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A surrogate modeling framework is developed in this study to circumvent the computational burden of high-fidelity numerical groundwater models for arid coastal aquifers. Two different surrogate models, namely, artificial neural network (ANN) and Gaussian process model (GPM) are trained to replace the computationally expensive numerical flow and transport model OpenGeoSys. A novel time-dependent training scheme is introduced which helps the surrogates in tracking the discrete-time state-space trajectories of the high-fidelity model, thereby making them suitable for variable-time simulations. The surrogates are also tested in the extrapolation range corresponding to some extreme boundary conditions such as a very high rate of extraction. Both the surrogates show comparable accuracy in efficiently approximating the numerical model response; however, ANN is found to be much faster than GPM for the size of the data used. The trained surrogates are then used in developing a long-term planning and management framework for analyzing feasible management scenarios in the coastal aquifer of Oman. [ABSTRACT FROM AUTHOR]

ABSTRACTThe paper describes some of the recent developments and applications for knowledge extraction from trained artificial neural network based hydrological models. The methods are in general derived from the casual relationship between inputs and outputs, and analysis of the hidden neuron behaviour. The discussions are focused on works related to explaining the internal behaviour and embedded physical process identification in a trained ANN rainfall-runoff model. It is envisaged that knowledge extraction is important for artificial neural networks to gain wider degree of acceptance especially in the context of hydrologic modeling. Therefore, this domain has to become a major field of research since it validates the use of neural networks for applications where reasons or explanations on why or how a result has been achieved are important.

ABSTRACTModeling of rainfall runoff (R-R) processes is useful in many water resources management activities. Traditionally, hydrologists have employed deterministic/conceptual methods for R-R modeling. Recently, Artificial Neural Networks (ANNs) have become popular tools for R-R modeling. This paper reviews the literature on and presents state-of-the-art approaches to ANN R-R modeling. Certain aspects of ANN R-R modeling have been covered in greater detail. These include input selection, data division, ANN training, hybrid modeling, and extrapolation beyond the range of training data. There is a strong need to carry out extensive research on these aspects while developing ANN R-R models.

A variety of forecast modeling techniques, from conventional techniques such as regression and time series analyses to relatively new artificial intelligence (AI) techniques such as expert systems and artificial neural networks (ANNs), were investigated for use in short‐term water demand forecasting. Daily water demand, daily maximum air temperature, and daily total rainfall data from Lexington, Ky., for 1982–92 were used to develop and test several forecast models. The performance of each model was evaluated using two standard statistical parameters. On the basis of the measured statistical parameters, the AI models outperformed the conventional models. Both expert system and ANN technologies should be further explored by water utility engineers and managers because these techniques have the potential to enhance the operational performance of various water supply and delivery systems.

AbstractA decision support system has been developed for drought characterization and management. The purpose of the decision support system is to assist the operators and water managers of the water supply system of the City of Lexington, Kentucky. The motivation of this study was a severe drought that occurred in the state of Kentucky during the summer of 1988. The data derived from the City of Lexington, Kentucky and the Kentucky River Basin were employed in this study. The developed decision support system consists of three components: a water demand forecasting component, a streamflow forecasting component, and an integrated expert system component. The water demand and streamflow forecasting components of the decision support system predict the water consumption for the City of Lexington and the flow in Kentucky River at Lock 10 near Winchester, Kentucky, respectively. The lead time of the forecasting models was taken as five days as they were intended to be employed in developing a short-term drought management policy. Various modeling techniques ranging from regression and time series analysis to the relatively new technique of expert systems and artificial neural networks were explored for forecasting both water demand and streamflow. The integrated expert system component consists of five sub-components. Each sub-component entails developing a knowledge base for a specific purpose. The expert system component integrates all sub-components and characterizes the drought potential in the coming five days and recommends a drought management policy for the week to come. The developed decision support system is capable of running on a persona] computer “and provides a user-friendly platform for decision-makers to explore a wide range of drought management alternatives.

•Aqueous leaf extract C. papayahas been used for the synthesis of AgNPs (Pa-AgNPs).•Synthesized Pa-AgNPs has been characterized using UV–visSpectrophotometer, TEM, EDS, Zeta potential and DLS.•Synergistic effect of Pa-AgNPs + Antibiotic was observed against E.coliand S. aureusin ZOI assay.•Efficient dye degradation of individual and dye mixture was also observed with Pa-AgNPs.