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

Widespread exploitation of the honeybee by early Neolithic farmers.

This is the author's version of an article subsequently published in Nature. The definitive version is available from the publisher via: doi: 10.1038/nature15757.Copyright © 2015, Rights Managed by Nature Publishing GroupThe pressures on honeybee (Apis mellifera) populations, resulting from threats by modern pesticides, parasites, predators and diseases, have raised awareness of the economic importance and critical role this insect plays in agricultural societies across the globe. However, the association of humans with A. mellifera predates post-industrial-revolution agriculture, as evidenced by the widespread presence of ancient Egyptian bee iconography dating to the Old Kingdom (approximately 2400 BC). There are also indications of Stone Age people harvesting bee products; for example, honey hunting is interpreted from rock art in a prehistoric Holocene context and a beeswax find in a pre-agriculturalist site. However, when and where the regular association of A. mellifera with agriculturalists emerged is unknown. One of the major products of A. mellifera is beeswax, which is composed of a complex suite of lipids including n-alkanes, n-alkanoic acids and fatty acyl wax esters. The composition is highly constant as it is determined genetically through the insect's biochemistry. Thus, the chemical 'fingerprint' of beeswax provides a reliable basis for detecting this commodity in organic residues preserved at archaeological sites, which we now use to trace the exploitation by humans of A. mellifera temporally and spatially. Here we present secure identifications of beeswax in lipid residues preserved in pottery vessels of Neolithic Old World farmers. The geographical range of bee product exploitation is traced in Neolithic Europe, the Near East and North Africa, providing the palaeoecological range of honeybees during prehistory. Temporally, we demonstrate that bee products were exploited continuously, and probably extensively in some regions, at least from the seventh millennium cal BC, likely fulfilling a variety of technological and cultural functions. The close association of A. mellifera with Neolithic farming communities dates to the early onset of agriculture and may provide evidence for the beginnings of a domestication process.Natural Environment Research Council (NERC)English HeritageEuropean Research Council (ERC)Leverhulme TrustMinistère de la Culture et de la CommunicationMinistère de l’Enseignement Supérieur et de la RechercheRoyal SocietyWellcome Trus

Data sharing reveals complexity in the westward spread of domestic animals across Neolithic Turkey

This study presents the results of a major data integration project bringing together primary archaeozoological data for over 200,000 faunal specimens excavated from seventeen sites in Turkey spanning the Epipaleolithic through Chalcolithic periods, c. 18,000-4,000 cal BC, in order to document the initial westward spread of domestic livestock across Neolithic central and western Turkey. From these shared datasets we demonstrate that the westward expansion of Neolithic subsistence technologies combined multiple routes and pulses but did not involve a set 'package' comprising all four livestock species including sheep, goat, cattle and pig. Instead, Neolithic animal economies in the study regions are shown to be more diverse than deduced previously using quantitatively more limited datasets. Moreover, during the transition to agro-pastoral economies interactions between domestic stock and local wild fauna continued. Through publication of datasets with Open Context (opencontext.org), this project emphasizes the benefits of data sharing and web-based dissemination of large primary data sets for exploring major questions in archaeology (Alternative Language Abstract S1)

Neural network models as a management tool in lakes

A research was made on the potential use of neural network based models in eutrophication modelling. As a result, an algorithm was developed to handle the practical aspects of designing, implementing and assessing the results of a neural network based model as a lake management tool. To illustrate the advantages and limitations of the neural network model, a case study was carried out to estimate the chlorophyll-a concentration in Keban Dam Reservoir as a function of sampled water quality parameters (PO4 phosphorus, NO3 nitrogen, alkalinity, suspended solids concentration, pH, water temperature, electrical conductivity, dissolved oxygen concentration and Secchi depth) by a neural network based model. Alternatively, the same system was solved with a linear multiple regression model in order to compare the performances of the proposed neural network based model and the traditional linear multiple regression model. For both of the models, the linear correlation coefficients between the logarithms of observed and calculated chlorophyll-a concentrations were calculated. The correlation coefficient R, the best linear fit between the observed and calculated values, was evaluated to assess the performances of the two models. R values of 0.74 and 0.71 were obtained for the neural network based model and the linear multiple regression model, respectively. The study showed that the neural network based model can be used to estimate chlorophyll-a with a performance similar to that of the traditional linear multiple regression method. However, for cases where the input and the output variables are not linearly correlated, neural network based models are expected to show a better performance