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Sand production by soil erosion in small watershed is a complex physical process. There are few physical models suitable to describe the characteristics of the intense erosion in domestic loess plateau. Introducing support vector machine (SVM) oriented to small sample data and possessing good extension property can be an effective approach to predict soil erosion because SVM has been applied in hydrological prediction to some extent. But there are no effective methods to select the rational parameters for SVM, which seriously limited the practical application of SVM. This paper explored the application of intelligence-based particle swarm optimization (PSO) algorithm in automatic selection of parameters for SVM, and proposed a prediction model by linking PSO and SVM for small sample data analysis. This method utilized the high efficiency optimization property and swarm paralleling property of PSO algorithm and the relatively strong learning and extending capacity of SVM. For an example of Huangfuchuan small watershed, its intensive fragmentation and intense erosion earn itself the name of “worst erosion in the world”. Using four characteristics selection algorithms of correlation feature selection, the primary affecting factors for soil erosion in this small watershed were determined to be the channel density, ravine area, sand rock proportion, and the total vegetation coverage. Based on the proposed PSO–SVM algorithm, the soil erosion modulus in the small watershed was predicted. The accuracy of the simulation and prediction was good, and the average error was 3.85%. The SVM predicting model was based on the monitoring data of sand production. The construction of the SVM erosion modulus prediction model for the small watershed comprehensively reflected the complex mechanism of soil erosion and sand production. It had certain advantage and relatively high practical value in small sample prediction in the discipline of soil erosion.

Superabsorbent polymer (SAP) can absorb a solution of 10-folds to 1000-folds as much as its own weight. Its micromorphology directly affects its water absorption mechanism and absorption characteristics. In this work, we selected three types of polymer products for research analysis: KH (sodium polyacrylate-type), HLM (polyacrylamide-type), and AS (polyacrylamide-kaliumcrylic acid-crosslinking-copolymer-type). The environmental scanning electronic microscope (ESEM) method and the fractal theory are applied for studying the micromorphology of the three types of SAPs. The results show that KH possesses the highest water absorption multiplying factor and HLM possesses the lowest one. Under the condition of repeated water absorption and water release, the structure of HLM hydrogel transfers from gridding membrane to network structure. Various types of cation solutions significantly affect the characteristics of micromorphology of HLM hydrogel, and both uniqueness and diversity prevail. Under the condition of repeated water absorption and water release, the swelling multiplying factor of HLM in deionized water abides by an “inverted U” distribution. The water absorption multiplying factor for the third time is the largest one. This is primarily caused by both reduction of resilience of crosslinking network and dissolution of polymer on the surface of hydrogel under the condition of repeated water absorption and water release. The boundary of hydrogel pore network of the three types of SAPs possesses fractal characteristics. The number of fractal dimensions is as follows: fractal dimension of HLM T3 (1.2100) > T5 (1.1226). © 2009 Wiley Periodicals, Inc. J Appl Polym Sci, 2009

The hydraulic property of drip emitters is primarily reflected in steady dissipation of pressure energy by flow resistance. The dentate path helps in dissipating energy as water travels to the emitter opening and maintains a turbulent flow condition that facilitates movement of particulate material out of system to avoid clogging. In this study, emitters were designed using variable geometric parameters. The visualisation characteristics of computational fluid dynamics (CFD) was utilised for analysing the law of impact on flow movement by the path width (1.6, 1.3, 1.0, 0.6 mm), dentate height (1.0, 1.3, 1.6, 2.0 mm) and dentate angle (32°, 41°, 53°, 63°) and revealed the correlation relationship between the dentate structure of flow path of emitters and energy dissipation. Meanwhile, an anti‐clogging analysis was conducted. The simulation results indicated that the selected turbulent model and the numerical algorithm were suitable for the hydraulic calculation in flow paths. The rear of dentation...

In this study, a supervised artificial neural network (ANN) trained by back propagation (BP) algorithm s was developed to predict the transpiration of poplar based on six input variables. Based on the transpiration characteristics of trees, a three‐layer BP network was constructed with six input units and one output unit. Daily average temperature, relative humidity, photosynthetic affective radiation, wind speed, sunlight duration and the soil water content 50 cm below the soil surface were considered as the six input variables of the network, which primarily affected the transpiration of poplar. The prediction of daily transpiration of poplar in Heilonggang region, Hebei province was conducted. The research results indicated that R 2 equalled 0.9534 between measured values and predicted values. The maximum relative error, the minimum relative error and the average relative error were 16.85, 1.49, and 4.2%, respectively. The proposed model could describe the relationship between the daily transp...