ISH Journal of Hydraulic Engineering 

About: ISH Journal of Hydraulic Engineering is an academic journal published by Taylor & Francis. The journal publishes majorly in the area(s): Geology & Open-channel flow. It has an ISSN identifier of 2164-3040. Over the lifetime, 1001 publications have been published receiving 5972 citations. The journal is also known as: Indian Society for Hydraulics journal of hydraulic engineering & Journal of hydraulic engineering.

Temporal, variation of scour around circular bridge piers

Abstract: Extensive research has been carried out in the field of scour around bridge piers in past, which resulted in development of various mathematical expressions for computation of equilibrium scour depth. The modern trend in scour investigations is to study the temporal variation of scour rather than the equilibrium scour because the equilibrium scour occurs after a very long period of time. In this paper method of Kothyari et al. (1992) for computation of temporal variation of scour around circular pier has been modified with aid of experimental data of present study and data of various investigators. The mathematical model presented herein has potential of application for computation of temporal variation of scour depth around prototype circular bridge during the passage of the flood hydrograph.

Prediction of unsaturated hydraulic conductivity using adaptive neuro- fuzzy inference system (ANFIS)

Abstract: This paper aims to predict the unsaturated hydraulic conductivity of soil using Adaptive Neuro- fuzzy inference system (ANFIS), Multi-Linear Regression (MLR), and artificial neural network (ANN). Laboratory experiments carried out on 46 samples of sand, rice husk ash and fly ash (FA) mixture. Out of 46 data-set for modeling of unsaturated hydraulic conductivity 32 random data used for training and remaining 14 to the test. The results suggest improved performance by Gaussian membership function than triangular and generalized bell-shaped membership-based ANFIS. MLR is better than ANN and Gaussian membership function-based ANFIS for unsaturated hydraulic conductivity.

Rainfall—runoff modelling using artificial neural networks

Abstract: Runoff simulation and forecasting is essential for planning, designing and operation of water resources projects. In the present study, the rainfall—runoff process is modelled by coupling simple time—series (STS) and linear autoregressive (ARX) models with Artificial Neural Networks (ANN). The study uses the monthly data at Keesara, Damarcherla and Madhira gauging sites of Krishna basin. The study reveals that the performance of ANN model in the simulation and forecasting of monthly runoff during monsoon period can be improved considerably by including the residuals derived from STS and ARX models as additional inputs together with rainfall.

Estimation of natural ground water recharge

Abstract: Quantification of the rate of natural ground water recharge is a pre-requisite for efficient ground water resource management. It is particularly important in regions with large demands for ground water supplies, where such resources are the key to economic development. However, the rate of aquifer recharge is one of the most difficult factors to measure in the evaluation of ground water resources. Estimation of recharge, by whatever method, are normally subject to large uncertainties and errors. In this paper, various methods of estimating natural ground water recharge are outlined and critically reviewed with regard to their limitations and associated uncertainties.

Modeling the infiltration process with soft computing techniques

Abstract: Knowledge of infiltration process is very helpful in designing and planning of irrigation networks. In this study, the Artificial Neural Network (ANN) technique was used to estimate the infiltratio...