Anabi Hilary Kelechi¹, Mohammed H. Alsharif², Anya Chukwudi-eke Anya³, Mathias U. Bonet¹, Samson Aiyudubie Uyi¹, Peerapong Uthansakul^4,*, Jamel Nebhen⁵, Ayman A. Aly⁶

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2405-2424, 2021, DOI:10.32604/cmc.2021.018686

Abstract Water is one of the needs with remarkable significance to man and other living things. Water quality management is a concept based on the continuous monitoring of water quality. The monitoring scheme aims to accumulate data to make decisions on water resource descriptions, identify real and emergent issues involving water pollution, formulate priorities, and plan for water quality management. The regularly considered parameters when conducting water quality monitoring are turbidity, pH, temperature, conductivity, dissolved oxygen, chemical oxygen demand, biochemical oxygen demand, ammonia, and metal ions. The usual method employed in capturing these water parameters is the manual collection and sending… More >

Shuangsheng Zhang^1,5, Hanhu Liu¹, Jing Qiang^2,*, Hongze Gao^3,*, Diego Galar⁴, Jing Lin⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.119, No.2, pp. 373-394, 2019, DOI:10.32604/cmes.2019.03825

Abstract Coupling Bayes’ Theorem with a two-dimensional (2D) groundwater solute advection-diffusion transport equation allows an inverse model to be established to identify a set of contamination source parameters including source intensity (M ), release location ( X₀ , Y₀) and release time (T₀), based on monitoring well data. To address the issues of insufficient monitoring wells or weak correlation between monitoring data and model parameters, a monitoring well design optimization approach was developed based on the Bayesian formula and information entropy. To demonstrate how the model works, an exemplar problem with an instantaneous release of a contaminant in a confined groundwater… More >