Six years (1998–2003) of SeaWiFS multispectral satellite data are used to document the seasonal and interannual variability of the Columbia River plume on the North American west coast. A supervised classification scheme using 5 channels of normalized water-leaving radiance (nLw at 412, 443, 490, 510 and 555 nm), with training pixels adjusted temporally to optimize the signature of plume core c...

There is diversity in the use of global goodness-of-fit statistics to determine how well models forecast flood hydrographs. This paper compares the results from nine evaluation measures and two forecasting models. The evaluation measures comprise global goodness-of-fit measures as recommended by Legates and McCabe (1999) and Smith (2000) and more flood specific measures which gauge the ability ...

The data from the SynthDB dataset used for training consists of the 99 sequences featuring 2 textures [1]. Each texture is labeled as one of these 12 classes: grass, jellyfish, pond, boiling, escalator, fire, river-far, river, steam, plant-a, plant-i, and sea-far. All results reported on the SynthDB and Dyntex datasets used a single scale, i.e. S=1. In our experiments, the use of multiple scale...

Accurate real-time reservoir inflow forecasting is an important requirement for operation, scheduling and planning conjunctive use in any basin. In this study, Time Delay Artificial Neural Network (TDANN) models, which are time lagged feed-formatted networks with delayed memory processing elements at the input layer, are applied to forecast the daily inflow into a planned Reservoir (Almopeos Ri...

In this research, feedforwardANN (Artificial Neural Network) model is developed and validated for predicting the pH at 10 different locations of the distribution system of drinking water of Hyderabad city. The developed model is MLP (Multilayer Perceptron) with back propagation algorithm.The data for the training and testing of the model are collected through an experimental analysis on weekly ...

Effective watershed management requires accurate modeling of river discharge. Many years of data collection are often required to capture variations in seasonal trends and produce accurate predictive and descriptive models. In this study artificial neural networks that employ inductive transfer are used to develop models that predict the discharge (flow rate) of streams in Nova Scotia from weat...

Estimation of suspended sediments in rivers using soft computing techniques has been extensively performed around the world since 1990’s. However, accuracy in the results was always found to be highly desired and a profound crucial task. This study presents a thorough comparison between the performances of best basis function of Radial Basis Functions (RBF) and the best training algorithm in Mu...

riparian vegetation is a component of river system that performs certain basic functions including primary vegetative production, protecting stream banks from erosion, trapping sediments, promoting water quality, wildlife habitat and fisheris, forage for livestock, etc. in this research, spatio-temporal change of vegetation riparian area of aghbolagh-river as an upstream of karoon-river for 65-...

General circulation models (GCMs), the climate models often used in assessing the impact of climate change, operate on a coarse scale and thus the simulation results obtained from GCMs are not particularly useful in a comparatively smaller river basin scale hydrology. The article presents a methodology of statistical downscaling based on sparse Bayesian learning and Relevance Vector Machine (RV...