Artificial neural networks in freight rate forecasting

Reliable freight rate forecasts are essential to stimulate ocean transportation and ensure stakeholder benefits in a highly volatile shipping market. However, compared to traditional time-series approaches, there are few studies using artificial intelligence techniques (e.g. artificial neural networks, ANNs) to forecast shipping freight rates, and fewer still incorporating forward freight agreement (FFA) information for freight rate forecasts. The aim of this paper is to examine the ability of FFAs to improve forecasting accuracy. We use two different dynamic ANN models, NARNET and NARXNET, and we compare their performance for 1, 2, 3 and 6 months ahead. The accuracy of the forecasting models is evaluated with the use of mean squared error (MSE), based on actual secondary data including historical Baltic Panamax Index (BPI) data (available online), and primary data on Baltic forward assessment (BFA) collected from the Baltic Exchange. The experimental results show that, in general, NARXNET outperforms NARNET in all forecast horizons, revealing the importance of the information contained in FFAs in improving forecasting accuracy. Our findings provide better forecasts and insights into the future movements of freight markets and help rationalise chartering decisions. © 2019, Springer Nature Limited

Assessing and Forecasting Chlorophyll Abundances in Minnesota Lake using Remote Sensing and Statistical Approaches

Harmful algae blooms (HABs) can negatively impact water quality, lake aesthetics, and can harm human and animal health. However, monitoring for HABs is rare in Minnesota. Detecting blooms which can vary spatially and may only be present briefly is challenging, so expanding monitoring in Minnesota would require the use of new and cost efficient technologies. Unmanned aerial vehicles (UAVs) were used for bloom mapping using RGB and near-infrared imagery. Real time monitoring was conducted in Bass Lake, in Faribault County, MN using trail cameras. Time series forecasting was conducted with high frequency chlorophyll-a data from a water quality sonde. Normalized Difference Vegetation Index (NDVI) was generally well correlated to chlorophyll-a measured by a sonde (R2 = 0.678 for all data from 5 flights, between 0.323-0.986 for individual flights), while Visible Water Residence Index (VWRI) showed a weaker and less consistent correlation with chlorophyll-a (R2 = 0.027 for all data from 5 flights, between 0.17-0.866 for individual flights). While RGB cameras (trail cameras or UAVs) were useful for visual inspection and spotting blooms, these results suggest that quantitative remote sensing of chlorophyll in Minnesota Lakes should use near-infrared cameras at a minimum. Univariate time series forecasts using sonde chlorophyll-a data were compared using classical (ARIMA, wavelet-ARIMA) and machine learning techniques (LSTM, wavelet-LSTM). Chlorophyll-a was positively correlated to temperature and precipitation, while negatively correlated to conductivity and turbidity. Peak summer chlorophyll concentrations also appeared to be positively correlated to recent precipitation totals. 10-day chlorophyll-a forecasts using univariate LSTM and ARIMA outperformed a multivariate forecast (using conductivity, turbidity, temperature, and precipitation as predictors), suggesting that lower cost monitoring setups (a single chlorophyll probe) may be practical. To assist in understanding meteorological factors impacting interannual variability of blooms in Bass Lake, the relationship between peak summer chlorophyll-a (from Sentinel-2 satellite imagery) and temperature and precipitation were analyzed at Bass Lake. The impact of meteorological factors on patterns in chlorophyll-a for lakes in the Western Corn Belt Plains (WCBP) was also examined, using Sentinel-2 imagery (imagery was available for 160 lakes in the WCBP during 2019 and 2020). Peak summer Chlorophyll-a (from Sentinel-2 imagery) at Bass Lake was positively correlated to 2-week precipitation totals, suggesting a potential role of precipitation induced nutrient loading in initiating blooms; a negative correlation between peak chlorophyll-a and 60-day precipitation totals also suggested that increased residence time during drier periods may be a driving factor as well. While a slight negative correlation between precipitation and peak summer chlorophyll-a was present in a larger scale analysis of 160 WCBP lakes, too many confounding factors were present to show the impact of precipitation on blooms at a broader scale in Minnesota

