National Institute of Technology Calicut

About: National Institute of Technology Calicut is a education organization based out in Kozhikode, Kerala, India. It is known for research contribution in the topics: Computer science & Control theory. The organization has 3627 authors who have published 4638 publications receiving 50830 citations. The organization is also known as: Calicut Regional Engineering College & NIT Calicut.

Neural network modeling for groundwater-level forecasting in coastal aquifers

Abstract: Advances in the artificial intelligence-based models can act as robust tools for modeling hydrological processes. Neural network architectures coupled with learning algorithms are considered as useful modeling tools for groundwater-level fluctuations. Emotional artificial neural network coupled with genetic algorithm (EANN-GA) is one such novel hybrid neural network which has been used in the present study for the forecasting of groundwater levels at three sites (Site H3, Site H4.5, and Site H9) in a coastal aquifer system. This study was conceived to address and investigate the efficiency of the ensemble model (EANN-GA) for forecasting one-month ahead groundwater level and to compare its performance with emotional artificial neural network (EANN), generalized regression neural network (GRNN), and the conventional feedforward neural network (FFNN). Variations in the rainfall, tidal levels, and groundwater levels are selected as inputs for the development of EANN-GA, EANN, GRNN, and FFNN models. Suitable goodness-of-fit criteria such as Nash–Sutcliffe efficiency (NSE), bias, root mean squared error (RMSE), and graphical indicators are used for assessing the efficiency of the developed models. The improvement in the performance of the EANN-GA model over the developed EANN, GRNN, and FFNN models in terms of NSE is 0.81, 6.02, and 9.56% at Site H3; 4.35, 5.50, and 22.68% at Site H4.5; and 1.05, 7.18, and 21.75% at Site H9. Thus, it can be inferred that the EANN-GA model outperforms the developed EANN model, GRNN model, and FFNN model. Further, this paper examines the predictive capability of extreme events by the EANN-GA, EANN, GRNN, and FFNN models. The RMSE values of the EANN-GA model at all peak points are found as 0.27, 0.23, and 0.10 m at sites H3, H4.5, and H9, respectively, and the results indicate superior performance of EANN-GA model. To check the generalization ability of the developed EANN-GA models, they are validated with the data of another site (Site I2) located in the same coastal aquifer. Superior prediction capability and generalization ability make the EANN-GA model a better alternative for predicting groundwater levels. Overall, this study demonstrates the effectiveness of EANN-GA in modeling spatio-temporal fluctuations of groundwater levels. It is also concluded that the EANN-GA model yields remarkably better predictions of extreme events, and hence, it could be a promising technique for developing alarm systems for real-world water problems.

Simulation-based metamodels for scheduling a dynamic job shop with sequence-dependent setup times

Abstract: This paper presents the salient aspects of a simulation-based experimental study of scheduling rules for scheduling a dynamic job shop in which the setup times are sequence-dependent. A discrete-event simulation model of the job shop system is developed for the purpose of experimentation. Seven scheduling rules from the literature are incorporated in the simulation model. Five new setup-oriented scheduling rules are proposed and implemented. Simulation experiments have been conducted under experimental conditions characterised by different setup time ratios. The simulation results are analysed using statistical significance tests. The results indicate that setup-oriented rules provide better performance than ordinary rules. The difference in performance between these two groups of rules increases with increase in shop load and setup time ratio. One of the proposed rules performs better for mean flow time and mean tardiness measures. Multiple linear regression based metamodels have been developed for the b...