scholarly journals INVESTIGATIONS OF WATER QUALITY FLUCTUATION OF THE INNERMOST PART OF THE ARIAKE SEA BY FIELD OBSERVATION

2004 ◽  
Vol 48 ◽  
pp. 1279-1284
Author(s):  
Koichiro OHGUSHI ◽  
Satoru SAKEMI ◽  
Hiroyuki ARAKI
Keyword(s):  
Water Quality ◽  
Field Observation ◽  
Ariake Sea ◽  
Innermost Part

scholarly journals DENSITY STRATIFICATION AND WATER QUALITY IN A VERTICAL SECTION ALONG TAKESAKI-OHMUTA LINE OF ARIAKE SEA

2004 ◽  
Vol 48 ◽  
pp. 1273-1278
Author(s):  
Takahisa TOKUNAGA ◽  
Ken-ichi UZAKI ◽  
Nobuhiro MATSUNAGA ◽  
Toshimitsu KOMATSU
Keyword(s):  
Water Quality ◽  
Vertical Section ◽  
Density Stratification ◽  
Ariake Sea

Application of Data Fusion for Uncertainty and Sensitivity Analysis of Water Quality in the Shenandoah River

2019 ◽  
pp. 1383-1410
Author(s):  
Mbongowo Joseph Mbuh
Keyword(s):  
Water Quality ◽  
Data Fusion ◽  
Field Observation ◽  
Water Quality Modeling ◽  
Coefficient Of Determination ◽  
Water Quality Simulation ◽  
Quality Modeling ◽  
Invaluable Tool ◽  
Using Data ◽  
Very High

This article is aimed at demonstrating the feasibility of combining water quality observations with modeling using data fusion techniques for efficient nutrients monitoring in the Shenandoah River (SR). It explores the hypothesis; “Sensitivity and uncertainty from water quality modeling and field observation can be improved through data fusion for a better prediction of water quality.” It models water quality using water quality simulation programs and combines the results with field observation, using a Kalman filter (KF). The results show that the analysis can be improved by using more observations in watersheds where minor variations to the analysis result in large differences in the subsequent forecast. Analyses also show that while data fusion was an invaluable tool to reduce uncertainty, an improvement in the temporal scales would also enhance results and reduce uncertainty. To examine how changes in the field observation affects the final KF analysis, the fusion and lab analysis cross-validation showed some improvement in the results with a very high coefficient of determination.

scholarly journals FIELD OBSERVATION ON WATER QUALITY AND PRODUCTION OF ATTACHED ALGAE AT RIFFLES AND POOLS

1999 ◽  
Vol 43 ◽  
pp. 1037-1042
Author(s):  
Syunsuke IKEDA ◽  
Yuji TODA ◽  
Yoshihisa AKAMATSU
Keyword(s):  
Water Quality ◽  
Field Observation ◽  
Attached Algae

scholarly journals Characteristics of environmental changing in Ariake Sea based on surveying the data of water quality and bottom sediment

2008 ◽  
Vol 55 ◽  
pp. 1021-1025
Author(s):  
Yoshihiro SONODA ◽  
Kiyoshi TAKIKAWA ◽  
Taketomi TOKONAMI ◽  
Isao SODA ◽  
Takashi SAITO
Keyword(s):  
Water Quality ◽  
Bottom Sediment ◽  
Ariake Sea

Application of Data Fusion for Uncertainty and Sensitivity Analysis of Water Quality in the Shenandoah River

2018 ◽  
Vol 9 (3) ◽  
pp. 31-54
Author(s):  
Mbongowo Joseph Mbuh
Keyword(s):  
Water Quality ◽  
Data Fusion ◽  
Field Observation ◽  
Water Quality Modeling ◽  
Coefficient Of Determination ◽  
Water Quality Simulation ◽  
Quality Modeling ◽  
Invaluable Tool ◽  
Using Data ◽  
Very High

This article is aimed at demonstrating the feasibility of combining water quality observations with modeling using data fusion techniques for efficient nutrients monitoring in the Shenandoah River (SR). It explores the hypothesis; “Sensitivity and uncertainty from water quality modeling and field observation can be improved through data fusion for a better prediction of water quality.” It models water quality using water quality simulation programs and combines the results with field observation, using a Kalman filter (KF). The results show that the analysis can be improved by using more observations in watersheds where minor variations to the analysis result in large differences in the subsequent forecast. Analyses also show that while data fusion was an invaluable tool to reduce uncertainty, an improvement in the temporal scales would also enhance results and reduce uncertainty. To examine how changes in the field observation affects the final KF analysis, the fusion and lab analysis cross-validation showed some improvement in the results with a very high coefficient of determination.

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