Selection and Calibration of Numerical Modeling in Flow and Water Quality

Numerical modeling is frequently used in coastal engineering research and application. One possible issue associated with using this method however, is that initial outcomes might differ from expectation. Modeling manipulation addresses this issue by changing the initial parameters which in turn affects the final output results. The advancement of modeling techniques in recent years has seen a gradual but steady accruement of knowledge regarding the relationship between input parameters and possible outputs, which has resulted in improved accuracy and efficiency methods. The advancements within this field have predominantly occurred on an individual basis and as such lack standardisation. An expert numerical modeler may use this knowledge subconsciously, yet may not know how to convey it to the model users. This paper has been written to introduce a systematic intelligent coding schema designed to organise current coastal engineering modeling manipulation knowledge into a standardised format. This system is relevant to the development of appropriate strategies for improved accuracy and efficiency as well as model modification to simulate specific real phenomena in prototype application cases.     

Distributed process modeling for regional assessment of coastal vulnerability to sea-level rise

Sea-level rise involves increases in the coastal processes of inundation and erosion which are affected by a complex interplay of physical environmental parameters at the coast. Many assessments of coastal vulnerability to sea-level rise have been detailed and localised in extent. There is a need for regional assessment techniques which identify areas vulnerable to sea-level rise. Four physical environmental parameters – elevation, exposure, aspect and slope, are modeled on a regional scale for the Northern Spencer Gulf (NSG) study area using commonly available low-resolution elevation data of 10 m contour interval and GIS-based spatial modeling techniques. For comparison, the same parameters are modeled on a fine-scale for the False Bay area within the NSG using high-resolution elevation data. Physical environmental parameters on the two scales are statistically compared to coastal vulnerability classes as identified by Harvey et al. [1] using the Spearman rank-correlation test and stepwise linear regression. Coastal vulnerability is strongly correlated with elevation and exposure at both scales and this relationship is only slightly stronger for the high resolution False Bay data. The results of this study suggest that regional scale distributed coastal process modeling may be suitable as a first cut in assessing coastal vulnerability to sea-level rise in tide-dominated, sedimentary coastal regions. Distributed coastal process modeling provides a suitable basis for the assessment of coastal vulnerability to sea-level rise of sufficient accuracy for on-ground management and priority-setting on a regional scale.     

Environmental Exposure Assessment for Emergency Response in a Nuclear Power Plant Using an Integrated Source Term and 3D Numerical Model

Nuclear power plants are normally assumed to be safe when their radiation impact in all operational states is kept at a reasonably low level. However, accidentally released radioactive substances and ionizing radiation may lead to a situation that cannot maintain the regulatory prescribed dose limits for internal and external exposure of the personnel and population. Nuclear emergency preparedness and response in nuclear or radiological events have been of concern recently in international communities. Nuclear power plants may need to provide essential information regarding possible scenarios of accidental releases that might have short-term detrimental effects and long-term risks in nearby populated regions. This paper presents a synergistic integration of a source term model and a three-dimensional, time-dependent, numerical model (i.e., HOTMAC/RAPTAD), which was applied to simulate a specific scenario in which a vapor cloud was accidentally released from Maanshan (i.e., the third nuclear power plant) in South Taiwan. It aims at dealing with middle-range risk assessment for nuclear emergency preparedness and response. The solutions of such an integrated modeling platform can be found with numerical analyses that describe the processes of radionuclide generation, transport, decay, and deposition, giving the final risk assessment in a neighboring coastal city—Kaohsiung, South Taiwan. In addition, sensitivity analyses were performed to evaluate the internal consistency of model parameters, which further support the application potentials. Such a modeling technique is valuable because it can characterize the fate and transport of radioactive nuclides over the long term. The case study in South Taiwan uniquely demonstrates the feasibility and significance of such model integration.     

决策树模型在水环境监测网络中选取代表性样点的应用

地表水体中的硝酸盐污染已经成为全球关注的热点环境问题之一。现今,国内外均建立了相关的监测网络对地表水体的水质实施长期监测,但是却导致大量的监测数据累积,给后续的科学研究工作带来了不便,尤其是在庞大的监测网络中如何选取有代表性样点的研究点则成为急需解决的问题之一。以比利时弗拉芒地区地表水的长期监测物理化学指标为例,利用决策树模型评估地表水样点的硝酸盐污染来源专家分类的有效性,为点位优化提供理论依据。原有监测点位的污染源专家分类和模型输出的可匹配率为80%,优化后监测点位从原有47个点降低到30个点,提高了监测工作效率。     

