Tesis sobre el tema "Early water system"

This thesis investigates connections between low albedo asteroids and carbonaceous chondrite meteorites using spectroscopy. Meteorites and asteroids preserve information about the early solar system including accretion processes and parent body processes active on asteroids at these early times. One process of interest is aqueous alteration. This is the chemical reaction between coaccreted water and silicates producing hydrated minerals. Some carbonaceous chondrites have experienced extensive interactions with water through this process. Since these meteorites and their parent bodies formed close to the beginning of the Solar System, these asteroids and meteorites may provide clues to the distribution, abundance and timing of water in the Solar nebula at these times. Chapter 2 of this thesis investigates the relationships between extensively aqueously altered meteorites and their visible, near and mid-infrared spectral features in a coordinated spectral-mineralogical study. Aqueous alteration is a parent body process where initially accreted anhydrous minerals are converted into hydrated minerals in the presence of coaccreted water. Using samples of meteorites with known bulk properties, it is possible to directly connect changes in mineralogy caused by aqueous alteration with spectral features. Spectral features in the mid-infrared are found to change continuously with increasing amount of hydrated minerals or degree of alteration. Building on this result, the degrees of alteration of asteroids are estimated in a survey of new asteroid data obtained from SOFIA and IRTF as well as archived the Spitzer Space Telescope data. 75 observations of 73 asteroids are analyzed and presented in Chapter 4. Asteroids with hydrated minerals are found throughout the main belt indicating that significant ice must have been present in the disk at the time of carbonaceous asteroid accretion. Finally, some carbonaceous chondrite meteorites preserve amorphous iron-bearing materials that formed through disequilibrium condensation in the disk. These materials are readily destroyed in parent body processes so their presence indicates the meteorite/asteroid has undergone minimal parent body processes since the time of accretion. Presented in Chapter 3 is the spectral signature of meteorites that preserve significant amorphous iron-bearing materials and the identification of an asteroid, (93) Minerva, that also appears to preserve these materials.

In lowland river systems, in-channel, slow-flowing or still-water areas (still-water patches, SWPs) are considered to be important habitats for many organisms, particularly the early-life stages of fish and shrimp. However, the distribution of the early life-stages of fish and shrimp among these habitats appears to be very patchy and studies suggest that the quality and diversity of microhabitat conditions within SWPs and the accessibility of SWPs to spawning adults and dispersing young may be important determinants of their suitability as nursery habitat. The aims of this thesis were to examine the utilisation of still-water patches by fish and shrimp in a lowland river in relation to habitat suitability and accessibility, with particular emphasis on early-life stages. To determine the factors influencing habitat selection among SWPs, the environmental variability in SWP habitat, and both the distribution and the movement patterns of fish and shrimp, were examined in the Broken River, a lowland river in south-eastern Australia. SWP habitat was found to be highly spatially and temporally variable in the Broken River. SWPs differed in relation to permanence, accessibility and microhabitat variables, and all life-stages of fish and shrimp were found to be significantly spatially aggregated among SWPs. This suggests that individual SWPs may differ in their suitability as habitat, and/or in their accessibility to dispersing organisms and indicates either differential rates of retention, movement into SWPs, spawning effort or survival among SWPs for these organisms. Significant associations were found for all species and life-stages in relation to the microhabitat characteristics of SWPs. The two introduced species, carp and gambusia, were found to have fewer associations, which suggests that these species are habitat generalists. Cover and SWP morphology variables were shown to be important for all native species. Significant, positive associations were found for most species and life-stages with large, deep, SWPs containing instream cover, however, the extent of cover preferred was variable. It was hypothesised that large, deep SWPs that contain instream cover are more environmentally stable and provide better foraging efficiency and reduced competition for space, whilst also providing refuge from predators and, that they may be easier to locate than smaller patches. Specific associations with microhabitat variables differed among all species and life-stages, and this was attributed to differences in diet and predation rates. Consequently, generalised microhabitat relationships for particular life-stages or species could not be identified and the results from this thesis suggest that a diversity of microhabitat conditions are required to meet the differing requirements of various life-stages and species. Significant associations were also found for most groups in relation to the accessibility characteristics of SWPs, indicating that the ability of individuals to access SWPs is an important factor in determining their distribution among SWPs. This further suggests that movement is an important factor in the distribution pattern of fish and shrimp among SWPs. Significant associations were found for most groups in relation to patch isolation, adjacent hydraulic habitat and entrance conditions, indicating that landscape composition and configuration as well as boundary conditions may be important determinants of organisms being able to locate suitable patches. Associations with accessibility variables differed among species and life-stages, and may be attributable to differences in movement capabilities. Field manipulations of instream cover and entrance depth were conducted to further examine the habitat associations found. The results confirmed a positive relationship between instream cover and fish and shrimp abundances. No species, however, responded consistently to the manipulation of entrance depths, and this was attributed to water level rises throughout the experiment and/or the correlation of entrance depth with SWP depth. However, the results from the field manipulations suggested that deeper habitats are able to be exploited by small-bodied adults and larvae when significant levels of instream cover are also available as refuge from predation. In order to confirm the importance of movement in the selection of SWP habitat by fish and shrimp, the movement patterns of fish and shrimp into and out of SWPs were investigated. Whilst the results from this aspect of the study were inconclusive for fish, the results for shrimp confirmed that adults and larvae moved routinely into and out of SWPs. However, for all shrimp species, movement appeared to be limited to a certain period of larval development, indicating that SWP quality and stability may be more important at particular stages of development than others. The results of this thesis have demonstrated the importance of SWP quality and stability for fish and shrimp in the Broken River and have shown that habitat preferences vary among individual species and life-stages. Consequently, in order to manage for multiple species and life-stages, consideration must be given not only to the availability of SWPs, but also to their stability over time and to the availability of a diverse range of microhabitats. In addition, consideration must also be given to the accessibility of SWPs and this will require a greater knowledge of the specific spawning and dispersal requirements of the organisms which utilise these patches, in combination with a greater understanding of the impacts of flow modification on riverine landscape composition and configuration.

