NDLI: Development of an Improved Pollution Source Identification Model Using Numerical and ANN Based Simulation-Optimization Model

Content Provider	Springer Nature Link
Author	Borah, Triptimoni Bhattacharjya, Rajib Kumar
Copyright Year	2016
Abstract	The identification of unknown pollution sources is an important and challenging task for the engineers working on pollution management of a groundwater aquifer. The locations and transient magnitude of unknown contaminant sources can be identified using inverse optimization technique. In this approach, the absolute difference between the simulated and the observed contaminant concentration at the observation locations of the aquifer is minimized by using an optimization algorithm. The simulated concentrations is calculated using the aquifer simulation model. As such, there is a need to incorporate the aquifer simulation model with the optimization model. Thus the performance of the model is highly related to the aquifer simulation model. The incorporation of the sophisticated numerical simulation model will give better performance, but the model will be computationally expensive. On the other hand, the model will be computationally less expensive if an approximate simulation model is used in place of the numerical simulation model. However, in this case, the predictive performance of the model will decline. For achieving efficiency in both computational time as well as in predicting the performance, this study presents a new genetic algorithms based simulation-optimization method incorporating both the numerical and the approximate simulation models. The efficiency and field applicability of the model is demonstrated using illustrative study areas. The performance evaluation of the model shows that the proposed model has the potential for real-world field applications.
Starting Page	5163
Ending Page	5176
Page Count	14
File Format	PDF
ISSN	09204741
Journal	Water Resources Management
Volume Number	30
Issue Number	14
e-ISSN	15731650
Language	English
Publisher	Springer Netherlands
Publisher Date	2016-08-31
Publisher Institution	European Water Resources Association (EWRA)
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Pollution sources Groundwater management Optimization methods Simulation-optimization Genetic algorithms Artificial neural networks Hydrogeology Hydrology/Water Resources Geotechnical Engineering & Applied Earth Sciences Atmospheric Sciences Civil Engineering Environment
Content Type	Text
Resource Type	Article
Subject	Water Science and Technology Civil and Structural Engineering

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Groundwater System Modeling for Simultaneous Identification of Pollution Sources and Parameters with Uncertainty Characterization

Application of Neural Networks and Optimization Model in Conjunctive Use of Surface Water and Groundwater

Efficient Identification of Unknown Groundwater Pollution Sources Using Linked Simulation-Optimization Incorporating Monitoring Location Impact Factor and Frequency Factor

A self-tuning ANN model for simulation and forecasting of surface flows

Coupled Quantity-Quality Simulation-Optimization Model for Conjunctive Surface-Groundwater Use

Developments on Modeling of Groundwater Flow and Contaminant Transport

Prediction the Groundwater Level of Bastam Plain (Iran) by Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS)

The Use of NARX Neural Networks to Forecast Daily Groundwater Levels

Comparative Study of Artificial Neural Networks and Wavelet Artificial Neural Networks for Groundwater Depth Data Forecasting with Various Curve Fractal Dimensions

Development of an Improved Pollution Source Identification Model Using Numerical and ANN Based Simulation-Optimization Model

Similar Documents

Groundwater System Modeling for Simultaneous Identification of Pollution Sources and Parameters with Uncertainty Characterization

Application of Neural Networks and Optimization Model in Conjunctive Use of Surface Water and Groundwater

Efficient Identification of Unknown Groundwater Pollution Sources Using Linked Simulation-Optimization Incorporating Monitoring Location Impact Factor and Frequency Factor

A self-tuning ANN model for simulation and forecasting of surface flows

Coupled Quantity-Quality Simulation-Optimization Model for Conjunctive Surface-Groundwater Use

Developments on Modeling of Groundwater Flow and Contaminant Transport

Prediction the Groundwater Level of Bastam Plain (Iran) by Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS)

The Use of NARX Neural Networks to Forecast Daily Groundwater Levels

Comparative Study of Artificial Neural Networks and Wavelet Artificial Neural Networks for Groundwater Depth Data Forecasting with Various Curve Fractal Dimensions

Development of an Improved Pollution Source Identification Model Using Numerical and ANN Based Simulation-Optimization Model