Assessment of sediment transport approaches for sand-bed rivers by means of machine learning

Abstract

The quantification of the sediment carrying capacity of a river is a difficult task that has received much attention. For sand-bed rivers especially, several sediment transport functions have appeared in the literature based on various concepts and approaches; however, since they present a significant discrepancy in their results, none of them has become universally accepted. This paper employs three machine learning techniques, namely artificial neural networks, symbolic regression based on genetic programming and an adaptive-network-based fuzzy inference system, for the derivation of sediment transport formulae for sand-bed rivers from field and laboratory flume data. For the determination of the input parameters, some of the most prominent fundamental approaches that govern the phenomenon, such as shear stress, stream power and unit stream power, are utilized and a comparison of their efficacy is provided. The results obtained from the machine learning techniques are superior to those of the commonly-used sediment transport formulae and it is shown that each of the input combinations tested has its own merit, as they produce similarly good results with respect to the data-driven technique employed.

Editor Z.W. Kundzewicz     

Comparative analysis of fuzzy inference systems for water consumption time series prediction

Two types of fuzzy inference systems (FIS) are used for predicting municipal water consumption time series. The FISs used include an adaptive neuro-fuzzy inference system (ANFIS) and a Mamdani fuzzy inference systems (MFIS). The prediction models are constructed based on the combination of the antecedent values of water consumptions. The performance of ANFIS and MFIS models in training and testing phases are compared with the observations and the best fit model is identified according to the selected performance criteria. The results demonstrated that the ANFIS model is superior to MFIS models and can be successfully applied for prediction of water consumption time series.     

基于钻孔数据的含断层地质体三维建模方法

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前兆台站及观测测项的密度改正方法

介绍了使用MYCIN不精确推理模型生成发震可信度分布图、 可信度密度等值线图以及进行前兆测项的密度改正方法。 通过对1989年大同6.1级和1998年张北6.2级地震的震前异常进行推理和比较, 经过密度改正的可信度密度等值线图可以克服某些地区由于前兆测项密度较高而造成对地点判断的偏差, 从而对未来发震地区的判断起到较好的效果。     

ALCGEIS知识获取与推理机设计

运用专家系统的知识获取和推理机设计原理与方法,探讨了ALCGEIS系统的基本结构、知识分级、知识获取过程和实现程序,推理机的基本原理和控制策略及其程序实现的算法,以及推理知识的搜索策略;论述了 AIEGEIS推理规则的设计方法及其推理算法。     

基于交互多模型的水下目标跟踪方法

According to the requirements of real-time performance and reliability in underwater maneuvering target tracking as well as clarifying motion features of the underwater target, an interacting multiple model algorithm based on fuzzy logic inference （FIMM） is proposed. Maneuvering patterns of the target are represented by model sets, including the constant velocity model （CA）, the Singer mode~, and the nearly constant speed horizontal-turn model （HT） in FIMM technology. The simulation results show that compared to conventional IMM, the reliability and real-time performance of underwater target tracking can be improved by FIMM algorithm.     

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