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 (ET0) 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 ET0 in the Beas-Sutlej basin of Himachal Pradesh (India). Four climatic locations in the basin namely, Kullu, Mandi, Bilaspur, and Chaba were selected. The meteorological dataset comprised air temperature (maximum, minimum and mean), relative humidity, solar radiation, and wind speed, recorded daily for a period of 35 years (1984-2019). The datasets from 1984 to 2012 and 2013 to 2019 were utilized for training and testing the models, respectively. The performance of the developed models was evaluated using several statistical indices. For each location, the best performed MLP-ANN model was the one with the complete combination of the meteorological data. The architecture of the best performing model for Kullu, Mandi, Bilaspur, and Chaba was (6-2-4-1), (6-5-4-1), (6-5-4-1), and (6-4-6-1), respectively. It was observed, however, that the performance of other models was also relatively good, given the limited meteorological data utilized in those models. Further, to appreciate the relative predictive ability of the developed models, a comparison was performed with four existing established empirical models. The approach adopted in this study can be effectively utilized by water users and field researchers for modelling and predicting ET0 in data-scarce locations.

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. MDD at optimum fly ash content was achieved at low water content, which resulted in less dry unit weight than that of typical conventional fill. In Beas, Sutlej, and Ghaggar sands the optimum fly ash content up to which the hydraulic conductivity value reduced uniformly was found to be 30, 45, and 40%, respectively. Any further increase in the fly ash content results in a negligible decrease in hydraulic conductivity value. The observed hydraulic conductivity of sand-fly ash mix lies in the range of silts, which emboldens the use of sand-fly ash mix as embankment material. Further, the evaluation of empirical equations considered in the study substantiates the efficacy of the Terzaghi equation in estimating the hydraulic conductivity of river sand-fly ash mix.