TY - JOUR
T1 - Optimal spatio-temporal design of water quality monitoring networks for reservoirs
T2 - Application of the concept of value of information
AU - Maymandi, Nahal
AU - Kerachian, Reza
AU - Nikoo, Mohammad Reza
N1 - Publisher Copyright:
© 2018 Elsevier B.V.
PY - 2018/3
Y1 - 2018/3
N2 - This paper presents a new methodology for optimizing Water Quality Monitoring (WQM) networks of reservoirs and lakes using the concept of the value of information (VOI) and utilizing results of a calibrated numerical water quality simulation model. With reference to the value of information theory, water quality of every checkpoint with a specific prior probability differs in time. After analyzing water quality samples taken from potential monitoring points, the posterior probabilities are updated using the Baye's theorem, and VOI of the samples is calculated. In the next step, the stations with maximum VOI is selected as optimal stations. This process is repeated for each sampling interval to obtain optimal monitoring network locations for each interval. The results of the proposed VOI-based methodology is compared with those obtained using an entropy theoretic approach. As the results of the two methodologies would be partially different, in the next step, the results are combined using a weighting method. Finally, the optimal sampling interval and location of WQM stations are chosen using the Evidential Reasoning (ER) decision making method. The efficiency and applicability of the methodology are evaluated using available water quantity and quality data of the Karkheh Reservoir in the southwestern part of Iran.
AB - This paper presents a new methodology for optimizing Water Quality Monitoring (WQM) networks of reservoirs and lakes using the concept of the value of information (VOI) and utilizing results of a calibrated numerical water quality simulation model. With reference to the value of information theory, water quality of every checkpoint with a specific prior probability differs in time. After analyzing water quality samples taken from potential monitoring points, the posterior probabilities are updated using the Baye's theorem, and VOI of the samples is calculated. In the next step, the stations with maximum VOI is selected as optimal stations. This process is repeated for each sampling interval to obtain optimal monitoring network locations for each interval. The results of the proposed VOI-based methodology is compared with those obtained using an entropy theoretic approach. As the results of the two methodologies would be partially different, in the next step, the results are combined using a weighting method. Finally, the optimal sampling interval and location of WQM stations are chosen using the Evidential Reasoning (ER) decision making method. The efficiency and applicability of the methodology are evaluated using available water quantity and quality data of the Karkheh Reservoir in the southwestern part of Iran.
KW - Entropy theory
KW - Evidential Reasoning method
KW - Reservoir
KW - Spatio-temporal optimization
KW - Value of information theory
KW - Water quality monitoring network
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U2 - 10.1016/j.jhydrol.2018.01.011
DO - 10.1016/j.jhydrol.2018.01.011
M3 - Article
AN - SCOPUS:85041401834
SN - 0022-1694
VL - 558
SP - 328
EP - 340
JO - Journal of Hydrology
JF - Journal of Hydrology
ER -