Comparison of ANN approach with 2D and 3D hydrodynamic models for simulating estuary water stage

Accurately predicting tidal levels, including tidal and freshwater discharge effects, is important for human activities in estuaries. The traditional harmonic analysis method and numerical modeling are usually adopted to simulate and predict estuary water stages. This study applied artificial neural networks (ANNs) as an alternative modeling approach to simulate the water stage time-series of the Danshui River estuary in northern Taiwan. We compared this approach with vertical (laterally averaged) 2D and 3D hydrodynamic models. Five ANN models were constructed to simulate the water stage time-series at the Shizi Tou, Taipei Bridge, Rukuoyan, Xinhai Bridge, and Zhongzheng Bridge locations along the Danshui River estuary. ANN models can preserve nonlinear characteristics between input and output variables and are superior to physical-based hydrodynamic models during the training phase. The simulated results reveal that the vertical 2D and 3D hydrodynamic models could not capture the observed water stages during an input of high freshwater discharge from upstream boundaries, while the ANN could match the observed water stage. However, during the testing phase, the ANN approach was slightly inferior to the 2D and 3D models at the Xinhai Bridge, Zhongzheng Bridge, and Rukouyan locations. Our results show that the ANN was able to predict the water stage time-series with reasonable accuracy, suggesting that ANNs can be a valuable tool for estuarine management.     

Comparison of 2-D and 3-D computer models for the M. Salta rock fall, Vajont Valley, northern Italy

A rock fall occurs when a fragment of rock is detached from a cliff and travels down-slope at high speed. Rock falls are a constant hazard in mountainous regions and pose a significant threat to the population. In this paper, a comparison of software designed to model rock falls is presented. The computer codes selected for the experiment are STONE and RocFall®. STONE is a research code for the 3-dimensional simulation of rock falls. RocFall® is commercial software widely used for the 2-dimensional simulation of rock falls along user defined topographic profiles. The two computer programs require similar input and provide comparable outputs, allowing for a quantitative evaluation of their modelling results. To compare the software, the Monte Salta rock fall, in northern Italy, was selected. Specific tests were designed to compare the ability of the software to predict the maximum travel distance of the falling boulders, and the distance from the ground of the computed rock fall trajectories. Results indicate that the two rock fall modelling codes provide similar—but not identical—results. In general, STONE computes higher and longer rock fall trajectories than RocFall®, and allows identifying a larger area as potentially affected by falling boulders.     

Characteristic of exit moments and models of enlargement of states for finite markov chains in terms of global memory functionals

A notion of local- and global-memory functionate for discrete-type distributions is introduced by analogy with the notion of the memory for continuous-type distributions introduced in Muth’s papers. In the class of PH-distributions (i.e., of distributions of the time of Markov chain exit from a subset of states) the necessary and sufficient conditions are obtained for the case where the exit time has an exponential (continuous-time) or a geometric (discrete time) distribution. A new notion of a global memory functional for decomposition of the state space of a finite Markov chain is introduced. Its properties as a measure of quality of decomposition and enlargement of a state space are studied. The asymptotic optimality is proved. Translated from Kibernetika i Sistemnyi Analiz, No. 3, pp. 110–120, May–June, 2000.     

Integration of Hough Transform of lines and planes in the framework of conformal geometric algebra for 2D and 3D robot vision

This paper presents the application of 2D and 3D Hough Transforms together with conformal geometric algebra to build 3D geometric maps using the geometric entities of lines and planes. Among several existing techniques for robot self-localization, a new approach is proposed for map matching in the Hough domain. The geometric Hough representation is formulated in such a way that one can easily relate it to the conformal geometric algebra framework; thus, the detected lines and planes can be used for algebra-of-incidence computations to find geometric constraints, useful when perceiving special configurations in 3D visual space for exploration, navigation, relocation and obstacle avoidance. We believe that this work is very useful for 2D and 3D geometric pattern recognition in robot vision tasks.     

Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modelling

The application of Artificial Neural Networks (ANNs) in the field of environmental and water resources modelling has become increasingly popular since early 1990s. Despite the recognition of the need for a consistent approach to the development of ANN models and the importance of providing adequate details of the model development process, there is no systematic protocol for the development and documentation of ANN models. In order to address this shortcoming, such a protocol is introduced in this paper. In addition, the protocol is used to critically review the quality of the ANN model development and reporting processes employed in 81 journal papers since 2000 in which ANNs have been used for drinking water quality modelling. The results show that model architecture selection is the best implemented step, while greater focus should be given to input selection considering input independence and model validation considering replicative and structural validity.