An application of the recently proposed generalized relevance learning vector quantization (GRLVQ) to the analysis and modeling of time series data is presented. We use GRLVQ for two tasks: first, for obtaining a phase space embedding of a scalar time series, and second, for short term and long term data prediction. The proposed embedding method is tested with a signal from the wellknown Lorenz...

For stormwater management, Low Impact Development (LID) practices provide more sustainable solutions than traditional piping and storm ponds. However, to be effective, LID practices must be integrated into planning at the beginning of the design process; yet architects and related design professionals making early decisions are not equipped to consider runoff calculations with their current too...

modeling of stream flow–suspended sediment relationship is one of the most studied topics in hydrology due to itsessential application to water resources management. recently, artificial intelligence has gained much popularity owing toits application in calibrating the nonlinear relationships inherent in the stream flow–suspended sediment relationship. thisstudy made us of adaptive neuro-fuzzy ...

Reducing non-point source phosphorus (P) loss to drinking water reservoirs is a main concern for New York City watershed planners, and modeling of P transport can assist in the evaluation of agricultural effects on nutrient dynamics. A spatially distributed model of total dissolved phosphorus (TDP) loading was developed using raster maps covering a 164-ha dairy farm watershed. Transport of TDP ...

This paper introduces two parsimonious models for predicting runoff for ungauged basins from the observed river flow data in gauged basins and precipitation data from rain gauging stations in the same area. The models assume the runoff is related to the precipitation subject to variance of drainage basins. The runoff can be modeled as a function of precipitation and parameters determined by bas...

[1] The augmented Noah land surface model described in the first part of the two‐part series was evaluated here over global river basins. Across various climate zones, global‐scale tests can reveal a model’s weaknesses and strengths that a local‐scale testing cannot. In addition, global‐scale tests are more challenging than local‐ and catchment‐scale tests. Given constant model parameters (e. g...