Evaluating different machine learning methods to simulate runoff from extensive green roofs

Green roofs are increasingly popular measures to permanently reduce or delay storm-water runoff. The main objective of the study was to examine the potential of using machine learning (ML) to simulate runoff from green roofs to estimate their hydrological performance. Four machine learning methods, artificial neural network (ANN), M5 model tree, long short-term memory (LSTM) and k nearest neighbour (kNN), were applied to simulate storm-water runoff from 16 extensive green roofs located in four Norwegian cities across different climatic zones. The potential of these ML methods for estimating green roof retention was assessed by comparing their simulations with a proven conceptual retention model. Furthermore, the transferability of ML models between the different green roofs in the study was tested to investigate the potential of using ML models as a tool for planning and design purposes. The ML models yielded low volumetric errors that were comparable with the conceptual retention models, which indicates good performance in estimating annual retention. The ML models yielded satisfactory modelling results (NSE >0.5) in most of the roofs, which indicates an ability to estimate green roof detention. The variations in ML models' performance between the cities was larger than between the different configurations, which was attributed to the different climatic characteristics between the four cities. Transferred ML models between cities with similar rainfall events characteristics (Bergen–Sandnes, Trondheim–Oslo) could yield satisfactory modelling performance (Nash–Sutcliffe efficiency NSE >0.5 and percentage bias |PBIAS| <25 %) in most cases. However, we recommend the use of the conceptual retention model over the transferred ML models, to estimate the retention of new green roofs, as it gives more accurate volume estimates. Follow-up studies are needed to explore the potential of ML models in estimating detention from higher temporal resolution datasets

Precision fish farming: a new framework to improve production in aquaculture

Aquaculture production of finfish has seen rapid growth in production volume and economic yield over the last decades, and is today a key provider of seafood. As the scale of production increases, so does the likelihood that the industry will face emerging biological, economic and social challenges that may influence the ability to maintain ethically sound, productive and environmentally friendly production of fish. It is therefore important that the industry aspires to monitor and control the effects of these challenges to avoid also upscaling potential problems when upscaling production. We introduce the Precision Fish Farming (PFF) concept whose aim is to apply control-engineering principles to fish production, thereby improving the farmer's ability to monitor, control and document biological processes in fish farms. By adapting several core principles from Precision Livestock Farming (PLF), and accounting for the boundary conditions and possibilities that are particular to farming operations in the aquatic environment, PFF will contribute to moving commercial aquaculture from the traditional experience-based to a knowledge-based production regime. This can only be achieved through increased use of emerging technologies and automated systems. We have also reviewed existing technological solutions that could represent important components in future PFF applications. To illustrate the potential of such applications, we have defined four case studies aimed at solving specific challenges related to biomass monitoring, control of feed delivery, parasite monitoring and management of crowding operations

Utvandringstidspunkt og marin åtferd hjå smolt frå Lærdalselva

Kartlegging av utvandringstidspunkt og marin åtferd hjå smolt av villaks, klekkeriprodusert laks og vill aure frå Lærdalselva vart undersøkt ved hjelp av akustisk telemetri. Av dei 40 fiskane som vart merka frå kvar gruppe vart 34 villaks, 31 klekkerifisk og 21 aure registrert i sjø. Sjølv om studien er basert på få individ av kvar gruppe, fanga ein opp ulike utvandringstoppar hjå smolten. I 2009 var vassføring den utløysande faktoren for utvandringa. Klekkeriprodusert laksesmolt hadde eit likt utvandringsforløp som vill laksesmolt. Klekkeriprodusert fisk vandrar ikkje berre på same dato som villfisk, han syner også same mønster i høve til vandringstid på døgeret. Både klekkerismolt og laksesmolt har relativt kort opphaldstid i utløpsområdet, og begge grupper av fisk søkjer seg raskt utover mot kysten. Auresmolten har ikkje den same retningsbestemte forflyttinga mot utløpet av fjorden som laksesmolten og ein stor del av auresmoltane vart registrert på lyttebøyene både i Årdals- og Lusterfjorden. Det er ikkje registrert auresmolt lenger ute enn til Balestrand-Vangsnes, men dette kan også skuldast mykje mindre tettleik av lyttebøyer i midtre og ytre del enn kva som er tilfellet i indre del av fjorden. Studiet indikerer at laksesmolten bruker omlag 14 dagar på å komme seg ut av Sognefjorden, noko som tilseier at hovudmengda av laksesmolt passerte ytre del av fjordsystemet i perioden medio mai til medio juni i 2009