Le système de simulation et d'avis précoce d'alerte est un outil puissant pour la surveillance de la qualité de l'eau de la rivière Mudan, une rivière importante dans les régions froides du nord-est de la Chine et qui se jette finalement dans la rivière de l'Amour en Russie. Ainsi la qualité de l'eau dans la rivière Mudan est une préoccupation importante non seulement au niveau local et régional,mais aussi au niveau international. L'objectif de cette étude est de créer un système de simulation et d'avis précoce d'alerte pour que la distribution spatio-temporelle de la qualité de l'eau durant les périodes de couverture glaciaire et d'eaux libres soit simulée et visualisée précisément et afin que l'on puisse appréhender la variation spatiale de polluants sur le cours de rivière. La thèse est structurée en 7 chapitres. Dans le premier chapitre nous décrivons le contexte de l'étude et faisons un état de lieu des recherches actuelles. Dans le chapitre Il, la comparaison des modèles principaux disponibles pour l'évaluation de la qualité de l'eau est réaliser ainsi que le choix du meilleur modèle comme base pour créer le système de modélisation. Dans le chapitre Ill, la construction du modèle,les conditions limites requises et les paramètres pour le modèle ont été vérifiés et étalonnés. Une procédure de simulation distribuée est conçue dans le chapitre IV pour améliorer l'efficacité de la simulation. Le chapitre V concerne la programmation et la réalisation la de simulation distribuée et le chapitre VI les techniques fondamentales pour mettre en œuvre le système. Le chapitre VII est la conclusion. Il y a trois points innovants dans ce travail: un modèle bidimensionnel de dynamique de fluides de l'environnement pour la rivière Mudan, une méthode efficace du calcul distribué et un prototype de système de simulation et d'avis précoce d'alerte qui peuvent largement améliorer la capacité de surveillance et de gestion de la qualité de l'eau de la rivière Mudan ou d'autres rivières similaires
Simulation and Early Warning System (SEWS) is a powerful tool for river water quality monitoring. Mudan River, an important river in northeastern cold regions of China, can run out of China into Russia. Thus, the water quality of Mudan River is highly concerned not only locally andregionally but also internationally. Objective of this study is to establish an excellent SEWS of water quality so that the spatio-temporal distribution of water quality in both open-water and ice-covered periods can be accurately simulated and visualized to understand the spatial variation of pollutants along the river course. The dissertation is structured into 7 chapters, chapter 1 outlines the background of the study and reviews the current progress. Chapter Il compares the main available models for evaluating river water quality so that a better model can be selected as the basis to establish a modeling system for Mudan River. Chapter Ill establishes the model, the required boundary conditions and parameters for the model were verified and calibrated. Chapter IV, a distributed simulation procedure was designed to increase the simulation efficiency. Chapter V discusses more about the programing and operational issues of the distributed simulation. Chapter VI is about the core techniques to implement the system. Chapter VII is the conclusion of the study to summarize the key points and innovations of the study. The study has the following three points as innovation : a two-dimensional environmental fluid dynamics model for Mudan River, an efficient distributed model computational method and a prototype of SEWS, which can greatly improve the capability of monitoring and management of water quality in Mudan River and other similar rivers

In the chain of water distribution, the network is the most complex element to be analyzed and managed to deliver safe water to the users due to the vast dispersion of the potential contamination spots. For this reason, some countries, especially those most sensible to the terrorist attacks (USA, Israel, Europe) have already started research programs aimed at the development of an Online Water Quality Monitoring (OWQM) and of an Early Warning Systems (EWSs). Both of them are based on sensors installed in selected nodes of the network and are capable of quickly detecting contamination events. The implementation of EWSs paves the way to new interesting research topics, with particular reference to the technological aspects, to the employment of expert systems for the interpretation of the detected data, and to the definition of modeling tools for the design and management of the monitoring and alarm systems. The Thesis focuses on some of these aspects, with the aim of contributing to a partial systematization of the knowledge required for the design and management of the aforementioned systems. This Thesis can be divided into two parts. The former part of the Thesis (Chapters 1, 2 and 3) describes the general issues and the approach normally adopted in choosing the water parameters to be monitored. The latter part of the Thesis (Chapters 4, 5 and 6) deals with some modeling aspects regarding the design and management of EWS, introducing innovative proposals and developments. In particular, the attention is given to the issue of determining the number and the optimal location of the sensors within the network. Ultimately, the last chapter shows the technical feasibility of a smart prototype system for the early detection of biological contaminations within the network. This system will efficiently enable water utility managers to ensure a real-time adoption of water quality control procedures.

