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Ecosystem Service (ES) toolkits are increasingly used to quantify and visualize the benefits gained from Nature-based-solutions (NBS) but modules for hydrology are often absent, or if present they lack meaningful hydrological functionality or validation. This leads to gaps in the evidence base required by decision makers. To bridge the gap between such limitations and more complex hydrological models this paper presents a hydrologically based NBS model compatible with spatial ES toolkits. The approach ‘Adapted Nature-based-solutions Rational Method’ (ANaRM) is based on the Rational Method, widely used in hydrology. We apply this model to the city of Birmingham, England, to validate its performance and to analyse the effects of different NBS scenarios. The validated ANaRM model provided robust estimates of peak flow using design storm rainfall. It proved capable of simulating the hydrological effects of NBS such as land use change from urban to green, or installation of SuDS and ponds. Results suggest ponds are found most effective for achieving peak flow reduction in channels and are the best option for mitigating fluvial flooding downstream. Reduction in localised runoff and pluvial flooding is best achieved by converting impervious surfaces such as buildings, hardstanding and roads to green solutions such as green roofs, permeable pavements and greenspace. This paper highlights the importance of considering the spatial effects of urban NBS on hydrology, and that these can be captured with relatively simple modelling approaches such as ANaRM. Its ease of use means it suits any level of user looking to represent the flood mitigation aspect of NBS spatially and has high potential as part of any ES toolkit focused on representing the spatial effects of NBS on ecosystem services.

Delve into Splunk and extend intelligence capabilities, and leverage machine learning to explore data efficientlyKey FeaturesGain the expertise you need to implement the advanced features and capabilities of Splunk 8Get to grips with advanced Splunk features and create compelling reports and dashboardsDevelop and manage advanced Splunk pipelines to integrate intelligence capabilities within your organizationBook DescriptionSplunk is the most widely used engine for working with machine-generated data. This expert-level guide will help you to leverage advanced use cases to drive business growth using operational intelligence and business analytics features. You'll start with an introduction to the new features in Splunk 8 and cover step-by-step exercises that will help you to understand each feature in depth. Next, you'll explore key tasks such as workload management, performance and alerting, Splunk Enterprise Security, and advanced indexing. You'll also learn how to create categorical charts and run analytical operations on metrics within the Splunk Analytics workspace, before understanding how to deliver insights across your organization even when faced with limited or complex data using advanced data analytics. The book will also show you how to monitor and maintain Splunk environments using advanced dashboards. Later, you'll create custom data visualizations and update dashboards using drag and drop and the UI-based dashboard editor. Finally, you'll add SplunkJS to a web app and use the Splunk Machine Learning Toolkit (MLTK) as an extension to the core Splunk platform using real-world use cases. By the end of this book, you'll have learned how to use various Splunk features to extend intelligence capabilities and perform machine learning to explore data effectively.What you will learnUnderstand the components of Splunk 8 and how they workConvert distributed search environments to clusters and configure disaster recovery sites using index clusteringFind out how to

Transform machine-generated data into valuable business insights using the powers of Splunk Key Features Explore the all-new machine learning toolkit in Splunk 7.x Tackle any problems related to searching and analyzing your data with Splunk Get the latest information and business insights on Splunk 7.x Book Description Splunk makes it easy for you to take control of your data and drive your business with the cutting edge of operational intelligence and business analytics. Through this Learning Path, you'll implement new services and utilize them to quickly and efficiently process machine-generated big data. You'll begin with an introduction to the new features, improvements, and offerings of Splunk 7. You'll learn to efficiently use wildcards and modify your search to make it faster. You'll learn how to enhance your applications by using XML dashboards and configuring and extending Splunk. You'll also find step-by-step demonstrations that'll walk you through building an operational intelligence application. As you progress, you'll explore data models and pivots to extend your intelligence capabilities. By the end of this Learning Path, you'll have the skills and confidence to implement various Splunk services in your projects. This Learning Path includes content from the following Packt products: Implementing Splunk 7 - Third Edition by James Miller Splunk Operational Intelligence Cookbook - Third Edition by Paul R Johnson, Josh Diakun, et al What you will learn Master the new offerings in Splunk: Splunk Cloud and the Machine Learning Toolkit Create efficient and effective searches Master the use of Splunk tables, charts, and graph enhancements Use Splunk data models and pivots with faster data model acceleration Master all aspects of Splunk XML dashboards with hands-on applications Apply ML algorithms for forecasting and anomaly detection Integrate advanced JavaScript charts and leverage Splunk's API Who this book is for This Learning Path is for data analysts, bus

Sustainable urban water management initiatives are increasingly required to combat rapid urbanisation and climate pressures. Initiatives include the role of tree planting for which there is need for strong evidence of benefits and drawbacks to support effective future planning. We report robustness of evidence from an assimilated database of urban hydrological impact studies which often had differing primary purposes. Consistent impacts were found at local level, with trees reducing runoff and infiltration. Despite the consistency of evidence, much is undermined by being somewhat lacking in robustness and scientific rigour. Many studies lack adequate controls, and models are often not strongly tested against observations. Moreover, evidence of impact at larger scales is lacking. Effects of tree characteristics were also investigated; such as maturity and species for which evidence is consistent and detailed, and arrangement for which there is less evidence. Realising the full potential of trees in urban water management decision-making would benefit from more-rigorous evidence.