Modelling of environmental flow options for optimal Atlantic salmon,Salmo salar,embryo survival during hydropeaking

Recent findings on the causes of Atlantic salmon embryo mortality during winter in a hydropeaking river suggest that long duration drawdowns during very cold periods are the most likely cause of mortality in the ramping zone. This paper presents a framework in which thresholds for optimal embryo survival at the microscale are linked to physical habitat requirements at the mesoscale and integrated into alternative hydropower operations at the catchment scale. The connections within this framework are derived from a one-dimensional hydraulic model at the mesoscale and a hydropower simulation programme at the catchment scale. The economic costs and feasibility of several alternative options for hydropeaking operation that would comply with ecological requirements for optimal survival of embryos were evaluated. A method to assess a wide range of alternative hydropower options that considers key factors to mitigate the conflicting requirements of ecological targets, technical feasibility and economics is presented. Targeted alternative environmental flow releases to meet specific ecological objectives are often more effective than general operational rules to comply with legislation. The development of well-informed and targeted mitigation strategies is important for future environmental hydropower management

Brief Communication: Mapping river ice using drones and structure from motion

In cold climate regions, the formation and break-up of river ice is important for river morphology, winter water supply, and riparian and instream ecology as well as for hydraulic engineering. Data on river ice is therefore significant, both to understand river ice processes directly and to assess ice effects on other systems. Ice measurement is complicated due to difficult site access, the inherent complexity of ice formations, and the potential danger involved in carrying out on-ice measurements. Remote sensing methods are therefore highly useful, and data from satellite-based sensors and, increasingly, aerial and terrestrial imagery are currently applied. Access to low cost drone systems with quality cameras and structure from motion software opens up a new possibility for mapping complex ice formations. Through this method, a georeferenced surface model can be built and data on ice thickness, spatial distribution, and volume can be extracted without accessing the ice, and with considerably fewer measurement efforts compared to traditional surveying methods. A methodology applied to ice mapping is outlined here, and examples are shown of how to successfully derive quantitative data on ice processes

Performance of a one-dimensional hydraulic model for the calculation of stranding areas in hydropeaking rivers

Fish stranding is a critical issue in rivers with peaking operations. The ability to accurately predict potential stranding areas can become a decisive factor to assess environmental impacts and to plan mitigation measures. The presented work shows that common procedures suggested in the literature in the use of one-dimensional (1D) models for flood zone mapping are not always applicable to compute stranding areas. Specific and easy-to-understand guidance needs to be given for smaller-scale issues. We provide specific guidelines to accurately predict potential stranding areas in a cost-effective manner. By analysing four different river morphologies in detail in a peaking river, we find that the optimal geometry effort (number of cross sections) does not necessarily coincide with the maximum and it varies between channel types according to river physical characteristics such as sinuosity and channel complexity. The use of a 1D model can provide good estimates with an optimal geometry layout

Thermogravimetric analysis for wood decay characterisation

The paper focuses on the use of thermogravimetric analysis (TGA) as a fast method for estimating the change of lignocellulosic materials during fungal degradation in laboratory trials. Traditionally, evaluations of durability tests are based on mass loss. However, to gain more knowledge of the reasons for differences in durability and strength between wooden materials, information on the chemical changes is needed. Pinus sylvestris sapwood was incubated with the brown rot fungus Gloeophyllum trabeum and the white rot fungus Trametes versicolor. The TGA approach used was found to be reproducible between laboratories. The TGA method did not prove useful for wood deteriorated by white rot, but the TGA showed to be a convenient tool for fast estimation of lignocellulosic components both in sound wood and wood decayed by brown rot