Application of Machine Learning Techniques to Forecast Harmful Algal Blooms in Gulf of Mexico

The Harmful Algal Blooms (HABs) forecast is crucial for the mitigation of health hazards and to inform actions for the protection of ecosystems and fisheries in the Gulf of Mexico (GoM). For the sake of simplicity of our application we assume ocean color satellite imagery from the National Oceanic and Atmospheric Administration as a proxy for HABs. In this study we use a deep neural network trained on the 2-Dimensional time series proxy data to provide a forecast of the HABs’ manifestations in the GoM.Our approach analyzes between both spatial and temporal features simultaneously. In addition, the network also helps to fill in the gaps of the time series data along the way. We use Long Short Term Memory (LSTM) layers to learn the underlying trends in the time series data and Convolutional layers to decode the spatial trends in the 2-Dimensional gridded data. Our unique contribution is an iterative, bidirectional training scheme, where we train two models: for forward and backward prediction. The intention is that if there is a functional dependence within the data in the forward time direction, then such a dependence may also exist in the backward time direction, which may be leveraged for predictions to fill the gaps in the data. We train each model to predict the next data point in their respective time-direction, based on an LSTM recurrence over the “lookback” data points. Since there are missing cells in the grid within each data point, we use a custom loss function that ignores prediction errors on missing cells. Thus the loss function critiques the models based on known cells alone, while the models act with (forward/backward) predictions that are spatiotemporally consistent across both missing and visible cells, thus updating the input training data, and consequently changing the object of critique. This actor-critic training scheme progresses iteratively, leading to the iterative improvement of the models/actors. Several models are developed with varying combinations of convolutional layers and max pooling layers to enable the model to learn the spatial and temporal trends within the month-long training data. The most effective model performs reasonably well with prediction of chlorophyll intensities

The geometry of flow: Advancing predictions of river geometry with multi-model machine learning

Hydraulic geometry parameters describing river hydrogeomorphic is important for flood forecasting. Although well-established, power-law hydraulic geometry curves have been widely used to understand riverine systems and mapping flooding inundation worldwide for the past 70 years, we have become increasingly aware of the limitations of these approaches. In the present study, we have moved beyond these traditional power-law relationships for river geometry, testing the ability of machine-learning models to provide improved predictions of river width and depth. For this work, we have used an unprecedentedly large river measurement dataset (HYDRoSWOT) as well as a suite of watershed predictor data to develop novel data-driven approaches to better estimate river geometries over the contiguous United States (CONUS). Our Random Forest, XGBoost, and neural network models out-performed the traditional, regionalized power law-based hydraulic geometry equations for both width and depth, providing R-squared values of as high as 0.75 for width and as high as 0.67 for depth, compared with R-squared values of 0.57 for width and 0.18 for depth from the regional hydraulic geometry equations. Our results also show diverse performance outcomes across stream orders and geographical regions for the different machine-learning models, demonstrating the value of using multi-model approaches to maximize the predictability of river geometry. The developed models have been used to create the newly publicly available STREAM-geo dataset, which provides river width, depth, width/depth ratio, and river and stream surface area (%RSSA) for nearly 2.7 million NHDPlus stream reaches across the rivers and streams across the contiguous US.Comment: 30 pages, 10 figure

Using Spatial Validity and Uncertainty Metrics to Determine the Relative Suitability of Alternative Suites of Oceanographic Data for Seabed Biotope Prediction. A Case Study from the Barents Sea, Norway

The use of habitat distribution models (HDMs) has become common in benthic habitat mapping for combining limited seabed observations with full-coverage environmental data to produce classified maps showing predicted habitat distribution for an entire study area. However, relatively few HDMs include oceanographic predictors, or present spatial validity or uncertainty analyses to support the classified predictions. Without reference studies it can be challenging to assess which type of oceanographic model data should be used, or developed, for this purpose. In this study, we compare biotope maps built using predictor variable suites from three different oceanographic models with differing levels of detail on near-bottom conditions. These results are compared with a baseline model without oceanographic predictors. We use associated spatial validity and uncertainty analyses to assess which oceanographic data may be best suited to biotope mapping. Our results show how spatial validity and uncertainty metrics capture differences between HDM outputs which are otherwise not apparent from standard non-spatial accuracy assessments or the classified maps themselves. We conclude that biotope HDMs incorporating high-resolution, preferably bottom-optimised, oceanography data can best minimise spatial uncertainty and maximise spatial validity. Furthermore, our results suggest that incorporating coarser oceanographic data may lead to more uncertainty than omitting such data.publishedVersio