In November 2009 and December 2015, two record‐breaking 24‐hr rainfalls occurred in Cumbria, UK, significantly changing the perception of flood risk for local communities. FEH13, the current UK rainfall depth‐duration‐frequency (DDF) model, estimated return periods of around 1,000 years for both events. The previous model, FEH99, received criticism from panel engineers responsible for making technical safety decisions relating to reservoirs for appearing to estimate relatively short return periods for extreme events. Although FEH13 is more consistent with current probable maximum precipitation (PMP) estimates, there is high uncertainty in both models due to the limited number of extremes captured by UK rain gauges. Furthermore, neither model included the 2009 or 2015 event in its calibration. Here, we re‐calibrate FEH13 using additional gauged rainfall data collected in Cumbria during 2006–2016, including the record‐breaking 2009 and 2015 storms. Using the updated calibration data set reduces the estimated return periods of the 2009 and 2015 events to approximately 140 years each. This case study illustrates the considerable uncertainty in short‐sample records, demonstrates the importance of maximising the quantity of relevant calibration data, shows that perception of risk depends upon the method and data used, and illustrates the difficulty of separating trends and natural variability.

A comprehensive guide to making machine data accessible across the organization using advanced dashboardsAbout This BookEnrich machine-generated data and transform it into useful, meaningful insightsPerform search operations and configurations, build dashboards, and manage logsExtend Splunk services with scripts and advanced configurations to process optimal resultsWho This Book Is ForThis book is intended for data analysts, business analysts, and IT administrators who want to make the best use of big data, operational intelligence, log management, and monitoring within their organization. Some knowledge of Splunk services will help you get the most out of the bookWhat You Will LearnFocus on the new features of the latest version of Splunk Enterprise 7Master the new offerings in Splunk: Splunk Cloud and the Machine Learning ToolkitCreate efficient and effective searches within the organizationMaster the use of Splunk tables, charts, and graph enhancementsUse Splunk data models and pivots with faster data model accelerationMaster all aspects of Splunk XML dashboards with hands-on applicationsCreate and deploy advanced Splunk dashboards to share valuable business insights with peersIn DetailSplunk is the leading platform that fosters an efficient methodology and delivers ways to search, monitor, and analyze growing amounts of big data. This book will allow you to implement new services and utilize them to quickly and efficiently process machine-generated big data. We introduce you to all the new features, improvements, and offerings of Splunk 7. We cover the new modules of Splunk: Splunk Cloud and the Machine Learning Toolkit to ease data usage. Furthermore, you will learn to use search terms effectively with Boolean and grouping operators. You will learn not only how to modify your search to make your searches fast but also how to use wildcards efficiently. Later you will learn how to use stats to aggregate values, a chart to turn data, and a time chart to show value

This research demonstrates a machine learning (ML) approach to array-based free-space optical (FSO) communication using mobile devices. Modern warfighters need non'radio frequency (RF) communication methods to eliminate the risks associated with RF communication, such as detection, eavesdropping, and jamming. FSO communications promises tremendous throughput among other advantages, such as low-probability of intercept/detect and resistance to jamming. However, atmospheric conditions significantly reduce achieved performance by introducing fading and noise on the channel. To increase channel resilience and throughput, we employ spatial codes using an array of lasers at the transmitter and train several ML models on the channel alphabet to provide efficient decoding at the receiver. We compare the performance of a Single Shot Detection (SSD) MobileNet model with a You-Only-Look-Once model during the training process, and we demonstrate data transfer over a proof-of-concept system using the trained SSD MobileNet model. We detail the hardware and software implementation for the proof-of-concept, which uses handheld mobile devices and an array of low-cost, low-power lasers. Future experimentation is planned to incorporate forward-error correction and testing over greater distances under realistic conditions.

Hydrological response of an urban catchment to storm events is determined by a number of factors including the degree of urbanisation and distribution and connectivity of urbanised surfaces. Therefore, the ability of spatially averaged catchment descriptors to characterise storm response is limited. Landscape metrics, widely used in ecology to quantify landscape structure, are employed to quantify urban land-cover patterns across a rural-urban gradient of catchments and attribute hydrological response. Attribution of all response metrics, except peak flow, is improved by combining lumped catchment descriptors with spatially explicit landscape metrics. Those representing connectedness and shape of suburban and natural greenspace improve characterisation of percentage runoff and storm runoff. Connectivity and location of urban surfaces are more important than impervious area alone for attribution of timing, validating findings from distributed hydrological modelling studies. Findings suggest potential improvements in attribution of storm runoff in ungauged urban catchments using landscape metrics.