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Multiple Scales of Suspended Sediment Dynamics in a Complex Geometry Estuary

為了解決Beach nourishment的問題，作者Achete, Fernanda Minikowski 這樣論述：

Many estuaries are located in urbanized, highly engineered environments. Cohesive sediment plays an important role due to its link with estuarine health and ecology. An important ecological parameter is the suspended sediment concentration (SSC) translated into turbidity levels and sediment budget.

This study contributes to investigate and forecast turbidity levels and sediment budget variability at San Francisco Bay-Delta system at a variety of spatial and temporal scales applying a flexible mesh process-based model (Delft3D FM). It is possible to have a robust sediment model, which reproduce

s 90% of the yearly data derived sediment budget, with simple model settings, like applying one mud fraction and a simple bottom sediment distribution. This finding opens the horizon for modeling less monitored estuaries.Comparing two case studies, i.e. the Sacramento-San Joaquin Delta and Alviso Sl

ough, a classification for estuaries regarding the main sediment dynamic forcing is proposed: event-driven estuary (Delta) and tide-driven estuary (Alviso Slough). In the event-driven estuaries, the rivers are the main sediment source and the tides have minor impact in the net sediment transport. In

the tide-driven estuaries, the main sediment source is the bottom sediment and the tide asymmetry defines the net sediment transport.This research also makes advances in connecting different scientific fields and developing a managerial tool to support decision making. It provides the basis to a ch

ain of models, which goes from the hydrodynamics, to suspended sediment, to phytoplankton, to fish, clams and marshes. Fernanda Minikowski Achete was born in Rio de Janeiro, Brazil, on 6 September 1986. She obtained her B.Sc. in Oceanography in 2008 at the Faculty of Oceanography, State Universit

y in Rio de Janeiro, Brazil. In 2011, she obtained her MSc in Coastal Engineering and Maritime Management (CoMEM) from an Erasmus Mundus partnership between the Technical University Delft in The Netherlands, the Norwegian University of Science and Technology in Norway and the Polytechnic University

of Catalonia in Spain. The thesis, ＂Ameland Bornrif: a case study for the sand engine＂, covered beach morphodynamics, and investigated the evolution of the mega nourishment, named the San Engine. The research was funded by Deltares, as part of the Building with Nature project. In October 2011, Ferna

nda Achete continued her PhD research at the Department of Water Science and Engineering at UNESCO-IHE, TU Delft in The Netherlands. Her research was part the project Computational Assessments of Scenarios of Change for Delta Ecosystem (CASCaDE II), a United State Geological Survey (USGS) multidisci

plinary project funded by Calfed and partially funded by the Brazilian Government via Capes agency.

Multiple Scales of Suspended Sediment Dynamics in a Complex Geometry Estuary

為了解決Beach nourishment的問題，作者Achete, Fernanda Minikowski 這樣論述：

Many estuaries are located in urbanized, highly engineered environments. Cohesive sediment plays an important role due to its link with estuarine health and ecology. An important ecological parameter is the suspended sediment concentration (SSC) translated into turbidity levels and sediment budget.

This study contributes to investigate and forecast turbidity levels and sediment budget variability at San Francisco Bay-Delta system at a variety of spatial and temporal scales applying a flexible mesh process-based model (Delft3D FM). It is possible to have a robust sediment model, which reproduce

s 90% of the yearly data derived sediment budget, with simple model settings, like applying one mud fraction and a simple bottom sediment distribution. This finding opens the horizon for modeling less monitored estuaries.Comparing two case studies, i.e. the Sacramento-San Joaquin Delta and Alviso Sl

ough, a classification for estuaries regarding the main sediment dynamic forcing is proposed: event-driven estuary (Delta) and tide-driven estuary (Alviso Slough). In the event-driven estuaries, the rivers are the main sediment source and the tides have minor impact in the net sediment transport. In

the tide-driven estuaries, the main sediment source is the bottom sediment and the tide asymmetry defines the net sediment transport.This research also makes advances in connecting different scientific fields and developing a managerial tool to support decision making. It provides the basis to a ch

ain of models, which goes from the hydrodynamics, to suspended sediment, to phytoplankton, to fish, clams and marshes. Fernanda Minikowski Achete was born in Rio de Janeiro, Brazil, on 6 September 1986. She obtained her B.Sc. in Oceanography in 2008 at the Faculty of Oceanography, State Universit

y in Rio de Janeiro, Brazil. In 2011, she obtained her MSc in Coastal Engineering and Maritime Management (CoMEM) from an Erasmus Mundus partnership between the Technical University Delft in The Netherlands, the Norwegian University of Science and Technology in Norway and the Polytechnic University

of Catalonia in Spain. The thesis, ＂Ameland Bornrif: a case study for the sand engine,＂ covered beach morphodynamics, and investigated the evolution of the mega nourishment, named the San Engine. The research was funded by Deltares, as part of the Building with Nature project. In October 2011, Ferna

nda Achete continued her PhD research at the Department of Water Science and Engineering at UNESCO-IHE, TU Delft in The Netherlands. Her research was part the project Computational Assessments of Scenarios of Change for Delta Ecosystem (CASCaDE II), a United State Geological Survey (USGS) multidisci

plinary project funded by Calfed and partially funded by the Brazilian Government via Capes agency.

基於機器學習於Coastsat之海陸分割 :以旗津海岸為例

為了解決Beach nourishment的問題，作者孫尉豪 這樣論述：

在近年來關於沿海工程研究中，Coastsat海岸線偵測系統受到廣泛應用，此系統透過衛星影像分析海岸線變化，而在衛星影像中，海陸分割指將沿岸影像準確地分成海洋及陸地區域，且海陸分割信息對於海岸線偵測有重要意義，但是在此系統中並未說明使用類神經網路(ANN)進行海陸分割，且在此模型對於氣候干擾的衛星影像無法有準確的海陸分割效果，故本研究透過實驗分析進行驗證並探討各機器學習模型於Coastsat系統中分類效能與差異。 本論文的研究方法分別使用各分類器包含類神經網路(ANN)、決策樹( DTC)、非線性支持向量機 (SVM)、k-近鄰演算法 (KNN) 、線性SVM加隨機梯度下降(SGD)優化器以

及深度學習模型HED-Unet於衛星影像進行訓練；並在方法中分為三個部分，首先從Google Earth Engine 獲取衛星影像進行資料收集，接著透過框取目標類別進行資料標註，最後透過模型訓練獲取影像中海洋與陸地的特徵並進行分類以獲得海陸分割結果。 本論文在實驗設計中以旗津海岸為目標，透過不同評估指標包含accuracies、 F1 scores 與precision-recall curves 來驗證各分類器之效能，也透過使用不同優化器與損失函數於HED-Unet模型中，驗證Adam+二值交叉熵(BCE)有最佳表現。實驗結果列出Coastsat 於各個機器學習模型之準確率並驗證ANN在

各分類器中有最佳的準確率；實驗結果也顯示深度學習模型HED-Unet可有效改善海陸分割於雲霧、海浪等大氣因素干擾。