Assessment of predictive models for chlorophyll-a concentration of a tropical lake

<p>Abstract</p> <p>Background</p> <p>This study assesses four predictive ecological models; Fuzzy Logic (FL), Recurrent Artificial Neural Network (RANN), Hybrid Evolutionary Algorithm (HEA) and multiple linear regressions (MLR) to forecast chlorophyll- a concentration using limnological data from 2001 through 2004 of unstratified shallow, oligotrophic to mesotrophic tropical Putrajaya Lake (Malaysia). Performances of the models are assessed using Root Mean Square Error (RMSE), correlation coefficient (r), and Area under the Receiving Operating Characteristic (ROC) curve (AUC). Chlorophyll-a have been used to estimate algal biomass in aquatic ecosystem as it is common in most algae. Algal biomass indicates of the trophic status of a water body. Chlorophyll- a therefore, is an effective indicator for monitoring eutrophication which is a common problem of lakes and reservoirs all over the world. Assessments of these predictive models are necessary towards developing a reliable algorithm to estimate chlorophyll- a concentration for eutrophication management of tropical lakes.</p> <p>Results</p> <p>Same data set was used for models development and the data was divided into two sets; training and testing to avoid biasness in results. FL and RANN models were developed using parameters selected through sensitivity analysis. The selected variables were water temperature, pH, dissolved oxygen, ammonia nitrogen, nitrate nitrogen and Secchi depth. Dissolved oxygen, selected through stepwise procedure, was used to develop the MLR model. HEA model used parameters selected using genetic algorithm (GA). The selected parameters were pH, Secchi depth, dissolved oxygen and nitrate nitrogen. RMSE, r, and AUC values for MLR model were (4.60, 0.5, and 0.76), FL model were (4.49, 0.6, and 0.84), RANN model were (4.28, 0.7, and 0.79) and HEA model were (4.27, 0.7, and 0.82) respectively. Performance inconsistencies between four models in terms of performance criteria in this study resulted from the methodology used in measuring the performance. RMSE is based on the level of error of prediction whereas AUC is based on binary classification task.</p> <p>Conclusions</p> <p>Overall, HEA produced the best performance in terms of RMSE, r, and AUC values. This was followed by FL, RANN, and MLR.</p

From Synchrony to Harmony: Ideas on the Function of Neural Assemblies and on the Interpretation of Neural Synchrony

Point of departure are experimental data acquired by simultaneous recording of the activity of a number (2–16) of individual neurons during presentation of a sensory stimulus. The area under investigation is the auditory midbrain (Torus semicircularis) of the immobilized grassfrog (Rana temporaria L.). The sensory stimuli are both artificial (noise, tones and clicks) and natural sounds (vocalizations and environmental sounds). The goal of investigation is an insight into the neural representation of the sensory environment

From Neuron to Assembly: Neuronal Organization and Stimulus Representation

The study of information processing in the sensory nervous system may be viewed as an investigation of images. Let us consider, for instance, the auditory nervous system. Throughout the auditory system, starting at the hair cells in the cochlea and the auditory nerve fibres, through the various stages of the auditory processor, composed of the numerous individual neurons with their different patterns of interconnections, we have what might be called “the neural image of sound” in its different realizations. The external world is paralleled by an internal representation (e.g., Craik 1943, McCulloch 1965). The acoustic environment of an animal, consisting of patterns of air pressure variations at the external ears, is represented and transformed internally by a network of neurons which communicate by complex spatio-temporal patterns of action potentials, the all-or-none events generated by the individual neurons