Since coastal waters are one of the most vulnerable marine systems to environmental pollution, it is very important to operationally monitor coastal water quality. This study attempts to estimate t...

Machine learning approaches to coastal water quality monitoring using GOCI satellite data

Water management practice has benefited from the development of model-driven Decision Support Systems (DSS), and in particular those that combine simulation with single or multiple-objective optimisation tools. However, there are many performance, acceptance and adoption problems with these decision support tools caused mainly by misunderstandings between the communities of system developers and users. This paper presents a general-purpose decision-support system generator, GANetXL, for developing specific applications that require multiobjective optimisation of spreadsheet-based models. The system is developed as an Excel add-in that provides easy access to evolutionary multiobjective optimisation algorithms to non-specialists by incorporating an intuitive interactive graphical user interface that allows easy creation of specific decision-support applications. GANetXL's utility is demonstrated on two examples from water engineering practice, a simple water supply reservoir operation model with two objectives and a large combinatorial optimisation problem of pump scheduling in water distribution systems. The two examples show how GANetXL goes a long way toward closing the gap between the achievements in optimisation technology and the successful use of DSS in practice.

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A DSS generator for multiobjective optimisation of spreadsheet-based models

Machine learning for durability and service-life assessment of reinforced concrete structures: Recent advances and future directions

Forecasting and early warning systems are important investments to protect lives, properties, and livelihood. While early warning systems are frequently used to predict the magnitude, location, and timing of potentially damaging events, these systems rarely provide impact estimates, such as the expected amount and distribution of physical damage, human consequences, disruption of services, or financial loss. Complementing early warning systems with impact forecasts has a twofold advantage: It would provide decision makers with richer information to take informed decisions about emergency measures and focus the attention of different disciplines on a common target. This would allow capitalizing on synergies between different disciplines and boosting the development of multihazard early warning systems. This review discusses the state of the art in impact forecasting for a wide range of natural hazards. We outline the added value of impact‐based warnings compared to hazard forecasting for the emergency phase, indicate challenges and pitfalls, and synthesize the review results across hazard types most relevant for Europe. Plain Language Summary Forecasting and early warning systems are important investments to protect lives, properties and livelihood. While such systems are frequently used to predict the magnitude, location, and timing of potentially damaging events, they rarely provide impact estimates, such as the expected physical damage, human consequences, disruption of services, or financial loss. Extending hazard forecast systems to include impact estimates promises many benefits for the emergency phase, for instance, for organizing evacuations. We review and compare the state of the art of impact forecasting across a wide range of natural hazards and outline opportunities and key challenges for research and development of impact forecasting.

https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2020RG000704

Impact Forecasting to Support Emergency Management of Natural Hazards

Machine learning based marine water quality prediction for coastal hydro-environment management

We use tsunami waveforms recorded on a dense array of seafloor pressure gauges offshore Oregon and California from the 2012 Haida Gwaii, Canada, earthquake to simulate the performance of two different real-time tsunami-forecasting methods. In the first method, the tsunami source is first estimated by inversion of recorded tsunami waveforms. In the second method, the array data are assimilated to reproduce tsunami wavefields. These estimates can be used for forecasting tsunami on the coast. The dense seafloor array provides critical data for both methods to produce timeliness (>30 min lead time) and accuracy in both timing and amplitude (>94% confidence) tsunami forecasts. Real-time tsunami data on dense arrays and data assimilation can be tested as a possible new generation tsunami warning system.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2016GL068368

Tsunami data assimilation of Cascadia seafloor pressure gauge records from the 2012 Haida Gwaii earthquake

Hybrid ANN–GA model for predicting turbidity and chlorophyll-a concentrations

17 The 2017 Tehuantepec earthquake (Mw 8.2) was the first great normal fault event 18 ever instrumentally recorded to occur in the Middle America Trench. The earthquake 19 generated a tsunami with an amplitude of 1.8 m (height=3.5 m) in Puerto Chiapas, Mexico. 20 Tsunami waveforms recorded at coastal tide gauges and offshore buoy stations were used to 21 estimate the optimum sea surface displacement without assuming any fault. Our optimum sea 22 surface displacement model indicated that the maximum uplift of 0.5 m is located near the 23 trench and the maximum subsidence of 0.8 m on the coastal side near the epicenter. We then 24 estimated the fault slip distribution that can best explain the optimum sea surface 25 displacement assuming ten different fault geometries. The best model suggests that a 26 compact region of large slip (3 – 6 m) extends from a depth of 30 km to 90 km, centered at a 27 depth of 60 km. 28 29

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2017GL076070

Optimum Sea Surface Displacement and Fault Slip Distribution of the 2017 Tehuantepec Earthquake (M w 8.2) in Mexico Estimated From Tsunami Waveforms

The Eastern Mediterranean Basin (EMB) is under the threat of tsunami events triggered by various causes including earthquakes and landslides. We propose a deployment of Offshore Bottom Pressure Gau...

A Tsunami Warning System Based on Offshore Bottom Pressure Gauges and Data Assimilation for Crete Island in the Eastern Mediterranean Basin

Recently, there are numerous tsunami observation networks deployed in several major tsunamigenic regions. However, guidance on where to optimally place the measurement devices is limited. This study presents a methodological approach to select strategic observation locations for the purpose of tsunami source characterizations, particularly in terms of the fault slip distribution. Initially, we identify favorable locations and determine the initial number of observations. These locations are selected based on extrema of empirical orthogonal function (EOF) spatial modes. To further improve the accuracy, we apply an optimization algorithm called a mesh adaptive direct search to remove redundant measurement locations from the EOF-generated points. We test the proposed approach using multiple hypothetical tsunami sources around the Nankai Trough, Japan. The results suggest that the optimized observation points can produce more accurate fault slip estimates with considerably less number of observations compared to the existing tsunami observation networks.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2017GL075791

Iyan E. Mulia

Papers

Tsunami data assimilation of Cascadia seafloor pressure gauge records from the 2012 Haida Gwaii earthquake

Hybrid ANN–GA model for predicting turbidity and chlorophyll-a concentrations

Optimum Sea Surface Displacement and Fault Slip Distribution of the 2017 Tehuantepec Earthquake (M w 8.2) in Mexico Estimated From Tsunami Waveforms

A Tsunami Warning System Based on Offshore Bottom Pressure Gauges and Data Assimilation for Crete Island in the Eastern Mediterranean Basin

Optimal Design for Placements of Tsunami Observing Systems to Accurately Characterize the Inducing Earthquake