The amount of transported sediment load by streams is a vital but high nonlinear dynamic process in water resources management. In the current paper, two optimum predictive models subjected to artificial neural network (ANN) were developed. The employed inputs were then prioritized using diverse sensitivity analysis (SA) methods to address new updated but more efficient ANN structures. The models were found through the 263 processed datasets of three rivers in Idaho, USA using nine different measured flow and sediment variables (e.g., channel geometry, geomorphology, hydraulic) for a period of 11 years. The used parameters were selected based on the prior knowledge of the conventional analyses in which the effect of suspended load on bed load was also investigated. Analyzed accuracy performances using different criteria exhibited improved predictability in updated models which can lead to an advanced understanding of used parameters. Despite different SA methods being employed in evaluating model parameters, almost similar results were observed and then verified using relevant sensitivity indices. It was demonstrated that the ranked parameters using SA due to covering more uncertainties can be more reliable. Evaluated models using sensitivity indices showed that contribution of suspended load on predicted bed load is not significant.

This paper presents a structured dictionary-based model for hyperspectral data that incorporates both spectral and contextual characteristics of spectral samples. The idea is to partition the pixels of a hyperspectral image into a number of spatial neighborhoods called contextual groups and to model the pixels inside a group as members of a common subspace. That is, each pixel is represented using a linear combination of a few dictionary elements learned from the data, but since pixels inside a contextual group are often made up of the same materials, their linear combinations are constrained to use common elements from the dictionary. To this end, dictionary learning is carried out with a joint sparse regularizer to induce a common sparsity pattern in the sparse coefficients of a contextual group. The sparse coefficients are then used for classification using a linear support vector machine. Experimental results on a number of real hyperspectral images confirm the effectiveness of the proposed representation for hyperspectral image classification. Moreover, experiments with simulated multispectral data show that the proposed model is capable of finding representations that may effectively be used for classification of multispectral resolution samples.

Modularity as a system of separate and independent sub-tasks is the appropriate approach to improve the performance of artificial neural networks (ANNs) models in hydrological processes. Using this characteristic, a block combined neural network (BCNN) structure incorporated with genetic algorithm (GA) and an additional decision block was suggested in this study. The optimum topology of embedded networks in each block was detected using a vector-based method subjected to different internal characteristics. This model then was applied on 879 datasets of bedload, considering velocity, discharge, mean grain size, slope, and depth as model’s inputs over streams in Idaho basin-USA. The correct classification rate of predicted bedload using BCNN (89.77%) comparing to other ANNs, and empirical models showed superior improvement in accuracy performance. Results of computed error metrics and confusion matrixes also demonstrated outstanding progress in BCNN than other models. It was showed that BCNN as a new method with an appropriate accuracy level could effectively be adopted for bedload prediction purposes.