Land Use Change from Non-urban to Urban Areas

This reprint is related to land-use change and non-urban and urban relationships at all spatiotemporal scales and also focuses on land-use planning and regulatory strategies for a sustainable future. Spatiotemporal dynamics, socioeconomic implication, water supply problems and deforestation land degradation (e.g., increase of imperviousness surfaces) produced by urban expansion and their resource requirements are of particular interest. The Guest Editors expect that this reprint will contribute to sustainable development in non-urban and urban areas

Drought Risk Management in Reflect Changing of Meteorological Conditions

Droughts are one of the main extreme meteorological, and hydrological phenomena, which influence both the functioning of ecosystems, and many important sectors of human economic activity. Throughout the world, various direct changes in meteorological, and climatic conditions, such as: air temperature, humidity, and evapotranspiration can be observed. They have a significant influence upon the shaping of the phenomenon of drought. Land cover and land use can also be indirect factors influencing evapotranspiration, and, by the same token, the water balance in the water catchment area. They can also influence the course of the process of the drought. The observed climate change, manifested mainly by increases in temperature, in turn, influencing evapotranspiration, may cause intensification in terms of both the degree and frequency of droughts. Droughts related to changes in the hydrological regime, and to the decrease in water resources. Its results can be observed in various sectors, related, among others, to a demand for water for people, agriculture and the Industry. It can also prove problematic for water ecosystems. To reflect the aforementioned information, a reasonable drought risk management is indispensable in order to ease the water demand related problems in various sectors of human activity. This book presents original research on various drought indicators, modern measurement techniques used, among others, for monitoring and predicting droughts, drought indicator trends, the impact of insufficient precipitation on human activity in the context of climate change, and examples of modern solutions devised to prevent water shortages

Prediction of iceberg-seabed interaction using machine learning algorithms

Every year thousands of icebergs are born out of glaciers in the Arctic zone and carried away by the currents and winds into the North Atlantic. These icebergs may touch the sea bottom in shallow waters and scratch the seabed, an incident called “ice-gouging”. Ice-gouging may endanger the integrity of the buried subsea pipelines and power cables because of subgouge soil displacement. In other words, the shear resistance of the soil causes the subgouge soil displacement to extend much deeper than the ice keel tip. This, in turn, may cause the displacement of the pipelines and cables buried deeper than the most possible gouge depth. Determining the best burial depth of the pipeline is a key design aspect and needs advanced continuum numerical modeling and costly centrifuge tests. Empirical equations suggested by design codes may be also used but they usually result in an over-conservative design. Iceberg management, i.e., iceberg towing and re-routing, is currently the most reliable approach to protect the subsea and offshore structures, where the approaching icebergs are hooked and towed in a safe direction. Iceberg management is costly and involves a range of marine fleets and advanced subsea survey tools to determine the iceberg draft, etc. The industry is constantly looking for cost-effective and quick alternatives to predict the iceberg draft and subgouge soil displacements. In this study, powerful machine learning (ML) algorithms were used as an alternative cost-effective approach to first screen the threatening icebergs by determining their drafts and then to predict the subgouge soil displacement to be fed into the structural integrity analysis. Developing a reliable solution to predict the iceberg draft and subgouge soil displacement requires a profound understanding of the problem's dominant parameters. Therefore, the present study started with dimensional analyses to identify the dimensionless groups of key parameters governing the physics of the problem. Two comprehensive datasets were constructed using the monitored characteristics of the real icebergs for draft prediction and experimental studies for the subgouge soil displacements reported in the literature. Using the constructed database, 14 ML algorithms ranging from neural network-based (NN-based) to three-based methods were sequentially used to predict the iceberg draft and the subgouge soil displacement. The studies were conducted both in clay and sand seabed. By different combinations of the input parameters, several ML models were developed and assessed by performing sensitivity analysis, error analysis, discrepancy analysis, uncertainty analysis, and partial derivative sensitivity analysis to identify the superior ML models along with the most influential input parameters. The best ML model was able to predict the iceberg drafts alongside the subgouge soil features with the highest level of precision, correlation, and lowest degree of complexity. A set of ML-based explicit equations were also derived from the wide range of field and experimental measurements for the estimation of iceberg drafts, subgouge soil deformations, and ice keel reaction forces, which outperformed the existing empirical equations. The study resulted in developing a set of tools that can be used for both a cost-effective screening of the threatening icebergs and the prediction of the corresponding subgouge soil displacements. The outcome of the study can effectively contribute to a significant reduction of iceberg management costs and greenhouse gas (GHG) emissions through the mitigation of the marine spread operation

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition