Streamflow forecasting is essential for hydrological engineering. In accordance with the advancement of computer aids in this field, various machine learning (ML) models have been explored to solve this highly non-stationary, stochastic, and nonlinear problem. In the current research, a newly explored version of an ML model called the long short-term memory (LSTM) was investigated for streamflow prediction using historical data for forecasting for a particular period. For a case study located in a tropical environment, the Kelantan river in the northeast region of the Malaysia Peninsula was selected. The modelling was performed according to several perspectives: (i) The feasibility of applying the developed LSTM model to streamflow prediction was verified, and the performance of the developed LSTM model was compared with the classic backpropagation neural network model; (ii) In the experimental process of applying the LSTM model to the prediction of streamflow, the influence of the training set size on the performance of the developed LSTM model was tested; (iii) The effect of the time interval between the training set and the testing set on the performance of the developed LSTM model was tested; (iv) The effect of the time span of the prediction data on the performance of the developed LSTM model was tested. The experimental data show that not only does the developed LSTM model have obvious advantages in processing steady streamflow data in the dry season but it also shows good ability to capture data features in the rapidly fluctuant streamflow data in the rainy season. INDEX TERMS Deep learning model, streamflow forecasting, tropical environment, window scale forecasting, LSTM.

Abstract-Visual exploration is essential to the visualization and analysis of densely sampled 3D DTI fibers in biological speciments, due to the high geometric, spatial, and anatomical complexity of fiber tracts. Previous methods for DTI fiber visualization use zooming, color-mapping, selection, and abstraction to deliver the characteristics of the fibers. However, these schemes mainly focus on the optimization of visualization in the 3D space where cluttering and occlusion make grasping even a few thousand fibers difficult. This paper introduces a novel interaction method that augments the 3D visualization with a 2D representation containing a low-dimensional embedding of the DTI fibers. This embedding preserves the relationship between the fibers and removes the visual clutter that is inherent in 3D renderings of the fibers. This new interface allows the user to manipulate the DTI fibers as both 3D curves and 2D embedded points and easily compare or validate his or her results in both domains. The implementation of the framework is GPUbased to achieve real-time interaction. The framework was applied to several tasks, and the results show that our method reduces the user's workload in recognizing 3D DTI fibers and permits quick and accurate DTI fiber selection.

No abstract

No abstract

Traditionally, synthesizing the animation of floral blossom is accomplished mostly manually and hence a timeconsuming and laborious task. In this paper we propose an interactive biology-based flower modeling and animating approach. We model the initial shape of the floral components including petal, pistil, stamen and pedicel, and two terminal status of floral blossom with a parameterized sketching interface. The subtle geometry of the whole flower is created according to the phyllotactic rules. We then extract a set of growth parameters for describing the floral blossom, and build a dynamic growth model to regulate the continuous morphing of floral components in the process of blossom. Finally, a sequence of naturally deforming flower models is generated with these parameters. Our initial experimental results demonstrate that our approach can efficiently generate visually pleasing simulation of floral blossom, which is consistent with biological rules.978 -1-4244-1579-3/07/$25.00