智能算法及其在环境预警中的应用

智能算法具有学习非线性问题的能力,可有效优化环境模型结构与参数,是环境预警的重要工具。重点分析了遗传算法和人工神经网络的相关特征,并以太湖蓝藻水华预报预警为例,介绍其在提高环境模型精度中的应用。     

Using exploratory regression to identify optimal driving factors for cellular automaton modeling of land use change

Defining transition rules is an important issue in cellular automaton (CA)-based land use modeling because these models incorporate highly correlated driving factors. Multicollinearity among correlated driving factors may produce negative effects that must be eliminated from the modeling. Using exploratory regression under pre-defined criteria, we identified all possible combinations of factors from the candidate factors affecting land use change. Three combinations that incorporate five driving factors meeting pre-defined criteria were assessed. With the selected combinations of factors, three logistic regression-based CA models were built to simulate dynamic land use change in Shanghai, China, from 2000 to 2015. For comparative purposes, a CA model with all candidate factors was also applied to simulate the land use change. Simulations using three CA models with multicollinearity eliminated performed better (with accuracy improvements about 3.6%) than the model incorporating all candidate factors. Our results showed that not all candidate factors are necessary for accurate CA modeling and the simulations were not sensitive to changes in statistically non-significant driving factors. We conclude that exploratory regression is an effective method to search for the optimal combinations of driving factors, leading to better land use change models that are devoid of multicollinearity. We suggest identification of dominant factors and elimination of multicollinearity before building land change models, making it possible to simulate more realistic outcomes.     

Assessment and Prediction of Contaminant Transport and Migration at a Florida Superfund Site

An important issue in the application of mathematical models to describe or predict the fate of solutes in soil and groundwater systems is providing the necessary data to support the spatial, temporal, and hydrogeologic model parameter requirements. The larger, more complex the model and scope of application, the more likely that significant amounts of data of several types are required. A management tool is required that allows the model and the modeler to interact with various needed databases, cope with numerous database management issues, efficiently achieve model parameterization and provide visualization of model output. The three-dimensional Groundwater Modeling System (GMS) that contains a state-of-the-art flow and solute transport model and a graphical user interface for data manipulation and analysis was applied to a Superfund site in Florida to demonstrate its capabilities for predicting solute migration.     

Identification of spatiotemporal nutrient patterns in a coastal bay via an integrated k-means clustering and gravity model

This study presents an integrated k-means clustering and gravity model (IKCGM) for investigating the spatiotemporal patterns of nutrient and associated dissolved oxygen levels in Tampa Bay, Florida. By using a k-means clustering analysis to first partition the nutrient data into a user-specified number of subsets, it is possible to discover the spatiotemporal patterns of nutrient distribution in the bay and capture the inherent linkages of hydrodynamic and biogeochemical features. Such patterns may then be combined with a gravity model to link the nutrient source contribution from each coastal watershed to the generated clusters in the bay to aid in the source proportion analysis for environmental management. The clustering analysis was carried out based on 1 year (2008) water quality data composed of 55 sample stations throughout Tampa Bay collected by the Environmental Protection Commission of Hillsborough County. In addition, hydrological and river water quality data of the same year were acquired from the United States Geological Survey's National Water Information System to support the gravity modeling analysis. The results show that the k-means model with 8 clusters is the optimal choice, in which cluster 2 at Lower Tampa Bay had the minimum values of total nitrogen (TN) concentrations, chlorophyll a (Chl-a) concentrations, and ocean color values in every season as well as the minimum concentration of total phosphorus (TP) in three consecutive seasons in 2008. The datasets indicate that Lower Tampa Bay is an area with limited nutrient input throughout the year. Cluster 5, located in Middle Tampa Bay, displayed elevated TN concentrations, ocean color values, and Chl-a concentrations, suggesting that high values of colored dissolved organic matter are linked with some nutrient sources. The data presented by the gravity modeling analysis indicate that the Alafia River Basin is the major contributor of nutrients in terms of both TP and TN values in all seasons. With this new integration, improvements for environmental monitoring and assessment were achieved to advance our understanding of sea-land interactions and nutrient cycling in a critical coastal bay, the Gulf of Mexico.     

