English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

High-cost remediation of groundwater pollution makes it important to obtain exact information about the source. This is quite difficult to achieve in naturally ill-posed inverse problems of this kind. If the aquifer parameters are also unknown, the problem becomes even more challenging. To address this difficulty, we propose the alternating direction genetic algorithm (ADGA) approach, together with modification of the order of magnitude of the decision variables, to increase the accuracy of the results and computational efficiency. Seven scenarios were designed to test the accuracy of the proposed approach in aquifer with different properties, number of pollution sources, parameters and measurement errors. The results show that combining the ADGA approach with modification of the order of magnitude of the decision variables for identifying both groundwater pollution source and aquifer parameters significantly increases the accuracy of estimated results. The NE value for the estimated results decreased from 9.81% to 58.44% for different cases, and computation time is about half decreased. In addition, the approach is applicable in situations where concentrations of observational data with measurement error, and for multiple source locations and non-uniform media.

Groundwater pollution source identification problems with unknown aquifer parameters by ADGA approach