Data-driven Modeling of Coastal Water Quality using the Bayesian Method for Coastal Management

The decrease of coastal-water quality in the Surabaya coastal region can be recognized from the conceentration of Total Suspended Sediment(TSS ) . As a result we need a system for monitoring sediment concentration in the coastal region of Surabaya which regularly measures TSS. The principle to model and monitor TSSconcentration using remote sensing methods is by the integration of Landsat-8OLI satellites image processing using some ofTSS-models then those are analyzed for looking its suitability with TSS value direcly measured in the field ( in-situ measurement). The TSS value modeled from all algorithms validated usingcorrelation analysis and linear regression . The result shows that TSS model with the highest correlation value is TSS algorithm by Budiman (2004)with r value 0.991. Hence this algorithm can be used to investigate TSS-distribution which represent the coastal water quality of Surabaya with TSS value between 75 mg/L to 125 mg/L.

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Using self-organizing map for coastal water quality classification: Towards a better understanding of patterns and processes

The Science of The Total Environment ◽

10.1016/j.scitotenv.2018.02.163 ◽

2018 ◽

Vol 628-629 ◽

pp. 1446-1459 ◽

Cited By ~ 27

Author(s):

Tao Li ◽

Guihua Sun ◽

Chupeng Yang ◽

Kai Liang ◽

Shengzhong Ma ◽

...

Keyword(s):

Water Quality ◽

Coastal Water ◽

Self Organizing Map ◽

Coastal Water Quality ◽

Quality Classification ◽

Water Quality Classification ◽

Patterns And Processes ◽

Self Organizing

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Use of co-created causal loop diagrams and fuzzy-cognitive scenario analysis for water quality management

10.5194/egusphere-egu21-5210 ◽

2021 ◽

Author(s):

Samaneh Seifollahi-Aghmiuni ◽

Zahra Kalantari ◽

Georgia Destouni

Keyword(s):

Water Quality ◽

Quality Management ◽

Baltic Sea ◽

Coastal Water ◽

Scenario Analysis ◽

Water Quality Management ◽

Coastal Water Quality ◽

Causal Loop ◽

Causal Loop Diagrams ◽

Quality Issues

<p>Current understanding is fragmented of the environmental, economic, and social processes involved in water quality issues. The fragmentation is particularly evident for coastal water quality, impacted both by local land catchment and larger-scale marine pressures and impacts. Research and policy so far has primarily addressed coastal water quality issues from either a land-based or a sea-based perspective, which does not support integrated management of the coupled land-coast-sea systems affecting coastal waters. For example, mitigation measures for improving the severe Baltic Sea eutrophication have mostly focused on land-based drivers, and not yet managed to sufficiently improve coastal or marine water quality. The strong human dimension involved in these water quality issues also highlights a need for participatory approaches to facilitate knowledge integration and drive synergistic strategic planning for sustainable management of coastal water quality. Considering the Swedish water management district of Northern Baltic Proper, including its main Norrstr&#246;m drainage basin and surrounding coastal catchment areas and waters, this study has used a participatory approach to evaluate various land-sea water quality interactions and associated management measures. A causal loop diagram has been co-created with different stakeholder groups, following a problem-oriented system thinking approach. This has been further used in fuzzy-cognitive scenario analysis to assess integrated land-coast-sea system behavior under changing human pressures and hydro-climatic conditions. Results show that synergy of several catchment measures is needed to improve coastal water quality locally, while cross-system/sector cooperation is also needed among all contributing national catchments to mitigate coastal eutrophication at the scale of the whole Baltic Sea. Furthermore, large-scale hydro-climatic changes and long-lived nutrient legacy sources also need to be accounted for in water quality management strategies and measures. System dynamics modelling, based on co-created causal loop diagrams and fuzzy-cognitive scenario analysis like those developed in this study, can support further quantification and analysis of the impacts of various mitigation strategies and measures on regional water quality problems and their possible sustainable solutions.</p>

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Energy policy and coastal water quality: An integrated energy, air and water quality modeling approach

The Science of The Total Environment ◽

10.1016/j.scitotenv.2021.151593 ◽

2021 ◽

pp. 151593

Author(s):

Jhih-Shyang Shih ◽

Charles T. Driscoll ◽

Dallas Burtraw ◽

Huizhong Shen ◽

Richard A. Smith ◽

...

Keyword(s):

Water Quality ◽

Energy Policy ◽

Coastal Water ◽

Water Quality Modeling ◽

Coastal Water Quality ◽

Modeling Approach ◽

Quality Modeling

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Student participation in a coastal water quality citizen science project and its contribution to the conceptual and procedural learning of chemistry

Chemistry Education Research and Practice ◽

10.1039/d1rp00190f ◽

2021 ◽

Author(s):

José Luís Araújo ◽

Carla Morais ◽

João Paiva

Keyword(s):

Water Quality ◽

Citizen Science ◽

Coastal Water ◽

Student Participation ◽

Scientific Research ◽

Procedural Learning ◽

Science Project ◽

Coastal Water Quality ◽

Chemistry Learning ◽

Citizen Science Project

The active participation of citizens in scientific research, through citizen science, has been proven successful. However, knowledge on the potential of citizen science within formal chemistry learning, at the conceptual...

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