Mercury contamination in the estuaries and coastal sediments of the Strait of Malacca

Fishers’ local ecological knowledge (LEK) is an additional tool to obtain information about cetaceans, regarding their local particularities, fishing interactions, and behavior. However, this knowledge could vary in depth of detail according to the level of interaction that fishers have with a specific species. This study investigated differences in small-scale fishers’ LEK regarding the estuarine dolphin (Sotalia guianensis) in three Brazilian northeast coastal communities where fishing is practiced in estuarine lagoons and/or coastal waters and where dolphin-watching tourism varies from incipient to important. The fishers (N?=?116) were asked about general characteristics of S. guianensis and their interactions with this dolphin during fishing activities. Compared to lagoon fishers, coastal fishers showed greater knowledge about the species but had more negative interactions with the dolphin during fishing activities. Coastal fishing not only offered the opportunity for fishers to observe a wider variety of the dolphin’s behavior, but also implied direct contact with the dolphins, as they are bycaught in coastal gillnets. Besides complementing information that could be used for the management of cetaceans, this study shows that the type of environment most used by fishers also affects the accuracy of the information they provide. When designing studies to gather information on species and/or populations with the support of fishers, special consideration should be given to local particularities such as gear and habitats used within the fishing community.     

Development of a landfill model to prioritize design and operating objectives

The application of scientifically based decision making tools to help address solid waste management issues dates back to the early 1960s. Researchers continue to use operations research tools to help optimize landfill design and operating parameters. This paper discusses the application of another type of decision making tool, the analytical hierarchy process (AHP), to address priority ranking for a number of landfill engineering design and operating objectives in developing and developed countries. In this application, the AHP is used to rank, and prioritize, economic, environmental, health and safety, legislative and public perception objectives for landfill design and operations specific to landfill distance from a community, and precipitation levels. Results from a global survey using the Delphi process are included, with a discussion on the survey’s impact on the objective rankings relative to community proximity and precipitation. The Delphi process worked extremely well, and was an excellent tool to use in this application. The initial results from the objective rankings show promise in the development of an integrated model for landfill design and operation.     

Developing a fuzzy neural network-based support vector regression (FNN-SVR) for regionalizing nitrate concentration in groundwater

The aim of this study is to develop a fuzzy neural network-based support vector regression model (FNN-SVR) for mapping crisp-input and fuzzy-output variables. In this model, an artificial neural network (ANN) estimator based on multilayer perceptron (MLP) is considered as the kernel function of the SVR, whereas asymmetric triangular fuzzy H-level sets are assumed for model parameters including weight and biases of the ANN model. A genetic algorithm (GA) with real coding is implemented to optimize the model parameters during the training phase. To evaluate the efficiency and applicability of the proposed model, it is applied for simulating and regionalizing nitrate concentration in Karaj Aquifer in Iran. The goodness-of-fit criteria indicate a better performance of the FNN-SVR compared to some benchmark models such as geostatistic techniques as well as traditional SVR models with linear, quadratic, polynomial, and Gaussian kernel functions for modeling nitrate concentrations in groundwater.     

Deformation of slopes as a cause of underground mining activities: three case studies from Ostrava–Karviná coal field (Czech Republic)

Underground mining activities may potentially play a role on the initiation and reactivation of the slope movements. However, an adequate attention has not yet been paid to these problems; in this study, the possible influence of present and former mining activities on the selected set of model slope deformations in the Ostrava-Karviná Coalfield (Opliji, Repiste and Orlova Lazy District) was analysed and a methodology for their observation for application to similar conditions and influence was described. Isocatabase maps, terrain deformation parameters calculated for the point lying on the slope deformation surface, length measurement by zone extensometer and dilatometer measurement in cracks was also provided for evaluation of the underground mining impact. It was found that inclinations of both boreholes were evidence of underground mining impact, and localization of inclinometer measurement on boreholes in the active part as well as in the near vicinity was very important as an important result of this study. Analysis of underground mining activity influence on model localities in relation to performed mining operations, subsidence and other influences on the ground surface was also determined. Thus, the study will contribute to a more objective knowledge of these problems of interest for the professional public and also for the state administration to solve problems associated with the utilisation and settlement of such affected areas.     

