1,720,958 research outputs found
Optimization of operation rule curves and flushing schedule in a reservoir
No full textFlushing sediment through a reservoir has been practiced successfully and found to be inexpensive in many cases. However, the great amount of water consumed in the flushing operation might affect the water supply. To satisfy the water demand and water consumed in the flushing operation, two models combining the reservoir simulation model and the sediment flushing model are established. In the reservoir simulation model, the genetic algorithm (GA) is used to optimize and determine the flushing operation rule curves. The sediment-flushing model is developed to estimate the amount of the flushed sediment volume, and the simulated results update the elevation-storage curve, which can be taken into account in the reservoir simulation model. The models are successfully applied to the Tapu reservoir, which has faced serious sedimentation problems. Based on 36 years historical sequential data, the results show that (i) the simulated flushing operation rule curves model has superior performance, in terms of lower shortage index (SI) and higher flushing efficiency (FE), than that by the original reservoir operation; (ii) the rational and riskless flushing schedule for the Tapu reservoir is suggested to be set within an interval of every 2 or 4 years in the months of May or June
A Study of Artificial Intelligence Techniques for the Estimation of the Arsenic Variation in the Regional Groundwater System
No full text人工智慧廣泛應用於水文系統中，提升水文量預測及推估準確性，但是鮮少案例應用於地下水水質推估，而地下水水質具有污染不易察覺、變異性大、影響因子不確定、易受到周邊水域環境影響及資料取得不易等特性，一般傳統模式難以推估，另一方面，砷物質存在於地層中，已被證實是造成烏腳病主要原因，對人體健康危害相當嚴重，實有必要建立可靠地下水中砷濃度推估模式，掌握地下水中砷污染情形，因此，本研究主要目的為應用類神經網路模式推估地下水中砷濃度變化。
本研究以地下水受嚴重砷污染之台灣西部雲林縣沿海地區為研究區域，並採用水利署1992年至2005年設置於本區28座監測井之水質資料為分析對象，地下水水質採樣期間，因經費或人為因素，部份監測井停止採樣或資料缺漏，影響後續對地下水中砷污染擴散機制之瞭解，為補遺本區監測井之砷濃度資料，本研究第一部分採用倒傳遞類神經網路，建立空間模式補遺地下水中砷濃度資料，在建立模式過程中遭遇到資料過少，模式過度訓練問題，應用主成份分析、交叉驗證法及修正型目標函數加以改善，有效提升模式推估精確度；另考量在尋求最佳推估模式架構及參數過程中，由於模式架構不確定與參數過多需耗用大量優選時間，且無法獲得可最佳模式，故本研究應用遺傳演算法之強大搜尋能力優選最佳模式之架構，其中優選項目包括輸入層因子、隱藏層之神經元個數及修正型目標函數之係數，成功解決模式之架構與參數不確定問題。
本研究之第二部份，主要考量砷在地下水環境中受到多種水質因子影響，故深入探討砷與其他地下水水質間之相關性，從而利用水質因子建立地下水中砷濃度之推估模式，模式分為單一水井模式及區域模式，整體而言，以單一水井模式推估結果較佳，但是，區域模式應用範圍較廣，可展現區域地下水中濃度特性，最後，本研究將觀測與本模式推估之地下水中砷變化結果繪出地下水中砷污染潛勢圖，展現本研究區域內1992至2005年砷濃度在時間與空間變化，提供政府單位與相關研究者了解地下水中砷變化情形及傳輸機制，有效減少居民誤飲用高砷地下水之風險，達到有效管理及利用地下水之目的Artificial intelligence is extensively applied to hydrological systems and is successfully implemented in the quantitative estimation of water quality. However, artificial intelligence techniques are seldom employed in the prediction of groundwater quality. The features of the groundwater pollution include imperceptibility, complex affective factors and limited data. It is not easy to employee traditional models for estimating the water quality in groundwater systems. Arsenic (As) proves to be a main factor of black-foot disease and threatens the health of residents. Constructing a reliable model for estimating arsenic concentration in groundwater is essential. Therefore, the aim of this study is to construct an artificial neural network (ANN) model for estimating arsenic concentration in groundwater systems.