Three layer wavelet based modeling for river flow

All existing methods regarding time series forecasting have always been challenged by the continuous climatic change taking place in the world. These climatic changes influence many unpredictable indefinite factors. This alarming situation requires a robust forecasting method that could efficiently work with incomplete and multivariate data. Most of the existing methods tend to trap into local minimum or encounter over fitting problems that mostly lead to an inappropriate outcome. The complexity of data regarding time series forecasting does not allow any one single method to yield results suitable in all situations as claimed by most researchers. To deal with the problem, a technique that uses hybrid models has also been devised and tested. The applied hybrid methods did bring some improvement compared to the individual model performance. However, most of these available hybrid models exploit univariate data that requires huge historical data to achieve precise forecasting results. Therefore, this study introduces a new hybrid model based on three layered architecture: Least Square Support Vector Machine (LSSVM), Discrete Wavelet Transform (DWT), correlation (R) and Kernel Principle Components Analyses (KPCA). The three-staged architecture of the proposed hybrid model includes Wavelet-LSSVM and Wavelet-KPCA-LSSVM enabling the model to present itself as a well-established alternative application to predict the future of river flow. The proposed model has been applied to four different data sets of time series, taking into account different time series behavior and data scale. The performance of the proposed model is compared against the existing individual models and then a comparison is also drawn with the existing hybrid models. The results of WKPLSSVM obtained from Coefficient of Efficiency (CE) performance measuring methods confirmed that proposed model has encouraging data of 0.98%, 0.99%, 0.94% and 0.99% for Jhelum River, Chenab River, Bernam River and Tualang River, respectively. It is more robust for all datasets regardless of the sample sizes and data behavior. These results are further verified using diverse data sets in order to check the stability and adaptability. The results have demonstrated that the proposed hybrid model is a better alternative tool for time series forecasting. The proposed hybrid model proves to be one of the best available solutions considering the time series forecasting issues

Deep learning for the early detection of harmful algal blooms and improving water quality monitoring

Climate change will affect how water sources are managed and monitored. The frequency of algal blooms will increase with climate change as it presents favourable conditions for the reproduction of phytoplankton. During monitoring, possible sensory failures in monitoring systems result in partially filled data which may affect critical systems. Therefore, imputation becomes necessary to decrease error and increase data quality. This work investigates two issues in water quality data analysis: improving data quality and anomaly detection. It consists of three main topics: data imputation, early algal bloom detection using in-situ data and early algal bloom detection using multiple modalities.The data imputation problem is addressed by experimenting with various methods with a water quality dataset that includes four locations around the North Sea and the Irish Sea with different characteristics and high miss rates, testing model generalisability. A novel neural network architecture with self-attention is proposed in which imputation is done in a single pass, reducing execution time. The self-attention components increase the interpretability of the imputation process at each stage of the network, providing knowledge to domain experts.After data curation, algal activity is predicted using transformer networks, between 1 to 7 days ahead, and the importance of the input with regard to the output of the prediction model is explained using SHAP, aiming to explain model behaviour to domain experts which is overlooked in previous approaches. The prediction model improves bloom detection performance by 5% on average and the explanation summarizes the complex structure of the model to input-output relationships. Performance improvements on the initial unimodal bloom detection model are made by incorporating multiple modalities into the detection process which were only used for validation purposes previously. The problem of missing data is also tackled by using coordinated representations, replacing low quality in-situ data with satellite data and vice versa, instead of imputation which may result in biased results

Convergence of Intelligent Data Acquisition and Advanced Computing Systems

This book is a collection of published articles from the Sensors Special Issue on "Convergence of Intelligent Data Acquisition and Advanced Computing Systems". It includes extended versions of the conference contributions from the 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS’2019), Metz, France, as well as external contributions

Análisis del estado trófico mediante teledetección y datos “in situ” en la laguna de Paca, Jauja – Junín 2019

El objetivo principal de esta investigación es analizar el estado trófico mediante de teledetección y datos in situ en la laguna de Paca; para ello se evaluó el estado trófico o grado de eutrofización actual de la laguna de Paca, mediante 3 indicadores que son la clorofila A, fosfato y transparencia en días diferentes del año 2019 (24/04/2019 – 30/05/2019 y 14/11/2019). Para obtener los datos de campo se recolecto muestras de agua, ½ L para obtener fosfato y 1 L para obtener clorofila A en 7 puntos distribuidos alrededor de la laguna, mismas que fueron enviadas al laboratorio para conocer las concentraciones de cada indicador; la transparencia fue obtenida directamente en campo con el disco de Secchi en diferentes puntos de la laguna. Para la recolección de datos de teledetección se descargaron imágenes del Sentinel 1 en la página Copernicus, imágenes que inicialmente fueron calibradas en el programa SNAP ESA dando como resultado valores en decibelios de las bandas VV y VH, conocidos como valores en crudo del satélite, posteriormente se correlaciono los valores crudos del satélite con los valores de campo generando así una ecuación que permita la obtener una imagen con valores en unidades conocidas de cada indicador. Las formulas generadas permiten tener una línea base de un modelamiento del estado trófico de la Laguna de Paca, también permiten obtener datos sin la necesidad de tener que ir a campo. Esta investigación permite concluir que el estado trófico de la laguna de Paca Jauja Junín 2019 con los indicadores Cl A y PO4 va de eutrófico a hipertrófico, la transparencia es el único indicador que señala que la laguna de Paca se encuentra en estado Mesotrófico, teniendo en cuenta que la correlación entre las variables no fue mayor a 0.70, a diferencia de la Cl-A y el PO4 que tienen una correlaciones mayores o iguales a 0.90

