Recent advances in image-based methods for environmental monitoring are opening new frontiers for remote streamflow measurements in natural environments. Such techniques offer numerous advantages compared to traditional approaches. Despite the wide availability of cost-effective devices and software for image processing, these techniques are still rarely systematically implemented in practical applications, probably due to the lack of consistent operational protocols for both phases of images acquisition and processing. In this work, the optimal experimental setup for LSPIV based flow velocity measurements under different conditions is explored using the software PIVlab, investigating performance and sensitivity to some key factors. Different synthetic image sequences, reproducing a river flow with a realistic velocity profile and uniformly distributed floating tracers, are generated under controlled conditions. Different parametric scenarios are created considering diverse combinations of flow velocity, tracer size, seeding density, and environmental conditions. Multiple replications per scenario are processed, using descriptive statistics to characterize errors in PIVlab estimates. Simulations highlight the crucial role of some parameters (e.g., seeding density) and demonstrate how appropriate video duration, frame-rate and parameters setting in relation to the hydraulic conditions can efficiently counterbalance many of the typical operative issues (i.e., scarce tracer concentration) and improve algorithms performance.

Collecting vast amount of data and performing complex calculations to feed modern Numerical Weather Prediction (NWP) algorithms require to centralize intelligence into some of the most powerful energy and resource hungry supercomputers in the world. This is due to the chaotic complex nature of the atmosphere which interpretation require virtually unlimited computing and storage resources. With Machine Learning (ML) techniques, a statistical approach can be designed in order to perform weather forecasting activity. Moreover, the recently growing interest in Edge Computing Tiny Intelligent architectures is proposing a shift towards the deployment of ML algorithms on Tiny Embedded Systems (ES). This paper describes how Deep but Tiny Neural Networks (DTNN) can be designed to be parsimonious and can be automatically converted into a STM32 microcontroller-optimized C-library through X-CUBE-AI toolchain; we propose the integration of the obtained library with Miosix, a Real Time Operating System (RTOS) tailored for resource constrained and tiny processors, which is an enabling factor for system scalability and multi tasking. With our experiments we demonstrate that it is possible to deploy a DTNN, with a FLASH and RAM occupation of 45,5 KByte and 480 Byte respectively, for atmospheric pressure forecasting in an affordable cost effective system. We deployed the system in a real context, obtaining the same prediction quality as the same DNN model deployed on the cloud but with the advantage of processing all the necessary data to perform the prediction close to environmental sensors, avoiding raw data traffic to the cloud.

Green roofs have been widely recognized as sustainable nature-based solutions to mitigate floods in urban areas, which, in the last decades, are increasing due to the combination of intense worldwide urbanization and climate change. Besides flood mitigation, green roofs provide additional benefits for the urban environment (e.g., reducing the urban heat island and ensuring energy saving for the underneath building). Moreover, green roofs facilitate the increase of urban biodiversity, attracting different species of small animals, and upgrade the city aesthetic value. Among the different types of green roofs, multilayer blue-green roofs present an additional layer to store water during rainfall events. As part of the Polder Roof field lab project, prototypes of multilayer blue-green roof developed by the Dutch company Metropolder were installed in four Italian cities: Cagliari, Palermo, Perugia, and Viterbo. The four prototypes and the experimental set up are described and the potential benefits of this innovative solution are discussed. Preliminary analyses, from December 2020 to December 2021, enable to estimate runoff reduction and thermal properties of multilayer blue-green roofs, underlying the high potential of this nature-based solution, which allows to retain most of the rainfall events and to mitigate the daily temperature variability.

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<p>Technological advances over last decades gave an innovative impulse to development of new streamflow measurements techniques, making possible to implement remote flow monitoring methods that allow for non-intrusive measurements. Here, we focus on image-based techniques that involve the use of digital camera, either installed on a bridge or equipped by a drone (UAVs – Unmanned Aerial Vehicles). The most widely known and used optical techniques are the Large-Scale Particle Image Velocimetry and the Large-Scale Particle Tracking Velocimetry. Optical techniques are based on four main steps: (i) seeding and recording, (ii) images pre-processing, (iii) images processing, and (iv) images post-processing. Tracer, naturally present on the water surface or artificially introduced, is assumed to move jointly with the surface liquid particles. Tracer dynamic is recorded and the resulting videos are processed by specific software, applying a statistical cross-correlation analysis to detect the most probable frame-by-frame tracer displacements. To obtain river discharge, it is then necessary to combine the geometry of the river cross-section with the assessed surface velocity field, often adopting simplified assumptions about the vertical velocity profiles.<br>The accuracy of these techniques depends on several factors, such as the size of the interrogation area, the seeding density, the video length, and many other aspects related to environmental and hydraulic conditions, that are less investigated in the scientific literature. The aim of this work is to exploit the results of an extensive field measurement campaign on several Sicilian rivers (Italy) to infer useful insights for the parametric setting of the two most popular open-source processing LS-PIV software (i.e, PIVlab and FUDAA-LSPIV). The field campaign includes discharge measures carried out at different sites, taking into account different roughness conditions and cross-sections, and, for each site, in different seasons, accounting then for different environmental and hydraulic conditions. Topographical surveys were preliminary performed on each site to obtain detailed DEMs, which are used in the pre-processing phase for image stabilization and orthorectification. Video sequences were acquired from both bridge and drone, using wood chips as tracer. Benchmark measures were also retrieved by ADCP.</p>