Optical Satellite Remote Sensing of the Coastal Zone Environment — An Overview

Optical remote-sensing data are a powerful source of information for monitoring the coastal environment. Due to the high complexity of coastal environments, where different natural and anthropogenic phenomenon interact, the selection of the most appropriate sensor(s) is related to the applications required, and the different types of resolutions available (spatial, spectral, radiometric, and temporal) need to be considered. The development of specific techniques and tools based on the processing of optical satellite images makes possible the production of information useful for coastal environment management, without any destructive impacts. This chapter will highlight different subjects related to coastal environments: shoreline change detection, ocean color, water quality, river plumes, coral reef, alga bloom, bathymetry, wetland mapping, and coastal hazards/vulnerability. The main objective of this chapter is not an exhaustive description of the image processing methods/algorithms employed in coastal environmental studies, but focus in the range of applications available. Several limitations were identified. The major challenge still is to have remote-sensing techniques adopted as a routine tool in assessment of change in the coastal zone. Continuing research is required into the techniques employed for assessing change in the coastal environment

Ocean forecasting for wave energy production

There are a variety of requirements for future forecasts in relation to optimizing the production of wave energy. Daily forecasts are required to plan maintenance activities and allow power producers to accurately bid on wholesale energy markets, hourly forecasts are needed to warn of impending inclement conditions, possibly placing devices in survival mode, while wave-by-wave forecasts are required to optimize the real-time loading of the device so that maximum power is extracted from the waves over all sea conditions. In addition, related hindcasts over a long time scale may be performed to assess the power production capability of a specific wave site. This paper addresses the full spectrum of the aforementioned wave modeling activities, covering the variety of time scales and detailing modeling methods appropriate to the various time scales, and the causal inputs, where appropriate, which drive these models. Some models are based on a physical description of the system, including bathymetry, for example (e.g., in assessing power production capability), while others simply use measured data to form time series models (e.g., in wave-to-wave forecasting). The paper describes each of the wave forecasting problem domains, details appropriate model structures and how those models are parameterized, and also offers a number of case studies to illustrate each modeling methodology

Developing a remote sensing system based on X-band radar technology for coastal morphodynamics study

New data processing techniques are proposed for the assessment of scopes and limitations from radar-derived sea state parameters, coastline evolution and water depth estimates. Most of the raised research is focused on Colombian Caribbean coast and the Western Mediterranean Sea. First, a novel procedure to mitigate shadowing in radar images is proposed. The method compensates distortions introduced by the radar acquisition process and the power decay of the radar signal along range applying image enhancement techniques through a couple of pre-processing steps based on filtering and interpolation. Results reveal that the proposed methodology reproduces with high accuracy the sea state parameters in nearshore areas. The improvement resulting from the proposed method is assessed in a coral reef barrier, introducing a completely novel use for X-Band radar in coastal environments. So far, wave energy dissipation on a coral reef barrier has been studied by a few in-situ sensors placed in a straight line, perpendicular to the coastline, but never been described using marine radars. In this context, marine radar images are used to describe prominent features of coral reefs, including the delineation of reef morphological structure, wave energy dissipation and wave transformation processes in the lagoon of San Andres Island barrier-reef system. Results show that reef attenuates incident waves by approximately 75% due to both frictional and wave breaking dissipation, with an equivalent bottom roughness of 0.20 m and a wave friction factor of 0.18. These parameters are comparable with estimates reported in other shallow coral reef lagoons as well as at meadow canopies, obtained using in-situ measurements of wave parameters.DoctoradoDoctor en Ingeniería Eléctrica y Electrónic

Tsunami risk management for crustal earthquakes and non-seismic sources in Italy

publishedVersio

Evaluation of Regional-Scale River Depth Simulations Using Various Routing Schemes within a Hydrometeorological Modeling Framework for the Preparation of the SWOT Mission