From 1992 to 2005, the government takes into account the serious arsenic pollution that occurred in the coastal area of the Yun-Lin County in Taiwan and set up 28 monitoring wells for investigating the pollution in groundwater. The collected water quality data were used when constructing models in this study. However, due to limited budget and/or human factors, some arsenic concentration data from these wells were missing, which affects the realization of the pollution in groundwater. The first subject of this study is to construct a spatial model for estimating missing data by applying ANN. During the process of model construction, inaccuracy and over-fitting commonly occur in sparse data. To overcome these problems, the principal component analysis, the cross-validation and the modified performance function are employed when constructing the model. These methods have the ability to effectively alleviate the over-fitting problem and improve model accuracy. On the other hand, searching and identifying the optimal ANN structure is quite time and labor consuming. Genetic algorithm is used to identify the effective input factors and the suitable number of neurons in hidden layer.
Another subject of this study is to build a water quality assessment model for arsenic concentration by analyzing the relationship between arsenic concentration and other water quality factors in groundwater. This subject has two scenarios: one is for the single well model; and the other is for the regional model. Results indicate that the affective factors of arsenic concentration significantly vary from the north to the south in the coastal area of the Yun-Lin County. Overall, the single well model performs better than the regional model, despite that the regional model can be extensively applied over the study area. Finally, the results of the spatial and water quality models are applied to displaying the distribution map of arsenic pollution so that groundwater managers can easily realize the temporal and spatial variation in arsenic concentration during 1992 and 2005. The information of arsenic variation can reduce the risk of drinking contaminated groundwater for local residents and effectively enhance the control and management of arsenic pollution in groundwater
Artificial neural networks for estimating regional arsenic concentrations in a blackfoot disease area in Taiwan
No full textHigh arsenic concentrations in groundwater have been detected in the south-western coastal area of Taiwan. In this study, artificial neural networks (ANNs) were investigated for their applicability to recovering the missing arsenic data and constructing the spatial distribution of arsenic concentration based on the arsenic concentration data of 28 groundwater observation wells. Due to a limited number of data sets, several strategies were proposed to construct the backpropagation neural networks (BPNs). The leave-one-out (LOO) cross-validation was adopted to diminish the bias in choosing validation data, and the modified performance function (MPF) was applied to reducing an over-fitting situation. Principal component analysis (PCA) was employed to transform the arsenic concentration of the regional wells into a limited number of main factors that were used as the input variables for the ANNs. Results showed that the LOO cross-validation was an effective tool for model selection, and the parameter, γ, of MPF played an important role for reducing errors in the model training and validation processes and alleviating the problem of over-fitting. Although sparse data sets have been used to construct ANNs, the models still achieved acceptable performance. The predicted spatial distribution of the arsenic concentration can provide useful information to local residents when groundwater achieves high levels of arsenic concentrations in non-functioning groundwater monitoring wells
Applying ANNs for Estimating the Regional Arsenic Pollution in Groundwater