Fuzzy Possibilistic Modeling for Closed Loop Recycling Collection Networks

Nowadays, in the competitive market, commercial companies due to their economic problems and also restrictions imposed by international organizations to comply with environmental regulations are making noticeable efforts to reduce the level of their wastes in their manufacturing systems and consequently the level of waste in consumers’ products. In harmony with this issue, one of the most effective ways which has successfully been used and proven to be economically profitable is to take products lifecycles into consideration. Consideration of products lifecycle has made supply chain practitioners to investigate the reverse logistics activities in addition to forward logistics activities. Hence, corporations, in order to reduce their cost on the one hand and boosting their efficiency on the other hand, were obligated to employ closed loop supply chain models to concurrently benefit from its economical and environmental advantages. Therefore, in this paper, a mixed nonlinear facility location–allocation model is proposed for recycling collection centers. The considered closed loop logistics model consists of multiple echelons, multiple suppliers, multiple collection centers, multiple time period and also multiple facilities. In real-life problems parameters like demand, cost, capacity, distances, and quantities of returned products are always uncertain. Therefore, in order to solve a realistic problem, foregoing parameters are considered as fuzzy in our proposed model. Subsequently, to solve fuzzy mixed nonlinear programming model, one of the most effective technique in the literature is used. Additionally, in order to demonstrate the behavior of the parameters employed in the model, a comprehensive sensitivity analysis is conducted. Computational results demonstrate that the proposed model can show favorable efficiency in solving supply chain problems.     

Statistical Simulation to Estimate Uncertain Behavioral Parameters of Hybrid Energy-Economy Models

In energy-economy modeling, new hybrid models attempt to combine the technological explicitness of bottom-up models with the macroeconomic feedbacks and statistically estimated behavioral parameters of top-down models. However, statistical estimation of behavioral parameters (portraying firm and household technology choices) with such models is challenged by the number of uncertain variables and the lack of historical data on technologies in terms of capital costs, operating costs, and market shares. Multiple combinations of parameter values might equally explain past technology choices. This paper reports on the application of a Bayesian statistical simulation approach for estimating the most likely values for these key behavioral parameters in order to best explain past technology choices and then simulate policies to influence future technology choices. The method included (1) data collection of key technology market shares, capital costs, and operating costs over the past; (2) backcasting a hybrid energy-economy model over a historical time period; and (3) the application of Markov chain Monte Carlo statistical simulation using the Metropolis–Hastings algorithm as a tool for estimating distributions for key parameters in the model. The results provide a means of indicating the uncertainty bounds around key behavioral parameters when generating forecasts of the effect of certain policies. However, the results also indicate that this approach may have limited applicability, given that future available technologies may differ substantially from past technologies and that it is difficult to separate the effects of parameter uncertainty from model structure uncertainty.     

Transport of NO<Subscript>X</Subscript> Emissions from Sugarcane Fertilisation into the Great Barrier Reef Lagoon

The Great Barrier Reef World Heritage Area contains highly sensitive ecosystems that are threatened by the effects of anthropogenic activity including eutrophication. The nearby sugarcane plantations of tropical north Queensland are fertilised annually and there has been ongoing concern about the magnitude of the loss of applied nitrogen to the environment. Previous studies have considered the potential of rainwater run-off to deposit reactive nitrogen species into rivers and ultimately into the Great Barrier Reef Lagoon, but have neglected the possibility of transport via the atmosphere. This paper reports the results of a modelling study commissioned by Australia’s National Heritage Trust aimed at assessing whether or not atmospheric deposition of reactive nitrogen from Queensland’s sugarcane plantations posed a potential threat to the Great Barrier Reef Lagoon. Atmospheric dispersion modelling was undertaken using The Air Pollution Model, developed by Australia’s Commonwealth Scientific and Industrial Research Organisation. Despite the predominance of onshore southeasterly winds, the dispersion model results indicate that 9% of the time during the sugarcane fertilization season (in the modeled years 2001–2006) the meteorological conditions resulted in emissions from the coastal regions of north Queensland being transported out over the ocean around the Great Barrier Reef. The results suggest that there may be a greater efficiency for transport out over the reef during October than for November and December. For the 2 months that exhibited the greatest potential for transport of coastal pollution to the Great Barrier Reef, the modeled deposition of nitrogen oxides (NO_X) into the Great Barrier Reef lagoon was less than 1% of the total emissions from the sugarcane plantations, but was not zero. Our model has a simple chemical scheme that does not cover the full chemistry of all reactive nitrogen compounds and so the results are only indicative of the potential levels of deposition. Nevertheless, our study shows that small amounts of NO_X that originate from sugarcane fertilization may be transported and dry deposited into the Great Barrier Reef lagoon. Other pathways not included in the modeling scheme may provide a more efficient transport mechanism. Whilst modern practices for the application of fertilizer to sugarcane plantations have drastically reduced emissions, the potential efficiency of transport of pollutants via the atmosphere may be of concern for other more highly polluting agricultural industries.     