Remote Sensing of Plant Biodiversity

This Open Access volume aims to methodologically improve our understanding of biodiversity by linking disciplines that incorporate remote sensing, and uniting data and perspectives in the fields of biology, landscape ecology, and geography. The book provides a framework for how biodiversity can be detected and evaluated—focusing particularly on plants—using proximal and remotely sensed hyperspectral data and other tools such as LiDAR. The volume, whose chapters bring together a large cross-section of the biodiversity community engaged in these methods, attempts to establish a common language across disciplines for understanding and implementing remote sensing of biodiversity across scales. The first part of the book offers a potential basis for remote detection of biodiversity. An overview of the nature of biodiversity is described, along with ways for determining traits of plant biodiversity through spectral analyses across spatial scales and linking spectral data to the tree of life. The second part details what can be detected spectrally and remotely. Specific instrumentation and technologies are described, as well as the technical challenges of detection and data synthesis, collection and processing. The third part discusses spatial resolution and integration across scales and ends with a vision for developing a global biodiversity monitoring system. Topics include spectral and functional variation across habitats and biomes, biodiversity variables for global scale assessment, and the prospects and pitfalls in remote sensing of biodiversity at the global scale

Remote Sensing of Plant Biodiversity

At last, here it is. For some time now, the world has needed a text providing both a new theoretical foundation and practical guidance on how to approach the challenge of biodiversity decline in the Anthropocene. This is a global challenge demanding global approaches to understand its scope and implications. Until recently, we have simply lacked the tools to do so. We are now entering an era in which we can realistically begin to understand and monitor the multidimensional phenomenon of biodiversity at a planetary scale. This era builds upon three centuries of scientific research on biodiversity at site to landscape levels, augmented over the past two decades by airborne research platforms carrying spectrometers, lidars, and radars for larger-scale observations. Emerging international networks of fine-grain in-situ biodiversity observations complemented by space-based sensors offering coarser-grain imagery—but global coverage—of ecosystem composition, function, and structure together provide the information necessary to monitor and track change in biodiversity globally. This book is a road map on how to observe and interpret terrestrial biodiversity across scales through plants—primary producers and the foundation of the trophic pyramid. It honors the fact that biodiversity exists across different dimensions, including both phylogenetic and functional. Then, it relates these aspects of biodiversity to another dimension, the spectral diversity captured by remote sensing instruments operating at scales from leaf to canopy to biome. The biodiversity community has needed a Rosetta Stone to translate between the language of satellite remote sensing and its resulting spectral diversity and the languages of those exploring the phylogenetic diversity and functional trait diversity of life on Earth. By assembling the vital translation, this volume has globalized our ability to track biodiversity state and change. Thus, a global problem meets a key component of the global solution. The editors have cleverly built the book in three parts. Part 1 addresses the theory behind the remote sensing of terrestrial plant biodiversity: why spectral diversity relates to plant functional traits and phylogenetic diversity. Starting with first principles, it connects plant biochemistry, physiology, and macroecology to remotely sensed spectra and explores the processes behind the patterns we observe. Examples from the field demonstrate the rising synthesis of multiple disciplines to create a new cross-spatial and spectral science of biodiversity. Part 2 discusses how to implement this evolving science. It focuses on the plethora of novel in-situ, airborne, and spaceborne Earth observation tools currently and soon to be available while also incorporating the ways of actually making biodiversity measurements with these tools. It includes instructions for organizing and conducting a field campaign. Throughout, there is a focus on the burgeoning field of imaging spectroscopy, which is revolutionizing our ability to characterize life remotely. Part 3 takes on an overarching issue for any effort to globalize biodiversity observations, the issue of scale. It addresses scale from two perspectives. The first is that of combining observations across varying spatial, temporal, and spectral resolutions for better understanding—that is, what scales and how. This is an area of ongoing research driven by a confluence of innovations in observation systems and rising computational capacity. The second is the organizational side of the scaling challenge. It explores existing frameworks for integrating multi-scale observations within global networks. The focus here is on what practical steps can be taken to organize multi-scale data and what is already happening in this regard. These frameworks include essential biodiversity variables and the Group on Earth Observations Biodiversity Observation Network (GEO BON). This book constitutes an end-to-end guide uniting the latest in research and techniques to cover the theory and practice of the remote sensing of plant biodiversity. In putting it together, the editors and their coauthors, all preeminent in their fields, have done a great service for those seeking to understand and conserve life on Earth—just when we need it most. For if the world is ever to construct a coordinated response to the planetwide crisis of biodiversity loss, it must first assemble adequate—and global—measures of what we are losing