No full text本研究中應用類神經網路建立地下水砷濃度推估模式，以解決高度非線性砷汙染傳輸問題，及提高砷濃度推估之準確性。本研究以雲林縣沿海地區為研究區域，採用類神經網路建構地下水中砷推估模式，模式分為單一水井水質及區域水井水質之類神經網路模式，單一水井水質類神經網路模式主要針對單一監測井藉由砷與其他水質因子相關性，建立類神經網路推估模式，區域水井水質類神經網路模式則是應用全區資料，建立適用研究區域範圍之砷濃度推估模式，研究中除探討輸入因子及網路架構對模式誤差之影響外，並針對地下水水質模式較易遭遇到資料過少問題，提出交叉驗證法及修正型目標函數加以改善模式推估誤差，其中單一水井水質類神經網路模式之平均誤差 (rmse) 為 65.7 ug/l，區域水井水質類神經網路模式之平均誤差 (rmse) 為 112 ug/l，大部分監測井推估結果屬可接受範圍，僅#7監測井誤差較大，但對於本區砷變動較大且複雜地區，而監測資料有限下，藉由類神經網路達到可接受誤差，本模式成功解決過去傳統模式不易推估區域地下水中砷污染問題。最後，本研究將模式推估結果結合地理資訊系統 (GIS) 展示雲林縣沿海地區地下水中砷污染分布情形，可作為日後政府管理地下水之參考依據。
The groundwater extracted by some regional farmers leads to a lower level of the groundwater and a release of the poisonous substance in the groundwater. That affects the health of local residents, even the inhabitants who do not ingest the local agriculture and aquaculture products. The aim of the study is to build the arsenic water quality model by adopting the artificial neural networks (ANNs). Taking the YUN-LIN County as an example, the ANNs were constructed and assessed. The models are divided into two parts: (1) single well models and (2) regional well models. In the process of constructing the models, the optimal input factors and structures of the ANN models were discussed in this study. At the same time, we applied the cross validation method and the modified objective function to solving the data scarce problems of the monitoring wells. The results produced by the single well models and the regional well models were compared and demonstrated their applicability. Finally, the results obtained by the ANN models were integrated with GIS to display the distribution of the arsenic concentration at the coastal area in the YUN-LIN County. The results can offer a good reference to government decision-makers for the management of the groundwater and installation of monitoring wells
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Artificial Neural Networks for Estimating Arsenic Variation in the Regional Ground Water
No full text致癌毒物－砷對環境與人類健康之影響係為社會所關切的議題，本研究架構砷濃度類神經網路推估模式，對區域內砷濃度進行推估工作，提供區域內砷濃度變化，以維護當地民眾使用地下水之安全。台灣西南沿海地區有嚴重砷污染地下水情形，水利署於民國81年至94年間於雲林沿海地區，共設置28口水質監測井，蒐集此區域水質資料作為架構模式之用，在過程中發現本區域雖有長期水質監測資料，惟資料時常發生零星遺漏情形，為解決此問題，本研究以雲林沿海地區為研究範例，架構類神經網路模式推估各水質站內遺漏砷濃度資料，並對沿海地區進行區域砷濃度推估工作。另一方面，由於本區域內水質站之資料不足，造成模式之穩定性不佳，無法建構可靠補遺推估模式，為改善此問題，首先採用交叉驗證以確認模式架構後，並採修正型目標函數搜尋模式中參數，其結果證明此兩種方法可改善模式不穩定性及過度訓練情況，擴大類神經網路之用範圍及應用領域，提供可靠砷濃度空間推估結果，對於了解此區域內砷濃度在時間與空間變化有很大助益，同時依據此結果可減少居民誤引用高濃度砷地下水之危險，達地下水有效管理及利用之目的。
With the great concern for the potential effects of aresenic on human health and the environment, there is a growing need for efficiently determining and modeling the presence and amount of aresenic in the ecohydrogeological systems. In this study,we construct the ANNs model to complete the lost aresenic data according to the relationship of the aresenic concentration of the monitoring wells in the region. The results offer the realization for the aresenic variation and keep the safe from the resident ingesting and usage of the groundwater. Blackfoot disease was once common the southwestern coast of Taiwan, especially in the alluvial fan of Chou-Shui River. In order to monitoring the aresenic concentration, the Water Resource Agency has setup twenty-eight water quality wells which distribute in coasltal area in Yun-Lin county from 1992 to 2005. However, some water quality data were lost or not record because of the man-made amd money factors. The lost data affect the development of the arsenic associate research. Therefore, we choose this region as the example to construct ANNs model to estimate the lost aresenic data.
Due to sparse and lost data, there are not steady and over-fitting problem in the process of constructing the ANNs model. To solve the problems, we first apply cross-validation to assure the architecture of the model amd adopt the modify-objective function to search the optimal weights. It is proven that these two methods can reduce the unsteady and over-fitting problems. The results have been apparently improved the realization of the spatial-temporal distribution of aresenic. Based on the results, the risk of ingesting the high aresenic groundwater can be decreased to reach the goal of efficiently controlling and usage of the groundwater
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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