The Surface Water and Ocean Topography (SWOT) mission will provide free water surface elevations, slopes, and river widths for rivers wider than 50 m. Models must be prepared to use this new finescale information by explicitly simulating the link between runoff and the river channel hydraulics. This study assesses one regional hydrometeorological model’s ability to simulate river depths. The Garonne catchment in southwestern France (56 000 km2) has been chosen for the availability of operational gauges in the river network and finescale hydraulic models over two reaches of the river. Several routing schemes, ranging from the simple Muskingum method to time-variable parameter kinematic and diffusive waves schemes, are tested. The results show that the variable flow velocity schemes are advantageous for discharge computations when compared to the original Muskingum routing method. Additionally, comparisons between river depth computations and in situ observations in the downstream Garonne River led to root-mean-square errors of 50–60 cm in the improved Muskingum method and 40–50 cm in the kinematic–diffusive wave method. The results also highlight SWOT’s potential to improve the characterization of hydrological processes for subbasins larger than 10 000 km2, the importance of an accurate digital elevation model, and the need for spatially varying hydraulic parameters

Storm Tide and Wave Simulations and Assessment

In this Special Issue, seven high-quality papers covering the application and development of many high-end techniques for studies on storm tides, surges, and waves have been published, for instance, the employment of an artificial neural network for predicting coastal freak waves [1]; a reproduction of super typhoon-created extreme waves [2]; a numerical analysis of nonlinear interactions for storm waves, tides, and currents [3]; wave simulation for an island using a circulation–wave coupled model [4]; an analysis of typhoon-induced waves along typhoon tracks in the western North Pacific Ocean [5]; an understanding of how a storm surge prevents or severely restricts aeolian supply [6]; and an investigation of coastal settlements and an assessment of their vulnerability [7]

Fluvial Processes in Motion: Measuring Bank Erosion and Suspended Sediment Flux using Advanced Geomatic Methods and Machine Learning

Excessive erosion and fine sediment delivery to river corridors and receiving waters degrade aquatic habitat, add to nutrient loading, and impact infrastructure. Understanding the sources and movement of sediment within watersheds is critical for assessing ecosystem health and developing management plans to protect natural and human systems. As our changing climate continues to cause shifts in hydrological regimes (e.g., increased precipitation and streamflow in the northeast U.S.), the development of tools to better understand sediment dynamics takes on even greater importance. In this research, advanced geomatics and machine learning are applied to improve the (1) monitoring of streambank erosion, (2) understanding of event sediment dynamics, and (3) prediction of sediment loading using meteorological data as inputs. Streambank movement is an integral part of geomorphic changes along river corridors and also a significant source of fine sediment to receiving waters. Advances in unmanned aircraft systems (UAS) and photogrammetry provide opportunities for rapid and economical quantification of streambank erosion and deposition at variable scales. We assess the performance of UAS-based photogrammetry to capture streambank topography and quantify bank movement. UAS data were compared to terrestrial laser scanner (TLS) and GPS surveying from Vermont streambank sites that featured a variety of bank conditions and vegetation. Cross-sectional analysis of UAS and TLS data revealed that the UAS reliably captured the bank surface and was able to quantify the net change in bank area where movement occurred. Although it was necessary to consider overhanging bank profiles and vegetation, UAS-based photogrammetry showed significant promise for capturing bank topography and movement at fine resolutions in a flexible and efficient manner. This study also used a new machine-learning tool to improve the analysis of sediment dynamics using three years of high-resolution suspended sediment data collected in the Mad River watershed. A restricted Boltzmann machine (RBM), a type of artificial neural network (ANN), was used to classify individual storm events based on the visual hysteresis patterns present in the suspended sediment-discharge data. The work expanded the classification scheme typically used for hysteresis analysis. The results provided insights into the connectivity and sources of sediment within the Mad River watershed and its tributaries. A recurrent counterpropagation network (rCPN) was also developed to predict suspended sediment discharge at ungauged locations using only local meteorological data as inputs. The rCPN captured the nonlinear relationships between meteorological data and suspended sediment discharge, and outperformed the traditional sediment rating curve approach. The combination of machine-learning tools for analyzing storm-event dynamics and estimating loading at ungauged locations in a river network provides a robust method for estimating sediment production from catchments that informs watershed management