Artificial Neural Networks (ANNs) have been comprehensively researched, both from a computer scientific perspective and with regard to their use for predictive modelling in a wide variety of applications including hydrology and the environment. Yet their adoption for live, real-time systems remains on the whole sporadic and experimental. A plausible hypothesis is that this may be at least in part due to their treatment heretofore as “black boxes” that implicitly contain something that is unknown, or even unknowable. It is understandable that many of those responsible for delivering Early Warning Systems (EWS) might not wish to take the risk of implementing solutions perceived as containing unknown elements, despite the computational advantages that ANNs offer. This thesis therefore builds on existing efforts to open the box and develop tools and techniques that visualise, analyse and use ANN weights and biases especially from the viewpoint of neural pathways from inputs to outputs of feedforward networks. In so doing, it aims to demonstrate novel approaches to self-improving predictive model construction for both regression and classification problems. This includes Neural Pathway Strength Feature Selection (NPSFS), which uses ensembles of ANNs trained on differing subsets of data and analysis of the learnt weights to infer degrees of relevance of the input features and so build simplified models with reduced input feature sets. Case studies are carried out for prediction of flooding at multiple nodes in urban drainage networks located in three urban catchments in the UK, which demonstrate rapid, accurate prediction of flooding both for regression and classification. Predictive skill is shown to reduce beyond the time of concentration of each sewer node, when actual rainfall is used as input to the models. Further case studies model and predict statutory bacteria count exceedances for bathing water quality compliance at 5 beaches in Southwest England. An illustrative case study using a forest fires dataset from the UCI machine learning repository is also included. Results from these model ensembles generally exhibit improved performance, when compared with single ANN models. Also ensembles with reduced input feature sets, using NPSFS, demonstrate as good or improved performance when compared with the full feature set models. Conclusions are drawn about a new set of tools and techniques, including NPSFS and visualisation techniques for inspection of ANN weights, the adoption of which it is hoped may lead to improved confidence in the use of ANN for live real-time EWS applications.

This thesis focuses specifically on what was taught in schools in South Australia in the context of art and design education. The period covered by the study extends from the 1880s, when a Central Educational Authority was established in South Australia, to the late 1920s, when significant changes to art and design philosophies and course designs became identifiable. The nature and content of the art and design courses designed and used in South Australia is examined against an historical background of influences such as the South Kensington System of drawing and that devised by Walter Smith for the Massachusetts educational system in the United States of America. The significant contribution of Harry Pelling Gill to the teaching of art and design in schools is closely examined. It is posited that his single influence affected the teaching of art and design in South Australian schools until well into the twentieth century. The process of the study looks in detail at the overall philosophies behind the teaching of art and design, the methodologies employed and the classroom practice which pupils and teachers undertook in the pursuit of courses outlined. Issues such as methods of teacher training, correspondence courses, examinations and exhibitions are considered as these relate to the central theme of the study. The study concludes in the late 1920s, with the advent of a revised course of instruction for public elementary schools, which heralded significant changes in both the content and methodology of art and design teaching in South Australian schools.

