Arthur E. Mynett

Integration of data mining techniques and heuristic knowledge in fuzzy logic modelling of eutrophication in Taihu Lake

Abstract A fuzzy logic (FL) model was developed in this study to predict algal biomass concentration in the eutrophic Taihu Lake, China. Common to any FL model, definition of membership functions and induction of inference rules remain difficult. They conventionally rely on the use of “heuristic knowledge”, which usually seems to be not enough to fulfil practical requirement or even unavailable. In this fuzzy model, a method combining data mining techniques with heuristic knowledge is developed. It used principal component analysis (PCA) to identify the major abiotic driving factors and to reduce dimensionality. Self-organising feature map (SOFM) technique and empirical knowledge were applied jointly to define the membership functions and to induce inference rules. As indicated by the results, the fuzzy model successfully demonstrated the potentials of exploring “embedded information” by combining data mining techniques with heuristic knowledge. The developed method had also been introduced to the European Commission project Harmful Algal Bloom Expert System (HABES) which involves 13 institutes and universities from 9 EU countries. The objectives of this paper are to illustrate how these techniques are integrated and how the developed fuzzy model is applied to predict the algal biomass concentration in the eutrophic lake.

Improving the operational forecasting system of the stratified flow in Osaka Bay using an ensemble Kalman filter–based steady state Kalman filter

Numerical models of a water system are always based on assumptions and simplifications that may result in errors in the models predictions. Such errors can be reduced through the use of data assimilation and thus can significantly improve the success rate of the predictions and operational forecasts. The ensemble Kalman filter (EnKF) is a generic data assimilation method which is suited for highly nonlinear models. However, for three-dimensional operational systems such as in the case of Osaka Bay, Japan, a full EnKF would be computationally too demanding. In the present paper, a steady state Kalman filter (SSKF) simplification based on the correlation scales derived from the EnKF is proposed. This EnKF-based SSKF (EnSSKF) as presented in this paper is applied in combination with the three-dimensional Delft3D-FLOW system, modeling the stratified circulation system of Osaka Bay in Japan. The aim of the application of the EnSSKF is to improve the daily operational forecasts of salinity and current profiles for engineering activities within the basin. Salinity and velocity components were assimilated on an hourly basis for the period 13–28 February 2002. The results of the filter performance and its forecasting ability are presented. The performance of the EnSSKF for improving the profiles of salinity and velocity components forecast during the first 24 h forecast is illustrated.

Revealing spatial pattern dynamics in aquatic ecosystem modelling with Multi-Agent Systems in Lake Veluwe

Aquatic ecosystems are among the most complex due to the highly nonlinearity, randomness, as well as interactive multi-processes in multi-scales. Besides, highly limited understanding and very limited measurement data make the modelling of such kind of systems a very challenging task, which needs to combine domain knowledge, available data and other models. The concept of Multi-Agent Systems (MAS) in modelling spatial population dynamics of aquatic plant growth is explored in this research due to the capability of MAS in utilizing various information and data, reflecting both the interactions among different entities and entitys own properties. Expert biological knowledge, GIS maps and environmental conditions are used as input information and data in the development of MAS model. Several different aspects are included: plant agents, environmental backgrounds, and animal influences. Environmental background factors include flow pattern, light visibility, water temperature, water depth and wind, which are all abiotic factors. Plant agents represent different plant species; the ones in this research are Potamogeton pectinatus (Pp) and Chara aspera (Cs), while animals here refer to water birds. Based on biological knowledge and data supplied for Lake Veluwe, the MAS modelling rules were developed. Through these MAS rules, agents are linked to environmental and biological processes. The resulting spatial pattern dynamics indicates that a multi-agent approach can exhibit complex behaviour even when the individual strategies of each agent are simple. The test case for Lake Veluwe showed quite good agreement compared to the GIS maps available and the biological knowledge presented. Multi-Agent Systems show a promising approach to modelling spatial population dynamics for aquatic plants.

Evaluation of Different Formulations to Optimally Locate Sensors in Sewer Systems

AbstractEfficient management of a sewer system includes the control of the conveyed wastewater quality to adequately operate treatment plants and protect the receiving water bodies. Moreover, these...

Simulating Gross Primary Production and Stand Hydrological Processes of páramo Grasslands in the Ecuadorian Andean Region Using the Biome-bgc Model

Abstract Many terrestrial biogeochemistry process models have been applied around the world at different scales and for a large range of ecosystems. Despite being essential ecosystems that sustain important ecological processes, only a few efforts have been made to estimate the gross primary production (GPP) and the hydrological budgets along an altitudinal gradient for grasslands in the Andean Region. One of the few previous studies in the region considered the heterogeneity of the main properties of the páramo vegetation and showed significant differences in plant functional types, site/soil parameters, and daily meteorology. This study extends the work previously mentioned by using the Biome-BGC model to simulate the GPP and the water fluxes in a representative area of the Ecuadorian Andean páramos. It focuses on three main growth forms of vegetation and is also extended to cells with similar properties. The responses of GPP and the water fluxes were dependent on environmental drivers, ecophysiology, and site-specific parameters. The results showed that the GPP estimations at lower elevations are more than twice the estimations at higher elevations, which might have a large implication during extrapolations at larger spatiotemporal scales. The assessment of the water fluxes in the páramo ecosystem was inaccurate, presumably due to the poor estimation of the soil processes, water storage, and evaporative processes. A further development in the soil and evaporative modeling process of Biome-BGC is needed in order to be fully applicable in the high-altitudinal páramo ecosystems. An accurate estimation of the temporal changes of carbon and water budgets can potentially assess the effect of the climate drivers in the biomass productivity of this terrestrial ecosystem.

