427 research outputs found

    Selection of representative feature training sets with self-organized maps for optimized time series modeling and prediction: application to forecasting daily drought conditions with ARIMA and neural network models

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    While the simulation of stochastic time series is challenging due to their inherently complex nature, this is compounded by the arbitrary and widely accepted feature data usage methods frequently applied during the model development phase. A pertinent context where these practices are reflected is in the forecasting of drought events. This chapter considers optimization of feature data usage by sampling daily data sets via self-organizing maps to select representative training and testing subsets and accordingly, improve the performance of effective drought index (EDI) prediction models. The effect would be observed through a comparison of artificial neural network (ANN) and an autoregressive integrated moving average (ARIMA) models incorporating the SOM approach through an inspection of commonly used performance indices for the city of Brisbane. This study shows that SOM-ANN ensemble models demonstrate competitive predictive performance for EDI values to those produced by ARIMA models

    The Drought Risk Analysis, Forecasting, and Assessment under Climate Change

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    This Special Issue is a platform to fill the gaps in drought risk analysis with field experience and expertise. It covers (1) robust index development for effective drought monitoring; (2) risk analysis framework development and early warning systems; (3) impact investigations on hydrological and agricultural sectors; (4) environmental change impact analyses. The articles in the Special Issue cover a wide geographic range, across China, Taiwan, Korea, and the Indo-China peninsula, which covers many contrasting climate conditions. Hence, the results have global implications: the data, analysis/modeling, methodologies, and conclusions lay a solid foundation for enhancing our scientific knowledge of drought mechanisms and relationships to various environmental conditions

    A New Weighting Scheme in Weighted Markov Model for Predicting the Probability of Drought Episodes

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    Drought is a complex stochastic natural hazard caused by prolonged shortage of rainfall. Several environmental factors are involved in determining drought classes at the specific monitoring station. Therefore, efficient sequence processing techniques are required to explore and predict the periodic information about the various episodes of drought classes. In this study, we proposed a new weighting scheme to predict the probability of various drought classes under Weighted Markov Chain (WMC) model. We provide a standardized scheme of weights for ordinal sequences of drought classifications by normalizing squared weighted Cohen Kappa. Illustrations of the proposed scheme are given by including temporal ordinal data on drought classes determined by the standardized precipitation temperature index (SPTI). Experimental results show that the proposed weighting scheme for WMC model is sufficiently flexible to address actual changes in drought classifications by restructuring the transient behavior of a Markov chain. In summary, this paper proposes a new weighting scheme to improve the accuracy of the WMC, specifically in the field of hydrology

    Latihan mengajar : Keberkesanannya terhadap pelajar Diploma Kejuruteraan serta Pendidikan di KUiTTHO (Kolej Universiti Teknologi Tun Hussein Onn) menurut persepsi pelajar

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    Kajian yang dijaiankan adaiah bertajuk "Latihan Mengajar : Kebersanannya Terhadap Pelajar Diploma Kejuruteraan berserta Pendidikan di KUiTTHO (Kolej Universiti Teknologi Tun Hussein Onn Menurut Persepsi Pelajar. Kajian ini bertujuan untuk meiihat sejauhmana keberkesanan program latihan mengajar terhadap pelajar yang telah melaluinya. Borang soalselidik diedarkan untuk mendapatkan maklumat dan seterusnya dianalisis untuk menghasilkan skor min dan peratusan. Hasil kajian menunjukkan kebanyakan responden memberikan reaksi positif terhadap keberkesanan program latihan mengajar. Hasil dari anaiisis kajian juga, pengkaji telah menghasilkan sebuah produk iaitu senarai semak yang boleh digunakan oleh pelajar yang akan menjalani program latihan mengajar supaya pelajar jelas dengan tindakan yang harus mereka ambil sebeium, semasa dan selepas menjalani latihan mengajar. Adaiah diharapkan agar produk ini dapat membantu untuk pelajar, pihak KUiTTHO dan seterusnya institusi tempat latihan mengajar supaya program ini dapat dilaksanakan dengan Iebih sempuma dan seterusnya mencapai objektif program latihan mengajar

    Satellite Data and Supervised Learning to Prevent Impact of Drought on Crop Production: Meteorological Drought

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    Reiterated and extreme weather events pose challenges for the agricultural sector. The convergence of remote sensing and supervised learning (SL) can generate solutions for the problems arising from climate change. SL methods build from a training set a function that maps a set of variables to an output. This function can be used to predict new examples. Because they are nonparametric, these methods can mine large quantities of satellite data to capture the relationship between climate variables and crops, or successfully replace autoregressive integrated moving average (ARIMA) models to forecast the weather. Agricultural indices (AIs) reflecting the soil water conditions that influence crop conditions are costly to monitor in terms of time and resources. So, under certain circumstances, meteorological indices can be used as substitutes for AIs. We discuss meteorological indexes and review SL approaches that are suitable for predicting drought based on historical satellite data. We also include some illustrative case studies. Finally, we will survey rainfall products existing at the web and some alternatives to process the data: from high-performance computing systems able to process terabyte-scale datasets to open source software enabling the use of personal computers