In the last 50 years, flooding has figured as the most frequent and widespread natural disaster globally. Extreme precipitation events stemming from climate change could alter the hydro-geological regime resulting in increased flood risk. Near real-time precipitation monitoring at local scale is essential for flood risk mitigation in urban and suburban areas, due to their high vulnerability. Presently, most of the rainfall data is obtained from ground‐based measurements or remote sensing that provide limited information in terms of temporal or spatial resolution. Other problems may be due to the high costs. Furthermore, rain gauges are unevenly spread and usually placed away from urban centers. In this context, a big potential is represented by the use of innovative techniques to develop low-cost monitoring systems. Despite the diversity of purposes, methods and epistemological fields, the literature on the visual effects of the rain supports the idea of camera-based rain sensors but tends to be device-specific. The present thesis aims to investigate the use of easily available photographing devices as rain detectors-gauges to develop a dense network of low-cost rainfall sensors to support the traditional methods with an expeditious solution embeddable into smart devices. As opposed to existing works, the study focuses on maximizing the number of image sources (like smartphones, general-purpose surveillance cameras, dashboard cameras, webcams, digital cameras, etc.). This encompasses cases where it is not possible to adjust the camera parameters or obtain shots in timelines or videos. Using a Deep Learning approach, the rainfall characterization can be achieved through the analysis of the perceptual aspects that determine whether and how a photograph represents a rainy condition. The first scenario of interest for the supervised learning was a binary classification; the binary output (presence or absence of rain) allows the detection of the presence of precipitation: the cameras act as rain detectors. Similarly, the second scenario of interest was a multi-class classification; the multi-class output described a range of quasi-instantaneous rainfall intensity: the cameras act as rain estimators. Using Transfer Learning with Convolutional Neural Networks, the developed models were compiled, trained, validated, and tested. The preparation of the classifiers included the preparation of a suitable dataset encompassing unconstrained verisimilar settings: open data, several data owned by National Research Institute for Earth Science and Disaster Prevention - NIED (dashboard cameras in Japan coupled with high precision multi-parameter radar data), and experimental activities conducted in the NIED Large Scale Rainfall Simulator. The outcomes were applied to a real-world scenario, with the experimentation through a pre-existent surveillance camera using 5G connectivity provided by Telecom Italia S.p.A. in the city of Matera (Italy). Analysis unfolded on several levels providing an overview of generic issues relating to the urban flood risk paradigm and specific territorial questions inherent with the case study. These include the context aspects, the important role of rainfall from driving the millennial urban evolution to determining present criticality, and components of a Web prototype for flood risk communication at local scale. The results and the model deployment raise the possibility that low‐cost technologies and local capacities can help to retrieve rainfall information for flood early warning systems based on the identification of a significant meteorological state. The binary model reached accuracy and F1 score values of 85.28% and 0.86 for the test, and 83.35% and 0.82 for the deployment. The multi-class model reached test average accuracy and macro-averaged F1 score values of 77.71% and 0.73 for the 6-way classifier, and 78.05% and 0.81 for the 5-class. The best performances were obtained in heavy rainfall and no-rain conditions, whereas the mispredictions are related to less severe precipitation. The proposed method has limited operational requirements, can be easily and quickly implemented in real use cases, exploiting pre-existent devices with a parsimonious use of economic and computational resources. The classification can be performed on single photographs taken in disparate conditions by commonly used acquisition devices, i.e. by static or moving cameras without adjusted parameters. This approach is especially useful in urban areas where measurement methods such as rain gauges encounter installation difficulties or operational limitations or in contexts where there is no availability of remote sensing data. The system does not suit scenes that are also misleading for human visual perception. The approximations inherent in the output are acknowledged. Additional data may be gathered to address gaps that are apparent and improve the accuracy of the precipitation intensity prediction. Future research might explore the integration with further experiments and crowdsourced data, to promote communication, participation, and dialogue among stakeholders and to increase public awareness, emergency response, and civic engagement through the smart community idea.
Negli ultimi 50 anni, le alluvioni si sono confermate come il disastro naturale più frequente e diffuso a livello globale. Tra gli impatti degli eventi meteorologici estremi, conseguenti ai cambiamenti climatici, rientrano le alterazioni del regime idrogeologico con conseguente incremento del rischio alluvionale. Il monitoraggio delle precipitazioni in tempo quasi reale su scala locale è essenziale per la mitigazione del rischio di alluvione in ambito urbano e periurbano, aree connotate da un'elevata vulnerabilità. Attualmente, la maggior parte dei dati sulle precipitazioni è ottenuta da misurazioni a terra o telerilevamento che forniscono informazioni limitate in termini di risoluzione temporale o spaziale. Ulteriori problemi possono derivare dagli elevati costi. Inoltre i pluviometri sono distribuiti in modo non uniforme e spesso posizionati piuttosto lontano dai centri urbani, comportando criticità e discontinuità nel monitoraggio. In questo contesto, un grande potenziale è rappresentato dall'utilizzo di tecniche innovative per sviluppare sistemi inediti di monitoraggio a basso costo. Nonostante la diversità di scopi, metodi e campi epistemologici, la letteratura sugli effetti visivi della pioggia supporta l'idea di sensori di pioggia basati su telecamera, ma tende ad essere specifica per dispositivo scelto. La presente tesi punta a indagare l'uso di dispositivi fotografici facilmente reperibili come rilevatori-misuratori di pioggia, per sviluppare una fitta rete di sensori a basso costo a supporto dei metodi tradizionali con una soluzione rapida incorporabile in dispositivi intelligenti. A differenza dei lavori esistenti, lo studio si concentra sulla massimizzazione del numero di fonti di immagini (smartphone, telecamere di sorveglianza generiche, telecamere da cruscotto, webcam, telecamere digitali, ecc.). Ciò comprende casi in cui non sia possibile regolare i parametri fotografici o ottenere scatti in timeline o video. Utilizzando un approccio di Deep Learning, la caratterizzazione delle precipitazioni può essere ottenuta attraverso l'analisi degli aspetti percettivi che determinano se e come una fotografia rappresenti una condizione di pioggia. Il primo scenario di interesse per l'apprendimento supervisionato è una classificazione binaria; l'output binario (presenza o assenza di pioggia) consente la rilevazione della presenza di precipitazione: gli apparecchi fotografici fungono da rivelatori di pioggia. Analogamente, il secondo scenario di interesse è una classificazione multi-classe; l'output multi-classe descrive un intervallo di intensità delle precipitazioni quasi istantanee: le fotocamere fungono da misuratori di pioggia. Utilizzando tecniche di Transfer Learning con reti neurali convoluzionali, i modelli sviluppati sono stati compilati, addestrati, convalidati e testati. La preparazione dei classificatori ha incluso la preparazione di un set di dati adeguato con impostazioni verosimili e non vincolate: dati aperti, diversi dati di proprietà del National Research Institute for Earth Science and Disaster Prevention - NIED (telecamere dashboard in Giappone accoppiate con dati radar multiparametrici ad alta precisione) e attività sperimentali condotte nel simulatore di pioggia su larga scala del NIED. I risultati sono stati applicati a uno scenario reale, con la sperimentazione attraverso una telecamera di sorveglianza preesistente che utilizza la connettività 5G fornita da Telecom Italia S.p.A. nella città di Matera (Italia). L'analisi si è svolta su più livelli, fornendo una panoramica sulle questioni relative al paradigma del rischio di alluvione in ambito urbano e questioni territoriali specifiche inerenti al caso di studio. Queste ultime includono diversi aspetti del contesto, l'importante ruolo delle piogge dal guidare l'evoluzione millenaria della morfologia urbana alla determinazione delle criticità attuali, oltre ad alcune componenti di un prototipo Web per la comunicazione del rischio alluvionale su scala locale. I risultati ottenuti e l'implementazione del modello corroborano la possibilità che le tecnologie a basso costo e le capacità locali possano aiutare a caratterizzare la forzante pluviometrica a supporto dei sistemi di allerta precoce basati sull'identificazione di uno stato meteorologico significativo. Il modello binario ha raggiunto un'accuratezza e un F1-score di 85,28% e 0,86 per il set di test e di 83,35% e 0,82 per l'implementazione nel caso di studio. Il modello multi-classe ha raggiunto un'accuratezza media e F1-score medio (macro-average) di 77,71% e 0,73 per il classificatore a 6 vie e 78,05% e 0,81 per quello a 5 classi. Le prestazioni migliori sono state ottenute nelle classi relative a forti precipitazioni e assenza di pioggia, mentre le previsioni errate sono legate a precipitazioni meno estreme. Il metodo proposto richiede requisiti operativi limitati, può essere implementato facilmente e rapidamente in casi d'uso reali, sfruttando dispositivi preesistenti con un uso parsimonioso di risorse economiche e computazionali. La classificazione può essere eseguita su singole fotografie scattate in condizioni disparate da dispositivi di acquisizione di uso comune, ovvero da telecamere statiche o in movimento senza regolazione dei parametri. Questo approccio potrebbe essere particolarmente utile nelle aree urbane in cui i metodi di misurazione come i pluviometri incontrano difficoltà di installazione o limitazioni operative o in contesti in cui non sono disponibili dati di telerilevamento o radar. Il sistema non si adatta a scene che sono fuorvianti anche per la percezione visiva umana. I limiti attuali risiedono nelle approssimazioni intrinseche negli output. Per colmare le lacune evidenti e migliorare l'accuratezza della previsione dell'intensità di precipitazione, sarebbe possibile un'ulteriore raccolta di dati. Sviluppi futuri potrebbero riguardare l'integrazione con ulteriori esperimenti in campo e dati da crowdsourcing, per promuovere comunicazione, partecipazione e dialogo aumentando la resilienza attraverso consapevolezza pubblica e impegno civico in una concezione di comunità smart.

