Artificial neural network model (ANN) has been extensively used in hydrological prediction. Generally, most existing rainfall-runoff models including artificial neural network model are not very successful at simulating streamflow in karst watersheds. Due to the complex physical structure of karst aquifer systems, runoff generation processes are quite different during flood and non-flood periods. In this paper, an ANN model based on back-propagation algorithm was developed to simulate and predict daily streamflow in karst watersheds. The idea of threshold was introduced into artificial neural network model [hereafter called Threshold-ANN model (T-ANN)] to represent the nonlinear characteristics of the runoff generation processes in the flood and non-flood periods.The T-ANN model is applied to the Hamajing watershed, which is a small karst watershed in Hubei Province, China. The network input, the previous discharge, is determined by the correlative analysis, and the network structure is optimized with the maximum Nash coefficient as the objective function. And the precipitation and previous discharge are chosen as the threshold factors to reflect the effect of specificity of karst aquifer systems, respectively. By using the T-ANN, the simulation errors of streamflow have been reduced, and the simulation becomes more successful, which would be helpful for runoff prediction in karst watersheds.
In order to discuss the effect of rainfall patterns and land use types on soil erosion, the experiment is carried out under natural rainfall events on different kinds of runoff plots in Zhangjiachong watershed. Based on the observed data of 44 individual rainfall events including moderate, heavy and storm rainfall, the differences of erosion modulus among hedgerows plots, terrace plots, and slope plots under different rainfall patterns are analyzed. And the effects of hedgerow and terrace patterns on control of soil loss are revealed by RUSLE. Wilcoxon signed rank test is applied to analyze the significant difference of erosion modulus in different plots and the coefficient of variation is used to compare the characteristics of erosion modulus under different rainfall patterns. The results show that the soil erosion modulus of earth banked terrace has the highest value and the lowest soil erosion modulus occurs in the slope land with hedgerows. The coefficients of variation for soil erosion modulus under heavy and storm rainfall are larger than that of moderate rainfall. Hedgerow pattern can effectively control soil erosion under moderate and heavy rainfall while the effect of hedgerow is considerably weakened under storm rainfall. Earth banked terraces own the highest erosion modulus followed by slope land and stone dike terraces.
Visible-light communication (VLC) is a promising method for indoor positioning. The received signal strength algorithm is the most widely used localization algorithm in visible-light positioning (VLP) systems. However, in a VLP system, the photodiode (PD) will have a small rotation angle during movement, which will result in a massive positioning error ignoring the angle. In this study, a three-dimensional (3D) indoor VLP system using an improved whale optimization algorithm (IWOA) is proposed to reduce the error caused by the PD rotation. Firstly, the model of the VLC system with the PD rotation angles is introduced. Secondly, a novel IWOA with an elite opposition-based learning strategy and Lévy flight strategy is proposed. The convergence speed and accuracy of the WOA are improved. Lastly, the IWOA algorithm is efficiently utilized to address the problem with the PD rotation in the indoor VLP system. Simulation results show that the average error of 3D positioning is 2.14 cm with no PD rotation. When the PD has a rotation angle, the average positioning error estimated by ignoring the rotation angle is 27.14 cm, while that estimated by considering the rotation angle is 7.85 cm. In the VLP system, the positioning error with the PD rotation angle is effectively reduced by the proposed algorithm, which can be applied in a variety of indoor location scenes.
Abstract. The baseflow is the drainage from the groundwater and soil water to the streamflow. As one important source of the streamflow, the baseflow could be the main source of the streamflow in the dry season. The Wei River, located in the semi-arid region of the Loess Plateau which is overlain by deep and loose soil, is the largest tributary of the Yellow River. According to former research, most of the streamflow in the dry season in the headwater of the Yellow River is baseflow. For the whole Yellow River basin, the baseflow is an important component of the streamflow, and accounts for about 44% of the annual runoff. Physically-based distributed hydrological models can simulate the runoff components separately, and are important tools to analyse the runoff components. Given the importance of the baseflow in the dry season for drought relief to support the ecological water requirement and irrigation, especially in the Wei River, the baseflow is analysed in this study. To investigate the baseflow in the Upper Wei River basin, a semi-distributed hydrological model based on a Representative Elementary Watershed approach (THREW) is employed to investigate the runoff generation process. To compare the results, an automatic baseflow separation method proposed by Arnold is used to separate the baseflow from the daily streamflow at Beidao hydrological station in Upper Wei River basin from 2001 to 2004. Based on the hydrological modelling and the Arnold separation method, the average annual baseflow index, i.e. the ratio of baseflow to the total runoff, is estimated as in the range of 0.30–0.36. The average intra-annual monthly baseflow index represents the seasonality of the baseflow due to the seasonality of the precipitation and evapotranspiration, and is also analysed.
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