Modelling the reference crop evapotranspiration in the Beas-Sutlej basin (India): an artificial neural network approach based on different combinations of meteorological data.

Accurate prediction of the reference evapotranspiration (ET) is vital for estimating the crop water requirements precisely. In this study, we developed multi-layer perceptron artificial neural network (MLP-ANN) models considering different combinations of the meteorological data for predicting the ET in the Beas-Sutlej basin of Himachal Pradesh (India). Four climatic locations in the basin namely, Kullu, Mandi, Bilaspur, and Chaba were selected.

Experimental investigations for assessing the influence of fly ash on the flow through porous media in Darcy regime.

Hydraulic conductivity plays a vital role in the studies encompassing explorations on flow and porous media. The study investigates the compaction characteristics of a river sand (Beas, Sutlej, and Ghaggar rivers) and fly ash mix in different proportions and evaluates four empirical equations for estimating hydraulic conductivity. Experiments show that an increase in the fly ash content results in a decrease in the maximum dry density (MDD) and an increase in the corresponding optimum moisture content (OMC) of sand-fly ash samples.