碩士
元智大學
資訊管理學系
106
With the advanced development of information and communication technology, new device has been presented based on the internet of things (IoTs). Integration of IoTs and cloud computing gradually changed the style of operations and productions in modern manufacturing industry. To maintain the normal operation of production equipment and avoid the unexpected failure of downtime, the preventative maintenance (PM) was used for regular testing and manual inspection. However, it’s difficult to monitor the condition of equipment in real time. Predictive maintenance (PDM ) use the sensor data or other information collected from the equipment monitoring system to evaluate the production line equipment before failure, and determine the maintenance schedule adaptively. This study aims to propose an IoTs framework with three-level layers, including hardware sensing layer, the network information layer, and application layer. First, each device via the sensor technology is used to collect sensor data in the hardware perception layer. Second, the sensor data are transferred to the designated equipment or the storage area in the network information layer. Then, long short-term memory model is used for building prediction models. Third, a graphical visual management interface for decision making is constructed in the application layer. To evaluate the validity of the proposed framework, a water chiller unit equipped new sensor device was used for building early-warning decision support system. The results demonstrate how to build the equipment engineering capability with sensor device and thus to improve the efficiency of industrial operation in a smart factory.

博士
國立臺灣海洋大學
河海工程學系
93
In practice, “rule curve”generally regulates reservoir operation. In an area of uneven water distribution, reservoir can stabilize the water flow to mitigate water problems. Therefore, a task to operate reservoirs efficiently is essential.However, operating by rule curves can’t fit the demand efficiently during dry season, and the operation of rule curves by single reservoir also works inefficiently. In addition, drought events hit Taiwan frequently in recent years, and which brought immense impacts to people’s daily life. Unfortunately, the drought early warning system in Taiwan is not well developed, and the alert categories are in their infancy and quite vague in their definitions. This study gives an example as the two-parallel joint operation between Feitsui and Shismen reservoir in northern Taiwan. The objectives are：(1)to select the optimum reservoir operating alternative for single use. (2)to develop the joint long-term reservoir operating rule curves for conjunctive use.(3)to build a handy early warning system for drought management to reduce the impacts. In this study, a lexicographic method is used to select the operation alternative for the Feitsui reservoir. Simulation results show that the alternative, the limitation of water supply by Taipei city government during the fight against 2002 drought, would be the best in terms of minimum shortage. On the other hand, the alternative, the new rules after May 31 2004, would be selected in terms of maximum water utilization. However, alternative of new rules need an assistance of the Drought Early Warning System to reduce its high risk of reservoir emptiness. Furthermore, the joint operation between Feitsui and Shihmen reservoir system is selected to demonstrace its applicability of the GA-based simulation model on a complex water resource system. In the multiple reservoir model, simulation results show Feitsui's surplus water can be utilized efficiently to fill Shihmen's deficit water without affecting Feitsui's main purpose as Taipei city's water supply. The optimal joint operation suggests that Feitsui, on average, can provide 650,000 m3/day and 850,000 m3/day to Shihmen during the wet season and dry season, respectively. In this research, a color-coded early warning system is also developed and proposed for drought management on the real-time reservoir operation. The system consists of three essential elements, namely, (a) drought watch, (b) water consumption measure, and (c) policy making. A new Drought Alert Index is used to characterize the alert level of drought severity. For demonstration, the drought warning procedures were effectively applied to historical droughts from dry condition to wet situation. The implementation of such a system proves that the decision support-like system can help the water authorities concerned take a timely action while confronting drought threats.

碩士
國立臺灣大學
生物環境系統工程學研究所
95
Climate Change impacts not only on individual situation but on short-term variation and on long-term gradually changes. This kind characteristic should adopt the suitable environmental management with long-term formulation and short-term operation in sure keeping environmental sustainability as social economy developing using environmental resource. In this article, the climate change assessment has been combined to set up an early warning system for a river, and focus on analyzing climate change impacts on water quality and stream assimilative capacity. The issue of warning period is also discussed, and furthermore, the adaptative strategies are provided and evaluated. However, there is a problem for establishing an early warning system that estimate future climate changes would be faced with high uncertainty. In general, take estimations would use global scale GCMs data but too rough-cut for estimating a smaller region, such a stream area. In order to get over the problem, the statistic downscaling model (SDSM) has been used for regional analysis and the data is applied for Touchen River in North Taiwan. The result shows that after downscaling can display regional characteristics reasonably. Farther, the data analysis also shows that in arid period, the concentration of BOD is growing worse. The BOD assimilative capacity of a river is confronted by decreasing. For understanding when will suffer the risk, not only climate change assessment but also local evolution will include to establish an early warning indicator (EWI) for analyzing the warning period, and then, to estimate the optimal strategy and when should take action. Furthermore, the acting warning will be alerted. For sustainable development, both bioenvironmental sustainability and social economy development are important. How to find a compromising point between human development and natural conservation is crucial. But climate changes impacts throw down the gage to our resource. To avoid the impacts, by impacts assessment we establish an early warning system for early warning what risk is, when risk will happen, and how we choose the optimal strategy for planning the course of management. Hopefully by setting up the framework of early warning system can defend stream area form impacts damaging and in sure the sustainable development.

