| dc.description |
At first, I express my sincere acknowledgement to Almighty Allah who has given me
enough strength to complete the thesis work successfully. I am grateful to my thesis
supervisor Professor Dr Md Tauhid Ur Rahman, Department of Civil Engineering,
Military Institute of Science and Technology (MIST) for advising and guiding me in the
right track. I am also thankful to all faculty members and staffs of Civil Engineering
Department, MIST for expressing their continuous support in all aspect. My family
members have sacrificed their valuable time to complete my thesis work. I also sincerely
acknowledge to all of my senior officers and members of Bangladesh Army who all have
allowed and helped me to continue and complete this work successfully.
In the process of thesis work, Professor Dr. Mohammad Azmal Hossain Bhuiyan,
Department of Botany and his team of the same department have extended their
continuous support to use departmental testing facilities.
I am thankful to all the authors of research papers which I have consulted and referred in
my paper. All the information helped me a lot to write successfully. My wholehearted and
sincere effort was always to accumulate all the error free data. I will be grateful to all
readers for any kind of comments and suggestions. |
en_US |
| dc.description.abstract |
Eutrophication in Lakes are caused by the confluence of numerous nutrients (nitrogen,
phosphorus etc), temperature, sunshine, dissolved oxygen, land use/land cover, socioeconomic condition, and other biophysical processes. Excess nutrients lead to algal
blooms, which in turn cause fish mortality, abnormal lake conditions, and an overall
disruption of the aquatic ecosystem. Dhaka city's lakes were seen decline in water quality
because of excessive urbanization, uncontrolled sewerage disposal that led to
Eutrophication. This research set out to evaluate the current and prospective
eutrophication levels in five selected lakes in Dhaka city, Bangladesh namely Uttara,
Mirpur, Baridhara, Gulshan and Hatirjheel areas. Primary water quality data were
measured form collected water samples and secondary satellite imagery data were used to
evaluate Trophic State Index (TSI). Calculated TSI from water quality parameters (Chl-a
test & TN test) were used to compare and validate the TSI value computed from NDCI.
Subsequently, Artificial Neural Network (ANN) model of machine learning algorithm
was developed incorporating land use land cover (LULC) and different normalized
satellite indices using 1990, 2000 and 2010 imagery that predicted the NDCI of 2021.
Predicted NDCI of 2021 was validated using TSI calculated from primary data. ANN
model was trained to predict the NDCI value for 2030 and 2040 to evaluate the TSI.
From Carlson’s trophic state equation, TSI for Chl-a were calculated as 81.64, 82, 88.61,
88.44 and 90.99, and TSI for TN were found to be 98.53, 94.46, 102.19, 92.42 and 98.39
indicating “Hypertrophic” state for all five lakes. Calculated NDCI and corresponding
TSI value from satellite imagery showed 84 to 95 percent similarity with the field
measurement TSI values for 2021. Individual calculated TSI from NDCI of 1990 and
2000 imagery revealed “Supertrophic” and 2010 and 2021 imagery showed
“Hypertrophic” state for all five lakes indicating lakes are in declined condition. Trained
ANN model from 1990, 2000 and 2010 predicted TSI value of 2021 with 81 percent
accuracy. This calibrated model further predicted NDCI and corresponding TSI for 2030
as 82.85, 82.28, 84.11, 76.38 & 79.22 and for 2040 as 87, 85.85, 88.07, 79.92 & 84.56 for
all five lakes. These predicted results indicate all lakes are “Hypertrophic” condition with
higher TSI values. Therefore, these lake water quality monitoring and subsequently
proper management should be ensured immediately. |
en_US |
