Comparison between response surface models and artificial neural networks in hydrologic forecasting

Developing an efficient and accurate hydrologic forecasting model is crucial to managing water resources and flooding issues. In this study, response surface (RS) models including multiple linear regression (MLR), quadratic response surface (QRS), and nonlinear response surface (NRS) were applied to daily runoff (e.g., discharge and water level) prediction. Two catchments, one in southeast China and the other in western Canada, were used to demonstrate the applicability of the proposed models. Their performances were compared with artificial neural network (ANN) models, trained with the learning algorithms of the gradient descent with adaptive learning rate (ANN-GDA) and Levenberg-Marquardt (ANN-LM). The performances of both RS and ANN in relation to the lags used in the input data, the length of the training samples, long-term (monthly and yearly) predictions, and peak value predictions were also analyzed. The results indicate that the QRS and NRS were able to obtain equally good performance in runoff prediction, as compared with ANN-GDA and ANN-LM, but require lower computational efforts. The RS models bring practical benefits in their application to hydrologic forecasting, particularly in the cases of short-term flood forecasting (e.g., hourly) due to fast training capability, and could be considered as an alternative to ANN

DOC and UVA attenuation with soil aquifer treatment in the saturated zone of an artificial coastal sandfill

Results from a direct recharge experiment conducted in the field to investigate DOC and UVA(254) attenuation rates during the direct injection of UF treated wastewater into a artificial coastal sandfill are presented in this paper. Approximately 500 m(3) of ultra-filtered wastewater was injected into the saturated zone, over a period of 9 days. The movement of the plume was tracked over 80 days, during which time samples were obtained from multilevel samplers installed in transects across the drift axis of the plume. An analysis of fluorescein in the samples obtained during the drift of the UF plume showed that DOC and UVA were attenuated beyond rates predicted by conservative mixing, by up to a maximum of 45%. A degradation coefficient of 0.0175 day(-1) was found to be applicable for DOC degradation. After a drift period of 80 days, DOC and UVA reduced to approximately 4.5 mg/l and 0.100 cm(-1), respectively, from initial values of 8.06 mg/l and 0.199 cm(-1).

Artificial neural network modeling of flow stress in hot rolling

In this study, an artificial neural network model is proposed to predict the flow stress variations during the hot rolling process. Optimization of the proposed neural network with respect to number of neurons within the hidden layer, different training methods and transfer functions of the neural network is performed. The results of the optimal network were compared with those of the conventional analytic method and it is shown that using an optimal neural network the mean calculated error is drastically reduced.

Development of a dynamic surface roughness monitoring system based on artificial neural networks (ANN) in milling operation

Dynamic surface roughness prediction during metal cutting operations plays an important role to enhance the productivity in manufacturing industries. Various machining parameters such as unwanted noises affect the surface roughness, whatever their effects have not been adequately quantified. In this study, a general dynamic surface roughness monitoring system in milling operations was developed. Based on the experimentally acquired data, the milling process of Al 7075 and St 52 parts was simulated. Cutting parameters (i.e., cutting speed, feed rate, and depth of cut), material type, coolant fluid, X and Z components of milling machine vibrations, and white noise were used as inputs. The original objective in the development of a dynamic monitoring system is to simulate wide ranges of machining conditions such as rough and finishing of several materials with and without cutting fluid. To achieve high accuracy of the resultant data, the full factorial design of experiment was used. To verify the accuracy of the proposed model, testing and recall/verification procedures have been carried out and results showed that the accuracy of 99.8 and 99.7 % were obtained for testing and recall processes.

Artificial water points facilitate the spread of an invasive vertebrate in arid Australia

Summary: The spread of invasive species after their initial introduction is often facilitated by human actions. In some cases, invaders only become established in habitats where dominant native species have been displaced as a result of human actions or where humans inadvertently provide essential resources such as food, water or shelter. We investigated if dams that provide water for livestock have facilitated the cane toad's (Rhinella marina) invasion of a hot semi-arid landscape by providing toads with a resource subsidy and hence refuge from extreme heat and aridity. To determine the relationship between the presence of surface water and habitat occupancy by toads, we surveyed natural and artificial water features for cane toads during the annual dry season. We used radiotracking and acoustic tags to determine whether movement patterns and shelter use of cane toads were focussed around dams. To determine whether dams provide toads with refuge from extreme heat and aridity, we deployed plaster models with internal thermometers to estimate ambient temperatures and toad desiccation rates in shelter sites. To determine whether dams alleviate the stress experienced by toads, we measured plasma corticosterone levels of toads that sheltered in and away from dams. Toads were present in sites with standing water and absent from waterless sites. Most radiotracked toads sheltered within 1 m of water. Toad movements were focussed around water. Toads tracked with passive acoustic telemetry over a 6-month dry season were highly resident at dams. Plaster models placed in toad shelter sites away from the water lost 27% more mass and experienced higher temperatures than models placed near the water's edge. Toads that sheltered in terrestrial shelters exhibited higher plasma corticosterone levels compared to toads that sheltered near dams. Dams provide toads with refuge habitats where they are less at risk from overheating and dehydration. Synthesis and applications. Artificial water points can facilitate biological invasions in arid regions by providing a resource subsidy for water-dependent invasive species. Our study suggests that there is scope to control populations of water-dependent invasive vertebrates in arid regions by restricting their access to artificial water points.

Application of multivariable control using artificial neural networks in a debutanizer distillation column

LOPES, Jose Soares Batista et al. Application of multivariable control using artificial neural networks in a debutanizer distillation column.In: INTERNATIONAL CONGRESS OF MECHANICAL ENGINEERING - COBEM, 19, 5-9 nov. 2007, Brasilia. Anais... Brasilia, 2007

Artificial fertilization of oocytes and sperm activation in pacu: effects of the spermatozoa:oocyte ratio, water volume, and in natura semen preservation

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Sites of Defoliation by Atta sexdens rubropilosa (Hymenoptera, Formicidae) in Artificial Plants

This study aimed to achieve a better understanding about the foraging behavior of leaf-cutter ant (Atta sexdens rubropilosa Forel) workers with respect to defoliation sites in plants. To accomplish that, artificial plants 70 cm in height were prepared and divided into four levels (heights), having natural plant leaves attached to them. Evaluations during the bioassays included the number of leaves dropped by the ants, as well as the percentage of plant mass removed. In all replicates, it became evident that the most exploited plant site is the apical region, which significantly differed from other plant levels.