scholarly journals ON THE ESTIMATION METHOD FOR WATER QUALITY IN THE INNERMOST PART OF THE ARIAKE SEA BY USING TERRA-ASTER IMAGES

2006 ◽  
Vol 50 ◽  
pp. 1405-1410
Author(s):  
Koichiro OHGUSHI ◽  
Hiroyuki ARAKI
Keyword(s):  
Water Quality ◽  
Estimation Method ◽  
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

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

scholarly journals Water Quality Indicator Interval Prediction in Wastewater Treatment Process Based on the Improved BES-LSSVM Algorithm

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 422
Author(s):  
Meng Zhou ◽  
Yinyue Zhang ◽  
Jing Wang ◽  
Yuntao Shi ◽  
Vicenç Puig
Keyword(s):  
Water Quality ◽  
Wastewater Treatment ◽  
Wastewater Treatment Plant ◽  
Treatment Plant ◽  
Estimation Method ◽  
Bald Eagle ◽  
Computational Time ◽  
Support Vector ◽  
Effluent Water ◽  
Interval Prediction

This paper proposes a novel interval prediction method for effluent water quality indicators (including biochemical oxygen demand (BOD) and ammonia nitrogen (NH3-N)), which are key performance indices in the water quality monitoring and control of a wastewater treatment plant. Firstly, the effluent data regarding BOD/NH3-N and their necessary auxiliary variables are collected. After some basic data pre-processing techniques, the key indicators with high correlation degrees of BOD and NH3-N are analyzed and selected based on a gray correlation analysis algorithm. Next, an improved IBES-LSSVM algorithm is designed to predict the BOD/NH3-N effluent data of a wastewater treatment plant. This algorithm relies on an improved bald eagle search (IBES) optimization algorithm that is used to find the optimal parameters of least squares support vector machine (LSSVM). Then, an interval estimation method is used to analyze the uncertainty of the optimized LSSVM model. Finally, the experimental results demonstrate that the proposed approach can obtain high prediction accuracy, with reduced computational time and an easy calculation process, in predicting effluent water quality parameters compared with other existing algorithms.

scholarly journals A STUDY ON FORMATION PROCESS AND ESTIMATION METHOD OF WATER QUALITY HYDROGRAPH IN A SMALL BASIN

2006 ◽  
Vol 50 ◽  
pp. 349-354
Author(s):  
Shuichi KURE ◽  
Kinsou Ryuu ◽  
Ryou EBANA ◽  
Tadashi YAMADA
Keyword(s):  
Water Quality ◽  
Formation Process ◽  
Estimation Method ◽  
Small Basin
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