Algae and cyanobacteria occur naturally in source waters and are known to cause extensive problems in the drinking water treatment industry. Cyanobacteria (especially Anabaena sp. and Microcystis sp.) are responsible for many water treatment problems in drinking water treatment works (DWTW) all over the world because of their ability to produce organic compounds like cyanotoxins (e.g. microcystin) and taste and odour compounds (e.g. geosmin) that can have an adverse effect on consumer health and consumer confidence in tap water. Therefore, the monitoring of cyanobacteria in source waters entering DWTW has become an essential part of drinking water treatment management. Managers of DWTW, rely heavily on results of physical, chemical and biological water quality analyses, for their management decisions. But results of water quality analyses can be delayed from 3 hours to a few days depending on a magnitude of factors such as: sampling, distance and accessibility to laboratory, laboratory sample turn-around times, specific methods used in analyses etc. Therefore the use of on-line (in situ) instruments that can supply real-time results by the click of a button has become very popular in the past few years. On-line instruments were developed for analyses like pH, conductivity, nitrate, chlorophyll-a and cyanobacteria concentrations. Although, this real-time (on-line) data has given drinking water treatment managers a better opportunity to make sound management decisions around drinking water treatment options based on the latest possible results, it may still be “too little, too late” once a sudden cyanobacterial bloom of especially Anabaena sp. or Microcystis sp. enters the plant. Therefore the benefit for drinking water treatment management, of changing the focus from real-time results to future predictions of water quality has become apparent. The aims of this study were 1) to review the environmental variables associated with cyanobacterial blooms in the Vaal Dam, as to get background on the input variables that can be used in cyanobacterial-related forecasting models; 2) to apply rule-based Hybrid Evolutionary Algorithms (HEAs) to develop models using a) all applicable laboratory-generated data and b) on-line measureable data only, as input variables in prediction models for harmful algal blooms in the Vaal Dam; 3) to test these models with data that was not used to develop the models (so-called “unseen data”), including on-line (in situ) generated data; and 4) to incorporate selected models into two cyanobacterial incident management protocols which link to the Water Safety Plan (WSP) of a large DWTW (case study : Rand Water). During the current study physical, chemical and biological water quality data from 2000 to 2009, measured in the Vaal Dam and the 20km long canal supplying the Zuikerbosch DWTW of Rand Water, has been used to develop models for the prediction of Anabaena sp., Microcystis sp., the cyanotoxin microcystin and the taste and odour compound geosmin for different prediction or forecasting times in the source water. For the development and first stage of testing the models, 75% of the dataset was used to train the models and the remaining 25% of the dataset was used to test the models. Boot-strapping was used to determine which 75% of the dataset was to be used as the training dataset and which 25% as the testing dataset. Models were also tested with 2 to 3 years of so called “unseen data” (Vaal Dam 2010 – 2012) i.e. data not used at any stage during the model development. Fifty different models were developed for each set of “x input variables = 1 output variable” chosen beforehand. From the 50 models, the best model between the measured data and the predicted data was chosen. Sensitivity analyses were also performed on all input variables to determine the variables that have the largest impact on the result of the output. This study have shown that hybrid evolutionary algorithms can successfully be used to develop relatively accurate forecasting models, which can predict cyanobacterial cell concentrations (particularly Anabaena sp. and Microcystis sp.), as well as the cyanotoxin microcystin concentration in the Vaal Dam, for up to 21 days in advance (depending on the output variable and the model applied). The forecasting models that performed the best were those forecasting 7 days in advance (R2 = 0.86, 0.91 and 0.75 for Anabaena[7], Microcystis[7] and microcystin[7] respectively). Although no optimisation strategies were performed, the models developed during this study were generally more accurate than most models developed by other authors utilising the same concepts and even models optimised by hill climbing and/or differential evolution. It is speculated that including “initial cyanobacteria inoculum” as input variable (which is unique to this study), is most probably the reason for the better performing models. The results show that models developed from on-line (in situ) measureable data only, are almost as good as the models developed by using all possible input variables. The reason is most probably because “initial cyanobacteria inoculum” – the variable towards which the output result showed the greatest sensitivity – is included in these models. Generally models predicting Microcystis sp. in the Vaal Dam were more accurate than models predicting Anabaena sp. concentrations and models with a shorter prediction time (e.g. 7 days in advance) were statistically more accurate than models with longer prediction times (e.g. 14 or 21 days in advance). The multi-barrier approach in risk reduction, as promoted by the concept of water safety plans under the banner of the Blue Drop Certification Program, lends itself to the application of future predictions of water quality variables. In this study, prediction models of Anabaena sp., Microcystis sp. and microcystin concentrations 7 days in advance from the Vaal Dam, as well as geosmin concentration 7 days in advance from the canal were incorporated into the proposed incident management protocols. This was managed by adding an additional “Prediction Monitoring Level” to Rand Waters’ microcystin and taste and odour incident management protocols, to also include future predictions of cyanobacteria (Anabaena sp. and Microcystis sp.), microcystin and geosmin. The novelty of this study was the incorporation of future predictions into the water safety plan of a DWTW which has never been done before. This adds another barrier in the potential exposure of drinking water consumers to harmful and aesthetically unacceptable organic compounds produced by cyanobacteria.
PhD (Botany), North-West University, Potchefstroom Campus, 2015