    A Statistical Analysis of Drought and Its Global Impact

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    Droughts are the most ambiguous of all natural hazards and yet are often cited as the most destructive and are responsible for the most widespread damage across all sectors of society. The purpose of this study was to further understand the impact that drought has on various sectors of society, especially the economic sector, and how various regions across the United States are specifically impacted by droughts and drought effects. In order to quantify the impact that drought has on the economic sector, an analysis was performed internationally between each country’s GDP and various drought indices such as PDSI, SPI, and SPEI. In order to account for exponential growth in GDP, the correlation was performed on detrended GDP using logarithmic trend free pre-whitening (TFPW) and logarithmic quadratic methods. The combination of PDSI and Log. TFPW gave the most complete understanding of negative correlation between drought and a nation’s economy. In order to focus on drought impact in the United States, ARIMA modeling was used to establish a forecasting model for PDSI time series for various climatic regions around the country. The accuracy of these forecasting models was quantified through an approximate AIC method and compared to precipitation and temperature of each of the regions to determine the influence each drought component had on model accuracy. The regions with lower temperatures such as the Upper Midwest gave the more accurate drought forecasting models. The applicability of each of these climatic regions towards drought studies were tested by Severity Area Frequency curve analysis. While the Northwest region of America necessitated a need for two drought sub-regions, most of the climatic regions were affected by droughts homogenously

    Methodologies for transforming data to information and advancing the understanding of water resources systems towards integrated water resources management

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    2017 Summer.Includes bibliographical references.The majority of river basins in the world, have undergone a great deal of transformations in terms of infrastructure and water management practices in order to meet increasing water needs due to population growth and socio-economic development. Surface water and groundwater systems are interwoven with environmental and socio-economic ones. The systems' dynamic nature, their complex interlinkages and interdependencies are inducing challenges for integrated water resources management. Informed decision-making process in water resources is deriving from a systematic analysis of the available data with the utilization of tools and models, by examining viable alternatives and their associated tradeoffs under the prism of a set of prudent priorities and expert knowledge. In an era of increasing volume and variety of data about natural and anthropogenic systems, opportunities arise for further enhancing data integration in problem-solving approaches and thus support decision-making for water resources planning and management. Although there is a plethora of variables monitored in various spatial and temporal scales, particularly in the United States, in real life, for water resources applications there are rarely, if ever, perfect data. Developing more systematic procedures to integrate the available data and harness their full potential of generating information, will improve the understanding of water resources systems and assist at the same time integrated water resources management efforts. The overarching objective of this study is to develop tools and approaches to overcome data obstacles in water resources management. This required the development of methodologies that utilize a wide range of water and environmental datasets in order to transform them into reliable and valuable information, which would address unanswered questions about water systems and water management practices, contributing to implementable efforts of integrated water resources management. More specifically, the objectives of this research are targeted in three complementary topics: drought, water demand, and groundwater supply. In this regard, their unified thread is the common quest for integrated river basin management (IRBM) under changing water resources conditions. All proposed methodologies have a common area of application namely the South Platte basin, located within Colorado. The area is characterized by limited water resources with frequent drought intervals. A system's vulnerability to drought due to the different manifestations of the phenomenon (meteorological, agricultural, hydrological, socio-economic and ecological) and the plethora of factors affecting it (precipitation patterns, the supply and demand trends, the socioeconomic background etc.) necessitates an integrated approach for delineating its magnitude and spatiotemporal extent and impacts. Thus, the first objective was to develop an implementable drought management policy tool based on the standardized drought vulnerability index framework and expanding it in order to capture more of drought's multifaceted effects. This study illustrated the advantages of a more transparent data rigorous methodology, which minimizes the need for qualitative information replacing it with a more quantitative one. It is believed that such approach may convey drought information to decision makers in a holistic manner and at the same time avoid the existing practices of broken linkages and fragmentation of reported drought impacts. Secondly, a multi-scale (well, HUC-12, and county level) comparative analysis framework was developed to identify the characteristics of the emergent water demand for unconventional oil and gas development. This effort revealed the importance of local conditions in well development patterns that influence water demand, the magnitude of water consumption in local scales in comparison to other water uses, the strategies of handling flowback water, and the need for additional data, and improved data collection methods for a detailed water life-cycle analysis including the associated tradeoffs. Finally, a novel, easy to implement, and computationally low cost methodology was developed for filling gaps in groundwater level time series. The proposed framework consists of four main components, namely: groundwater level time series; data (groundwater level, recharge and pumping) from a regional physically-based groundwater flow model; autoregressive integrated moving average with external inputs modeling; and the Ensemble Smoother (ES) technique. The methodology's efficacy to predict accurately groundwater levels was tested by conducting three numerical experiments at eighteen alluvial wells. The results suggest that the framework could serve as a valuable tool in gaining further insight of alluvium aquifer dynamics by filling missing groundwater level data in an intermittent or continuous (with relative short span) fashion. Overall, it is believed that this research has important implications in water resources decision making by developing implementable frameworks which advance further the understanding of water systems and may aid in integrated river basin management efforts
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