Avinash Agarwal

TL;DR: In this article, the authors used Support Vector Machines (SVM) to simulate runoff and sediment yield from watersheds and found that SVM provided significant improvement in training, calibration and validation as compared to ANN.

TL;DR: In this paper, daily, weekly, ten-daily, and monthly monsoon runoff and sediment yield from an Indian catchment were simulated using back propagation artificial neural network (BPANN) technique, and the results compared with the observed and with those due to single- and multi-input linear transfer function models.

TL;DR: In this paper, the Vamsadhara River basin of India has been studied and a batch-and pattern-learned back-propagation artificial neural network (ANN) model with variable learning rate (α) and momentum term (β) was used for optimisation.

TL;DR: Structural phase transitions after thermal annealing of films deposited at RT and 200°C are confirmed and it is found that electron-phonon interaction is a function of temperature and particle size and is independent of the structure.

TL;DR: Modified SI is an important marker for predicting the mortality rate and is significantly better than heart rate, systolic blood pressure, DBP and SI alone and should be used in the triage of serious patients, including trauma patients in the emergency room.