling coastal algal blooms

2006-2007 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Rainfall-runoff modelling using genetic programming

Conference Theme: Advances and Applications for Management and Decision MakingThe problem of accurately determining river flows from rainfall, evaporation and other factors, occupies an important place in hydrology. The rainfall-runoff process is believed to be highly non-linear, time varying, spatially distributed and not easily described by simple models. Practitioners in water resources have embraced data-driven modelling approaches enthusiastically, as they are perceived to overcome some of the difficulties associated with physics-based approaches. Such approaches have proved to be an effective and efficient way to model the rainfall-runoff process in situations where enough data on physical characteristics of catchment is not available or when it is essential to predict the flow in the shortest possible time to enable sufficient time for notification and evacuation procedures. In the recent past, an evolutionary based data-driven modelling approach, genetic programming (GP) has been used for rainfall-runoff modelling. In this study, GP has been applied for predicting the runoff from three catchments - a small steep-sloped catchment in Hong Kong (Hok Tau catchment) and two relatively bigger catchments located in the southern part of China (Shanqiao and Shuntian catchments). For the runoff predictions in Hok Tau catchment, the performance of the data-driven technique was not very satisfactory. This catchment, being a very steep-sloped catchment, has high peak discharge magnitudes with steep rising and recession limbs, which the GP models are unable to capture. This catchment being a small one with an area of about 5 km2 has a time of concentration of about 30-45 minutes, but the time interval of the available data is one day, which seems to be another reason for GP's inability to capture the complex rainfall to runoff transformation on this catchment. Using a dataset of smaller time interval, the data-driven model should perform better. A key advantage of GP as compared to traditional modelling approaches is that it does not assume any a priori functional form of the solution. For instance, in a typical regression method, the model structure is specified in advance (which is in general difficult to do) and the model coefficients are determined. For neural networks, the time consuming task of initially defining the network structure has to be undertaken and then the coefficients (weights) are found by the learning algorithm. On the other hand, in GP, the building blocks (the input and target variables and the function set) are defined initially, and the learning method subsequently finds both the optimal structure of the model and its coefficients. Moreover, since GP evolves an equation or formula relating the input and output variables, a major advantage of the GP approach is its automatic ability to select input variables that contribute beneficially to the model and disregard those that do not. GP can thus reduce substantially the dimensionality of the input variables. In GP, as in any data-driven prediction model, the selection of appropriate model inputs is extremely important. This is especially so when lagged input variables are also used. Inclusion of irrelevant inputs leads to poor model accuracy and creation of complex models, which are more difficult to interpret as compared to simpler ones. Thus, for the remaining two catchments, an attempt is made to use the evolutionary search capabilities of GP for selecting the significant input variables. These variables, indicated as significant by GP are then used as inputs for the actual predictions. In contrast to the not so satisfactory performance by the GP models for predicting the runoff from Hok Tau catchment, their performance for the other two catchments is quite satisfactory, as the GP models are able to capture the peaks quite well and the goodness-of-fit measures are also acceptable. These results indicate that GP can be used as a viable alternative for rainfall-runoff modelling, and the analytical form of the evolved equations facilitate easy interpretation. In this study, the GP evolved models are used for selection of significant variables influencing the rainfall to runoff transformation.postprin

Assessment framework for the impacts of climate change and urbanization on urban drainage systems

It has been widely recognized that global climate change will have negative impacts not only on the natural environment but also on the human-built environment. This paper describes the framework developed to assess the potential impacts of climate change and urbanization on drainage systems of Australian urban cities. One of real concerns is how the flooding risk will change over the next 5-25 years under such possible impacts. In this study, the assessment method is explored with regards to two major effects of climate change (i.e. changed pattern of storm event and rising sea level), two effects of urbanization (i.e. increasing impervious area and storm water harvesting) and two effects of hydraulic deterioration (i.e. reduced cross-sectional area and increased internal surface roughness of conduits). The framework is demonstrated on a simulation study at street. The outcomes of this study will provide preliminary understanding on how drainage systems respond to changing climate inputs and also guided steps to implement the framework on real-world problems

Pulmonary Immunization Using Antigen 85-B Polymeric Microparticles to Boost Tuberculosis Immunity

This study aims to evaluate immunization with polymeric microparticles containing recombinant antigen 85B (rAg85B) delivered directly to the lungs to protect against tuberculosis. rAg85B was expressed in Escherichia coli and encapsulated in poly(lactic-co-glycolic acid) microparticles (P-rAg85B). These were delivered as dry powders to the lungs of guinea pigs in single or multiple doses of homologous and heterologous antigens. Bacille Calmette-Guérin (BCG) delivered subcutaneously was employed as the positive control and as part of immunization strategies. Immunized animals were challenged with a low-dose aerosol of Mycobacterium tuberculosis (MTB) H37Rv to assess the extent of protection measured as reduction in bacterial burden (CFU) in the lungs and spleens of guinea pigs. Histopathological examination and morphometric analysis of these tissues were also performed. The heterologous strategy of BCG prime-P-rAg85B aerosol boosts appeared to enhance protection from bacterial infection, as indicated by a reduction in CFU in both the lungs and spleens compared with untreated controls. Although the CFU data were not statistically different from the BCG and BCG-BCG groups, the histopathological and morphometric analyses indicated the positive effect of BCG-P-rAg85B in terms of differences in area of tissue affected and number and size of granulomas observed in tissues. P-rAg85B microparticles appeared to be effective in boosting a primary BCG immunization against MTB infection, as indicated by histopathology and morphometric analysis. These encouraging observations are relevant to boosting adults previously immunized with BCG or exposed to MTB, commonly the case in the developing world, and should be followed by further assessment of an appropriate immunization protocol for maximum protection

Superior exploration-exploitation balance in shuffled complex evolution

10.1061/(ASCE)0733-9429(2004)130:12(1202)Journal of Hydraulic Engineering130121202-120

Genetic programming for analysis and real-time prediction of coastal algal blooms

Harmful algal blooms (HAB) have been widely reported and have become a serious environmental problem world wide due to its negative impacts to aquatic ecosystems, fisheries, and human health. A capability to predict the occurrence of algal blooms with an acceptable accuracy and lead-time would clearly be very beneficial to fisheries and environmental management. In this study, we present the first real-time modelling and prediction of algal blooms using a data driven evolutionary algorithm, Genetic Programming (GP). The daily prediction of the algal blooms is carried out at Kat O station in Hong Kong using 3 years of high frequency (two-hourly) chlorophyll fluorescence and related hydro-meteorological and water quality data. The results for the prediction of chlorophyll fluorescence, a measure of algal biomass, are within reasonable accuracy for a lead-time of up to 1 day. The results generally concur with those obtained with artificial neural network. As compared to traditional data-driven models, GP has the advantage of evolving an equation relating input and output variables. A detailed analysis of the results of the GP models shows that GP not only correctly identifies the key input variables in accordance with ecological reasoning, but also demonstrates the relationship between the auto-regressive nature of bloom dynamics and flushing time. This study shows GP to be a viable alternative for algal bloom modelling and prediction; the interpretation of the results is greatly facilitated by the analytical form of the evolved equations. © 2005 Elsevier B.V. All rights reserved.link_to_subscribed_fulltex

Improving runoff forecasting by input variable selection in Genetic Programming

10.1061/40569(2001)76Bridging the Gap: Meeting the World's Water and Environmental Resources Challenges - Proceedings of the World Water and Environmental Resources Congress 2001111