碩士
國立澎湖科技大學
水產資源與養殖研究所
102
Marine ornamental fish industry is considered as one of the six key emerging industries in the future Taiwan. Recently, researches on anemone fish breeding, cultivation, and farming are proliferation worldwide; however, studies on mass-scale cultivation of anemone fish are scarce. The major limitation and obstacle to industrial-scale cultivation of anemone fish is the high mortality resulting from diseases during the process of cultivation. Hence, the ornamental fish industry stagnated in Taiwan. In present study, the innovated closed water circulation system was applied to prevent the anemone fish from diseases, improve the success rate of breeding pairs, and develop technologies of mass-scale cultivation. Based on the technical support, breeding of commercial hybrids and establishing the provenance library of eye-spot anemone fish (Amphipeion ocellaris) significantly sustain the marine ornamental fish industry in Taiwan. In 10 months of cultivation, a total of 23 pairs of black & white clownfish (Amphiprion ocellaris var.) and black & white clownfish (A. ocellaris var.), 3 pairs of black & white clownfish (A. ocellaris var.) and A. ocellaris, 1 pair of black & white clownfish (A. ocellaris var.) and snowflake clownfish (A. ocellaris var.), and 3 pairs of A. ocellaris and A. ocellaris were successfully matched. The survivorship of the fishes maintained up to 90% during the whole cultivation. The numbers of successfully nurtured 3-cm fries of black & white clownfish, new breed of black & white clownfish × snowflake clownfish, black & white clownfish × clownfish, and clownfish × clownfish are 11000, 1200, 250, and 800, respectively. The results provide the fundamentals for commercial production in future clownfish market.

Dietary supplements were provided during 24 hour transport from hatchery and growth of turkeys in two trials. Female poults (768 and 825 respectively) were used in two 3 x 4 factorial analyses (transportation supplement x post placement supplement) with treatment provided during transport (no supplement, Oasis® and Oasis® + lysozyme (0.01%)) and as dietary supplements post-placement (no supplement, Bacitracin Methylene Disalicylate (BMD)(ANTI), lysozyme (LYS), BMD + lysozyme (AL)) as the main effects. Growth, incidence of mortality, gastrointestinal size, strength and histology and behavioural data was collected. Transport supplementation of poults did not improve growth or reduce mortality, but influenced early feeding and drinking behaviour at placement. Body weight and feed consumption increased and percent mortality decreased for birds fed AL. Gizzard and proventriculus weight increased when birds consumed ANTI and jejunal breaking strength was highest for birds consuming LYS. Villi morphology and bird behaviour were not affected by dietary supplementation.

碩士
國立中山大學
材料與光電科學學系研究所
101
The first part of this thesis is about pulsed laser ablation of micron size NiAl2O4 spinel powder or NiO-Al2O3 powder mixture in water regarding size/phase changes of the powders. The combined X-ray diffraction and transmission electron microscopic observations indicate that upon laser pulses, the stoichiometric nickel aluminate spinel powder were nanosized and decomposed as atom clusters for further nucleation as Al-doped NiO, Ni-doped -Al2O3 and β-Ni(OH)2 whereas the NiO and Al2O3 powder mixture can also become nanosized β-Ni(OH)2, Al-doped NiO, and Ni-doped -Al2O3 for further composition modification as nonstoichiometric nickel aluminate spinel. The colloidal solution containing the nanosized and hydrated phases in the NiO-Al2O3 system have a bimodal minimum band gap around 5 eV and 3 eV according to UV-visible absorption spectrum for potential photocatalytic applications. In the second part, an onset sintering-coarsening-coalescence (SCC) event of submicron NiO and micron NiAl2O4 powders (1:9 molar ratio) by isothermal firing in the 1300-1450oC range in air was characterized by N2 adsorption-desorption hysteresis isotherm and scanning electron microscopy. The apparent activation energy of such a rapid SCC process for the composite powders was estimated as 329±5 kJ/mol based on 50% change of specific surface area with accompanied formation of cylindrical pores. This SCC process was controlled by the relatively small-sized and less refractory NiO particles shedding light on their Brownian rotation kinetics at the NiAl2O4 grain boundaries. The minimum temperature of the SCC process, as of concern to optocatalytic applications of NiO/NiAl2O4 composite nanoparticles, is 1147oC based on the extrapolation of steady specific surface area reduction rates to null.