Rule-based Ecological Model

Limited by understandings of ecosystem processes and availability of ecological monitoring data, most of the conventional ecological models are conceptually simplifi ed and formulated in partial difference equations (PDEs). They are based on physical processes and follow the conservation laws of mass, momentum and energy [1, 2]. These models have been playing important roles in the progress of ecological research and are still fundamental tools. However, it has been widely recognised that many mechanisms of ecosystem dynamics are still unclear owing to their high complexity and non-linearity [3–5]. In addition, the majority of the understandings are qualitative rather than quantitative that is diffi cult to formulate in PDEs [6]. It is therefore necessary to develop methods that combine semi-knowledge with semi-data. This chapter focuses on the rule-based technique, which derives the rules from the often limited data available from in-situ measurements while taking expert knowledge as reference. Rule-based approaches are not based on detailed descriptions of physical mechanisms, but are built on cause–effect relationships. The major methods include feature reasoning [7–9], case reasoning [10–13] and decision trees [14, 15]. However, the most promising way is to integrate physically based formulations with empirical rules [6, 16].

Using multidimensional views of photographs for flood modelling

Using physically based computational models coupled with remote sensing technologies, photogrammetry techniques, and GIS applications are important tools for flood hazard mapping and flood disaster prevention. Also, information processing of massive input data with refined accuracy allows us to develop and to improve urban-flood-modeling at a detailed level. The topographical information from digital surface model (DSM) or digital terrain model (DTM) is essential for flood managers who actually require this high accuracy and resolution of input data to set up their practical applications. Light detecting and ranging (LiDAR) techniques are mainly used, but these costly techniques can be appraised by equipments, maintenance, and operations which include aircraft. Recent advances in photogrammetry and computer vision technologies like structure form motion (SfM) technique are widely used and offer cost-effective approaches to reconstruct 3D-topographical information from simple 2D photos, so-called 3D reconstruction. In terms of input data for flood modeling, the SfM technique can be comparable to other acquisition-techniques. In this paper, there are one experimental and two case studies. Firstly, a result of the experiment showed a similarity between flood maps by applying the SfM process form the 3D-reconstruction and using benchmark information. These 3D-reconstruction processes started from 2D photos, which were taken from virtual scenes by using multidimensional-view approach. These photos can be used to generate 3D information which is later used to create the DSM from multidimensional fusion of views (MFV-DSM). Then, the DSM was used as input data to set up 2D flood modeling. Thereafter, when using the DSMs as topographical input data, comparison between a benchmark DSM and MFV-DSM shows similarity flood-map results in both flood depths and flood extends. Secondary, the two cases from real world scenes also showed possibilities of using the SfM technique as an alternative acquisition tool, providing 3D information. This information can be used as input data for setting up modeling and can possibly be comparable or even outcompete with other acquisition techniques, such as LiDAR. As a result, using the SfM technique can be extended to become promising methods in practicable applications for modeling real flood events in real world scenes.

Extracting inundation patterns from flood watermarks with remote sensing SfM technique to enhance urban flood simulation: The case of Ayutthaya, Thailand

Abstract Flood watermarks stipulate peak water depths from a flood event, indicating a magnitude of inundation that took place. Such information is invaluable for instantiation and validation of urban flood models. However, collecting and processing such data from land surveys can be costly and time-consuming. New remote sensing and data processing technologies offer improved opportunities to address these issues. The present paper deals with the new structure from motion (SfM) technology and its application in extracting flood watermarks. For this purpose, the first of its kind, side-view SfM surveys with two mobile units were utilised. Survey works were carried out in the vicinity of Ayutthaya heritage area (Thailand) and data obtained were used for setting up numerical models and simulations of the 2011 flood event. The work undertaken demonstrates the significant capability of SfM technology for extraction of flood watermarks. With such technology, it was possible to indicate facades, low-level structures, and susceptible openings, which in turn have improved schematizations of two-dimensional (2D) flood models. The resulting model simulations were found to be more accurate (i.e., more close to the measurements of flood watermarks) than those obtained from models with conventional top-view light detection and ranging (LiDAR) data.

A Pilot Study for an Enhanced Algal Spatial Pattern Prediction Using RS Images

Accurate and reliable flow forecasting form an important basis for efficient real-time river management, including flood control, flood warning and so on. In order to improve the accuracy of flow forecasting, gain matrix of Kalman filter was applied to real-time correction of hydraulic model for spatial distributing the system deviation (called “expected value of system noise” in Kalman filter). That means Kalman gain matrix is used to distribute model system deviation from measurement cross sections to the entire state of the river system. State functions of Kalman filter were set up based on discretization and linearization Saint-Venant equations by adopting four-point linear implicit form, and the spatial distribution system deviation method (SDM) was used for real-time correction. The calculation of flood forecasting for river section from Cuntan to Fengjie of Yangtze River verifies that SDM is useful in promoting the accuracy of real-time flood forecasting.