Anita Talib

Phytoplankton community dynamics of two adjacent Dutch lakes in response to seasons and eutrophication control unravelled by non-supervised artificial neural networks

Abstract Long-term time-series of the eutrophic Dutch lakes Veluwemeer and Wolderwijd were subject to ordination and clustering by means of non-supervised artificial neural networks (ANN). A combination of bottom-up and top-down eutrophication control measures has been implemented in both lakes since 1979. Dividing time-series data from 1976 to 1993 into three distinctive management periods has facilitated a comparative analysis of the two lakes regarding both the seasonal and long-term dynamics in response to eutrophication control. Results of the study have demonstrated that non-supervised ANN are an alternative technique: (1) to elucidate causal relationships of complex ecological processes, and (2) to reveal long-term behaviours of ecosystems in response to different management approaches. It has been shown that external nutrient control combined with food web manipulation have turned both lakes from nitrogen to phosphorus limitation, and from blue-green algae to diatom and green algae dominance.

Rule-based agents for forecasting algal population dynamics in freshwater lakes discovered by hybrid evolutionary algorithms

Abstract In the context of this study two concepts were applied for the development of rule-based agents of algal populations: (1) rule discovery by means of a hybrid evolutionary algorithms (HEA) and rigorous k-fold cross-validation, and (2) rule generalisation by means of merged time-series data of lakes belonging to the same lake category. The rule-based agents developed during this study proved to be both explanatory and predictive. It has been demonstrated that the interpretation of the rules can be brought into the context of empirical and causal knowledge on chlorophyll-a dynamics as well as population dynamics of Microcystis and Oscillatoria under specific water quality conditions. The k-fold cross-validation of the agents based on measured data of each year of similar lakes revealed good forecasting accuracy resulting in r2 values ranging between 0.39 and 0.63.

Forecasting and explanation of algal dynamics in two shallow lakes by recurrent artificial neural network and hybrid evolutionary algorithm

Long-term time-series of the two eutrophic Dutch lakes Veluwemeer and Wolderwijd were subject to predictive modelling by recurrent supervised ANN (RANN) and hybrid evolutionary algorithms (HEA). A combination of bottom-up and top-down eutrophication control measures has been implemented in both lakes since 1979. Dividing the time-series data into training and validation datasets based on three distinctive management periods has facilitated a comparative analysis of the two lakes regarding the long-term dynamics in response to eutrophication control. Results of the study have demonstrated that RANN and HEA can be applied for (1) 5-day-ahead prediction of Oscillatoria spp. and (2) 5-day-ahead prediction of Scenedesmus spp. Firstly RANN achieved reasonably accurate results for 5-day-ahead forecasting of abundances of blue-green algae Oscillatoria and green algae Scenedesmus in both lakes. Secondly HEA achieved similar good forecasting results and also provided model representations for both algae species in the form of rule sets. The limitation with single lake models is that the rule sets discovered are lake specific. Merging of the two lake datasets using merged lake models for both training and testing have produced simpler and generic rule sets that explain the dynamics of Oscillatoria and Scenedesmus for both lakes. The results from this study have shown that both external nutrient control combined with food web manipulation have turned the lakes from hypereutrophic conditions and from Oscillatoria to Scenedesmus dominance. These complex dynamics that are associated with the shift from periods of turbid with Oscillatoria dominance to clear-water conditions with increased Scenedesmus in both lakes are predictable and can be explained by the key-driving variables in the generic rule sets discovered.

Prediction of Chemical Oxygen Demand In Dondang River Using Artificial Neural Network

The increase of human population every year results in increasing of water usage and demand. This phenomenon affects the overall water quality and change the flow of river ecosystems. Various residential and commercial building were built along Dondang river basin in Pulau Pinang. Irresponsible residents and factory manager treat the river as sites for illegal garbage dumping and waste disposal. As an effort to monitor river water quality in Malaysia, Alam Sekitar Malaysia (ASMA) collaborated with Department of Environment (DOE) Malaysia to measure water quality parameters every month. The data used for Dondang River was dated from January 1998 until December 2007 at three stations at different sites. Different variables in this model include Dissolve Oxygen (DO), Biological Oxygen Demand (BOD), Suspended Solid (SS), pH, ammonia (NH 3 ), temperature, nitrate (NO 3 ), total solid (TS) and phosphate (PO4).were used as inputs while the prediction is made for Chemical Oxygen Demand (COD). The number of hidden layer, processing elements, the value of learning rule and transfer were adjusted to achieve the minimum error and highest prediction accuracy. The prediction of COD were divided into training, cross validation and testing data. The best possible model estimation was choose from the model with higher generalization, less minimum square error and high r squared value. The model estimation then tested with Mean Sensitivity test to check the validity of the model and to determine the factors that affect the value of COD the most.. Sensitivity analysis showed that BOD is the most important variable that determine COD, followed by phosphate concentration, DO, susupended solids and temperature. The results are consistent with the effects of land use on the river water quality as Dondang River flows through mainly housing area and factories or industrial area.

Co-breeding Association of Aedes albopictus (Skuse) and Aedes aegypti (Linnaeus) (Diptera: Culicidae) in Relation to Location and Container Size

The occurrence of major outbreaks of dengue, and other vector borne diseases such as chikungunya and zika in tropical and subtropical regions has rendered control of the diseases a top-priority for many affected countries including Malaysia. Control of the mosquito vectors Aedes aegypti and Aedes albopictus through the reduction of breeding sites and the application of insecticides to kill immature forms and adults are the main control efforts to combat these diseases. The present study describes the association between Ae. albopictus and Ae. aegypti in shared breeding sites. This study is important given that any measure taken against one species may affect the other. A yearlong larval survey was conducted in four dengue endemic areas of Penang Island. Sorenson’s coefficient index indicated that no association between number of the immatures of the two species regardless of container size and study location. Therefore, the mean number Ae. albopictus immature was not decreased in the presence of Ae. aegypti in shared breeding container. However Ae. aegypti appeared to prefer breeding in habitats not occupied by Ae. albopictus, the two species sharing breeding sites only where available containers were limited. In control efforts, eliminating the preferred breeding containers for one species might not affect or reduce the population of the other species.

Pattern recognition of grazing dynamics in response to fish removal (Lake Wolderwijd, Netherlands) using non-supervised artificial neural networks

Limnological time-series data sets of the eutrophic Dutch lake Wolderwijd were modelled by means of non-supervised artificial neural networks (NSANN) for pattern recognition. This lake has been subjected to various eutrophication control measures for the past 3 decades, including the top-down approach of planktivorous fish removal or biomanipulation. NSANN was applied for patternising the effects of preand post-fish removal on the phytoand zooplankton dynamics. Results of the study have demonstrated that NSANN can: (1) elucidate short-term shifts and long-term causal relationships of complex ecological dynamics, associated with grazing, taking into account the ongoing impacts of phosphorus removal in the lake, (2) illustrate the potential impact of zooplankton grazing during the clear-water phase in spring and (3) the complex dynamics involving water quality, phytoand zooplankton changes that take place when Daphnia peaks