PhDH
Department of Hydrology and Water Resources
Demand for water resources has been on the increase and is compounded by population growth and related development demands. Thus, numerous sectors are affected by water scarcity and therefore effective management of drought-induced water deficit is of importance. Luvuvhu River Catchment (LRC), a tributary of the Limpopo River Basin in South Africa has witnessed an increasing frequency of drought events over the recent decades. Drought impacts negatively on communities’ livelihoods, development, economy, water resources, and agricultural yields. Drought assessment in terms of frequency and severity using Drought Indices (DI) in different parts of the world has been reported. However, the forecasting and prediction component which is significant in drought preparedness and setting up early warning systems is still inadequate in several regions of the world. This study aimed at characterising, assessing, and predicting drought conditions using DI as a drought quantifying parameter in the LRC. This was achieved through the application of hybrid statistical and machine learning models including predictions via a combination of hybrid models. Rainfall and temperature data were obtained from South African Weather Service, evapotranspiration, streamflow, and reservoir storage data were obtained from the Department of Water and Sanitation while root zone soil moisture data was derived from the NASA earth data Giovanni repository. The Standardised Precipitation Index (SPI), Standardised Precipitation Evapotranspiration Index (SPEI), Standardised Streamflow Index (SSI), and Nonlinear Aggregated Drought Index (NADI) were selected to assess and characterise drought conditions in the LRC. SPI is precipitation based, SPEI is precipitation and evapotranspiration based, SSI is based on streamflow while NADI is a multivariate index based on rainfall, potential evapotranspiration, streamflow, and storage reservoir volume. All indices detected major historical drought events that have occurred and reported over the study area, although the precipitation based indices were the only indices that categorised the 1991/1992 drought as extreme at 6- and 12- month timescales while the streamflow index and multivariate NADI underestimated the event. The most recent 2014/16 drought was also categorised to be extreme by the standardised indices. The study found that the multivariate index underestimates most historical drought events in the catchment. The indices further showed that the most prevalent drought events in the LRC were mild drought. Extreme drought events were the least found at 6.42%, 1.08%, 1.56%, and 4.4% for SPI, SPEI, SSI, and NADI, respectively. Standardised indices and NADI showed negative trends and positive upward trends, respectively. The positive trend showed by NADI depicts a decreased drought severity over the study period. Drought events were characterised based on duration, intensity, severity, and frequency of drought events for each decade of the 30 years considered in this study i.e. between 1986 – 1996, 1996 – 2006, 2006 – 2016. This was done to get finer details of how drought characteristics behaved at a 10-year interval over the study period. An increased drought duration was observed between 1986 - 1996 while the shortest duration was observed between 1996 - 2006 followed by 2006 - 2016. NADI showed an overall lowest catchment duration at 1- month timescale compared to the standardised indices. The relationship between drought severity and duration revealed a strong linear relationship across all indices at all timescales (i.e. an R2 of between 0.6353 and 0.9714, 0.6353 and 0.973, 0.2725 and 0.976 at 1-, 6- and 12- month timescales, respectively). In assessing the overall utilisation of an index, the five decision criteria (robustness, tractability, transparency, sophistication, and extendibility) were assigned a raw score of between one and five. The sum of the weighted scores (i.e. raw scores multiplied by the relative importance factor) was the total for each index. SPEI ranked the highest with a total weight score of 129 followed by the SSI with a score of 125 and then the SPI with a score of 106 while NADI scored the lowest with a weight of 84. Since SPEI ranked the highest of all the four indices evaluated, it is regarded as an index that best describes drought conditions in the LRC and was therefore used in drought prediction. Statistical (GAM-Generalised Additive Models) and machine learning (LSTM-Long Short Term Memory) based techniques were used for drought prediction. The dependent variables were decomposed using Ensemble Empirical Mode Decomposition (EEMD). Model inputs were determined using the gradient boosting, and all variables showing some relative off importance were considered to influence the target values. Rain, temperature, non-linear trend, SPEI at lag1, and 2 were found to be important in predicting SPEI and the IMFs (Intrinsic Mode Functions) at 1, 6- and 12- month timescales. Seven models were applied based on the different learning techniques using the SPEI time series at all timescales. Prediction combinations of GAM performed better at 1- and 6- month timescales while at 12- month, an undecomposed GAM outperformed the decomposition and the combination of predictions with a correlation coefficient of 0.9591. The study also found that the correlation between target values, LSTM, and LSTM-fQRA was the same at 0.9997 at 1- month and 0.9996 at 6- and 12- month timescales. Further statistical evaluations showed that LSTM-fQRA was the better model compared to an undecomposed LSTM (i.e. RMSE of 0.0199 for LSTM-fQRA over 0.0241 for LSTM). The best performing GAM and LSTM based models were used to conduct uncertainty analysis, which was based on the prediction interval. The PICP and PINAW results indicated that LSTM-fQRA was the best model to predict SPEI timeseries at all timescales. The conclusions drawn from drought predictions conducted in this study are that machine learning neural networks are better suited to predict drought conditions in the LRC, while for improved model accuracy, time series decomposition and prediction combinations are also implementable. The applied hybrid machine learning models can be used for operational drought forecasting and further be incorporated into existing early warning systems for drought risk assessment and management in the LRC for better water resources management. Keywords: Decomposition, drought, drought indices, early warning system, frequency, machine learning, prediction intervals, severity, water resources.
NRF

American jurisprudence currently applies two incompatible validity standards to determine which laws are enforceable. The natural law tradition evaluates validity by an uncertain standard of divine law, and its methodology relies on contradictory views of human reason. Legal positivism, on the other hand, relies on a methodology that commits the analytic fallacy, separates law from its application, and produces an incomplete model of law. These incompatible standards have created a schism in American jurisprudence that impairs the delivery of justice. This dissertation therefore formulates a new standard for legal validity. This new standard rejects the uncertainties and inconsistencies inherent in natural law theory. It also rejects the narrow linguistic methodology of legal positivism. In their stead, this dissertation adopts a pragmatic methodology that develops a standard for legal validity based on actual legal experience. This approach focuses on the operations of law and its effects upon ongoing human activities, and it evaluates legal principles by applying the experimental method to the social consequences they produce. Because legal history provides a long record of past experimentation with legal principles, legal history is an essential feature of this method. This new validity standard contains three principles. The principle of reason requires legal systems to respect every subject as a rational creature with a free will. The principle of reason also requires procedural due process to protect against the punishment of the innocent and the tyranny of the majority. Legal systems that respect their subjects' status as rational creatures with free wills permit their subjects to orient their own behavior. The principle of reason therefore requires substantive due process to ensure that laws provide dependable guideposts to individuals in orienting their behavior. The principle of consent recognizes that the legitimacy of law derives from the consent of those subject to its power. Common law custom, the doctrine of stare decisis, and legislation sanctioned by the subjects' legitimate representatives all evidence consent. The principle of autonomy establishes the authority of law. Laws must wield supremacy over political rulers, and political rulers must be subject to the same laws as other citizens. Political rulers may not arbitrarily alter the law to accord to their will. Legal history demonstrates that, in the absence of a validity standard based on these principles, legal systems will not treat their subjects as ends in themselves. They will inevitably treat their subjects as mere means to other ends. Once laws do this, men have no rest from evil.