Rainfall Network Optimization Using Radar and Entropy

Yeh, Hui‐Chung; Chen, Yen‐Chang; Chang, Chi-Lung; Ho, Chen Hsun; Wei, Chiang

doi:10.3390/e19100553

Cited by 24 publications

(15 citation statements)

References 29 publications

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“…Previous rain-measuring station network layout designs primarily divided the whole research area according to a certain grid and then considered all grids as potential candidate stations directly [22,43], or manually selected individual grids as potential points according to the kriging interpolation accuracy (kriging variance) and other constraints [32,73]. The former requires considerable redundant calculations, especially for large study areas, and data with high spatial and temporal resolution; thus, this method is often used with radar data [43]. The latter manual screening method selects relatively fewer potential sites; thus, it is not conducive to the overall planning of a regional network.…”

Section: Filtering Potential Stationsmentioning

confidence: 99%

“…Satellite remote sensing products appear to be suitable for the design of rain gauge networks in order to generate better rainfall forecasts, flood range forecasts, and water cycle simulations. Attempts have been made to apply remote sensing data instead of measurement data or model simulation data for network layouts using information entropy and statistical methods [2,42,43]. However, because satellite remote sensing data may present problems with low resolution and uncertainty, relying solely on remote sensing data to evaluate the accuracy of a network is inappropriate [42].…”

Section: Introductionmentioning

confidence: 99%

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A Method for the Optimized Design of a Rain Gauge Network Combined with Satellite Remote Sensing Data

et al. 2020

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A well-designed rain gauge network can provide precise and detailed rainfall data for earth science research; meanwhile, satellite precipitation data has been developed to generate more real spatial features, which provides new data support for the improvement of ground station network design methods. In this paper, satellite precipitation data are introduced into the design of a rain gauge network and an optimized method for designing a rain gauge network that comprehensively considers the information content, spatiotemporality, and accuracy (ISA) of the data is proposed. After screening the potential stations, the average spatial information index of the rain gauge network, which is calculated from remote sensing data, is used to address the shortcomings of applying spatial information from single-use measurement data. Then, the greedy ranking algorithm is used to rank the order in which the rain gauges are added to the network. The results of the rain gauge network design in the upper reaches of the Chaobai river show that compared with two methods that do not consider spatiality or use only measured data to consider spatiality, the proposed method performs better in terms of the spatial layout and accuracy verification. This study provides new ideas and references for the design of hydrological station networks and explores the use of remote sensing data for the layout of ground-based station networks.

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Section: Filtering Potential Stationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Method for the Optimized Design of a Rain Gauge Network Combined with Satellite Remote Sensing Data

et al. 2020

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“…A school can be regarded to be a complex system in an educational setting, and entropy is abound in every complex system [3]. If researchers could predict conditions of complex weather systems by utilizing the concept of entropy [4][5][6][7], wouldn't it also be possible to harness entropy to work for educational stakeholders to predict conditions and outcomes in the future? Specifically, "would it be possible to predict conditions that could enhance student performance, when there could be dynamic confounding factors with parameters that could change?"…”

Section: Research Problem and Research Questionsmentioning

confidence: 99%

Harnessing Entropy via Predictive Analytics to Optimize Outcomes in the Pedagogical System: An Artificial Intelligence-Based Bayesian Networks Approach

How

Hung

2019

Education Sciences

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Educational stakeholders would be better informed if they could use their students’ formative assessments results and personal background attributes to predict the conditions for achieving favorable learning outcomes, and conversely, to gain awareness of the “at-risk” signals to prevent unfavorable or worst-case scenarios from happening. It remains, however, quite challenging to simulate predictive counterfactual scenarios and their outcomes, especially if the sample size is small, or if a baseline control group is unavailable. To overcome these constraints, the current paper proffers a Bayesian Networks approach to visualize the dynamics of the spread of “energy” within a pedagogical system, so that educational stakeholders, rather than computer scientists, can also harness entropy to work for them. The paper uses descriptive analytics to investigate “what has already happened?” in the collected data, followed by predictive analytics with controllable parameters to simulate outcomes of “what-if?” scenarios in the experimental Bayesian Network computational model to visualize how effects spread when interventions are applied. The conceptual framework and analytical procedures in this paper could be implemented using Bayesian Networks software, so that educational researchers and stakeholders would be able to use their own schools’ data and produce findings to inform and advance their practice.

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“…It has been widely used in different sectors of hydraulics and hydrology to derive models of rainfall-runoff, infiltration, and soil moisture [22][23][24][25][26][27] as well as distribution of velocity, sediment concentration, and shear stress in open-channel flows [28][29][30][31][32][33][34][35][36][37][38][39]. Among the different applications, information theory has also been employed for the optimization, design, and management of several gauge stations including networks of water quality and groundwater [40,41], rainfall [42,43], streamflow, and water level [44][45][46][47][48][49][50][51]. These problems can be solved through a multi-objective optimization approach, in which the repetitive information is minimized whilst the total information is maximized.…”

Section: Introductionmentioning

confidence: 99%

MIMR Criterion Application: Entropy Approach to Select the Optimal Quality Parameter Set Responsible for River Pollution

Mirauda

Ostoich

2020

Sustainability

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Surface water quality has a vital role when defining the sustainability of the ecological environment, public health, and the social and economic development of whole countries. Unfortunately, the rapid growth of the worldwide population together with the current climate change have mostly determined fluvial pollution. Therefore, the employment of effective methodologies, able to rapidly and easily obtain reliable information on the quality of rivers, is becoming fundamental for an efficient use of the resource and for the implementation of mitigation measures and actions. The Water Quality Index (WQI) is among the most widely used methods to provide a clear and complete picture of the contamination status of a river stressed by point and diffuse sources of natural and anthropic origin, leading the policy makers and end-users towards a more and more correct and sustainable management of the water resource. The parameter choice is one of the most important and complex phases and recent statistical techniques do not seem to show great objectivity and accuracy in the identification of the real water quality status. The present paper offers a new approach, based on entropy theory and known as the Maximum Information Minimum Redundancy (MIMR) criterion, to define the optimal subset of chemical, physical, and biological parameters, describing the variation of the river quality level in space and time and thus identifying its pollution sources. An algorithm was implemented for the MIMR criterion and applied to a sample basin of Northeast Italy in order to verify its reliability and accuracy. A comparison with the Principal Component Analysis (PCA) showed how the MIMR is more suitable and objective to obtain the optimal quality parameters set, especially when the amount of investigated variables is small, and can thus be a useful tool for fast and low-cost water quality assessment in rivers.

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Rainfall Network Optimization Using Radar and Entropy

Cited by 24 publications

References 29 publications

A Method for the Optimized Design of a Rain Gauge Network Combined with Satellite Remote Sensing Data

A Method for the Optimized Design of a Rain Gauge Network Combined with Satellite Remote Sensing Data

Harnessing Entropy via Predictive Analytics to Optimize Outcomes in the Pedagogical System: An Artificial Intelligence-Based Bayesian Networks Approach

MIMR Criterion Application: Entropy Approach to Select the Optimal Quality Parameter Set Responsible for River Pollution

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