The application of satellite data for the quantification of mangrove loss and coastal management in the Godavari estuary, East Coast of India

The mangrove formations of Godavari estuary are due to silting over many centuries. The estuary covers an area of 62,000 ha of which dense Coringa mangrove forest spread in 6,600 ha. Satellite sensor data was used to detect change in the mangrove cover for a period of 12 years (1992-2004). It was found that an area of about 1,250 ha of mangroves was destroyed by anthropogenic interference like aquaculture, and tree felling etc. It was found that mangrove's spectral response/digital number (DN) value is much lower than non-mangrove vegetation such as plantation and paddy fields in SWIR band. By taking this as an advantage, spectral data was utilized for clear demarcation of mangroves from nearby paddy fields and other vegetation. Simpson's diversity index, which is a measure of biodiversity, was found to be 0.09, showing mangroves dominance. Ecological parameters like mud-flats/swamps, mangrove cover alterations, and biodiversity status are studied in detail for a period of 12 years. The increase in mangrove front towards coast was delineated using remote sensing data. The major advantages of remote sensing data is monitoring of change periodically. The combination of moderate and high-resolution data provided detailed coastal land use maps for implementing coastal regulation measures. The classification accuracy has been achieved is 90%. Overall, simple and viable measures are suggested based on multi-spectral data to sustain this sensitive coastal ecology.     

空气质量数值模型的构建及应用研究进展

综述了近年来国内外空气质量模型的发展历程、空气质量数值模型构建的关键技术以及应用研究。指出了目前空气质量数值预报及应用主要面临气象条件,尤其是大气边界层模拟、大气污染物排放源和大气物理化学过程等问题。提出,应当通过规范化制作和完善排放源清单数据,建立统一的排放源分析标准,提高排放源数据的准确性;多向发展观测手段,加大监测密度和频率,并进行实验室化学分析,提出适合我国的大气物理化学机制。就空气质量模式而言,对模式方案进行优化,以及使用气象、卫星资料同化等技术手段,将其与观测相结合,构建监测与预报系统相结合的统一体系,应用于多平台。     

Examination of compensatory model application in site selection

Nowadays, usage of compensatory models in land evaluation process and site selection is broadly observed. Meanwhile, methods such as analytical hierarchy process are used more than the others. Whether the usage of compensatory model methods in site selection studies is essentially correct or not is an issue that should be hesitated. In this model, inefficiency of a factor, to be offset by strength of others, means that in urban development site selection, if somewhere has unsuitable aspect but suitable slope, the aspect deficiency will be compensated by the slope. But quodlibet reflection is that in this model, sometimes, deficiency of proximity to fault can be compensated by other parameters strength. And finally, a place is diagnosed as a suitable area for urban development that is close to the fault. This research aims at examination of compensatory models application in urban development site selection of Taleghan Basin using analytical hierarchy process as one of the compensatory model methods. Among this the weaknesses and strengths of compensatory model were analyzed carefully. Results indicate that despite usefulness of this model in being fast, easy, and low expense, it has some weaknesses like very high sensitivity to decision maker’s idea.     

基于深度学习的人工智能空气质量预报系统构建

针对现有空气质量预报系统存在预报精度低、人工经验辅助、适用范围单一等问题,利用深度学习方法在分析数据内在特征方面表现出的优异性能,结合多源数据融合技术,设计了基于深度学习的空气质量预报系统实现方案.通过对多源数据集的实时制作更新、分析空气质量演变的时空特性、定义和拟合深度学习模型并部署于服务器等关键技术的研究,最终实现...     

Coastal dynamic and shoreline mapping: multi-sources spatial data analysis in Semarang Indonesia

Semarang coastal area has geomorphologically complex processes, such as erosion-sedimentation, land subsidence, and tidal inundation hazard. Multi-years shoreline mapping is considered a valuable task for coastal monitoring and assessment. This paper presents maps illustrating the shoreline dynamic in a coastal area of Semarang-Indonesia using multi-sources spatial data. The segment data has been obtained by visual delineation of the topographic maps Year 1908, 1937, 1992 and Ikonos image Year 2003 as well as digital number (DN) value analysis and masking operation of Landsat MSS Year 1972 and Landsat ETM Year 2001. For the long period of almost 100 year, the shoreline dynamic in Semarang coastal area is dominated by sedimentation process. Shoreline extended to the sea as a result of man-made infrastructure and natural processes. The research's result was satisfactory and the method has proven to be effective considering lack of homogeneous data-series. However, some further improvement regarding geo-processing can be made and the accuracy can be tested in future version.