Model-Based Clustering of Nonparametric Weighted Networks With Application to Water Pollution Analysis

Agarwal, Amal; Xue, Lingzhou

doi:10.1080/00401706.2019.1623076

Cited by 18 publications

(18 citation statements)

References 67 publications

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“…GeoNet detects changes in stream chemistry in complex stream networks using a new technique 25 (see also SI). Source codes are available at GitHub (https://github.com/amalag-19/ GeoNet_Methodology).…”

Section: ■ Methodology and Datamentioning

confidence: 99%

“…We previously reported a new algorithm for scalable river network-based assessment that completes a multistep statistical analysis over stream chemistry data and solves significant statistical challenges in terms of stream network analysis. 25 Sampling sites are clustered based on probability models over weighted river network systems. Although the tool (i.e., GeoNet) cannot yet be used to discover unknown spill incidents automatically, we show a first step in that direction here by demonstrating its utility in detecting spills in PA that have already been reported.…”

Section: ■ Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assessing Contamination of Stream Networks near Shale Gas Development Using a New Geospatial Tool

Agarwal

Wen

Chen

et al. 2020

Environ. Sci. Technol.

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Chemical spills in streams can impact ecosystem or human health. Typically, the public learns of spills from reports from industry, media, or government rather than monitoring data. For example, ∼1300 spills (76 ≥ 400 gallons or ∼1500 L) were reported from 2007 to 2014 by the regulator for natural gas wellpads in the Marcellus shale region of Pennsylvania (U.S.), a region of extensive drilling and hydraulic fracturing. Only one such incident of stream contamination in Pennsylvania has been documented with water quality data in peer-reviewed literature. This could indicate that spills (1) were small or contained on wellpads, (2) were diluted, biodegraded, or obscured by other contaminants, (3) were not detected because of sparse monitoring, or (4) were not detected because of the difficulties of inspecting data for complex stream networks. As a first step in addressing the last problem, we developed a geospatial-analysis tool, GeoNet, that analyzes stream networks to detect statistically significant changes between background and potentially impacted sites. GeoNet was used on data in the Water Quality Portal for the Pennsylvania Marcellus region. With the most stringent statistical tests, GeoNet detected 0.2% to 2% of the known contamination incidents (Na ± Cl) in streams. With denser sensor networks, tools like GeoNet could allow real-time detection of polluting events.

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Section: ■ Methodology and Datamentioning

confidence: 99%

Section: ■ Introductionmentioning

confidence: 99%

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Assessing Contamination of Stream Networks near Shale Gas Development Using a New Geospatial Tool

Agarwal

Wen

Chen

et al. 2020

Environ. Sci. Technol.

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“…Next, to update the varying network parameters

bold-italicθ false(\cdot false)

, we localize the log‐likelihood function at a sequence of grid points u (Agarwal & Xue, 2020) and derive the corresponding localized lower bound as

\begin{matrix} right LB (θ^{u}; \tilde{π}, \tilde{Γ}) = & left {falsefalse}_{\sum}^{t = 1} [{falsefalse}_{\sum}^{i < j} {falsefalse}_{\sum}^{k = 1} {falsefalse}_{\sum}^{l = 1} {\tilde{γ}}_{i k} {\tilde{γ}}_{j l} \log P_{θ^{u}} (Y_{t, i j} = y_{t, i j} | y_{t - 1}, z)] K_{h} (t - u) \\ right & left + {falsefalse}_{\sum}^{t = 1} [{falsefalse}_{\sum}^{i = 1} {falsefalse}_{\sum}^{k = 1} {\tilde{γ}}_{i k} (\log {\tilde{π}}_{k} - \log {\tilde{γ}}_{i k})] K_{h} (t - u) . \end{matrix}

where

K_{h} false(t - u false) =

…”

Section: Computationmentioning

confidence: 99%

Model‐based clustering of semiparametric temporal exponential‐family random graph models

Lee

Agarwal

Zhang

et al. 2022

Stat

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Model‐based clustering of time‐evolving networks has emerged as one of the important research topics in statistical network analysis. It is a fundamental research question to model time‐varying network parameters. However, due to difficulties in modelling functional network parameters, there is little progress in the current literature to model time‐varying network parameters effectively. In this work, we model network parameters as univariate nonparametric functions instead of constants. We effectively estimate those functional network parameters in temporal exponential‐family random graph models using a kernel regression technique and a local likelihood approach. Furthermore, we propose a semiparametric finite mixture of temporal exponential‐family random graph models by adopting finite mixture models, which simultaneously allows both modelling and detecting groups in time‐evolving networks. Also, we use a conditional likelihood to construct an effective model selection criterion and network cross‐validation to choose an optimal bandwidth. The power of our method is demonstrated in simulation studies and real‐world applications to dynamic international trade networks and dynamic arm trade networks.

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“…Water environment monitoring mainly uses two means of static monitoring and dynamic monitoring. By establishing a basin water environment monitoring system as well as dynamic monitoring and real-time monitoring of the basin water environment, the efficiency of basin water environment monitoring and the management of water pollution sources can be effectively improved [1][2].…”

Section: Introductionmentioning

confidence: 99%

Water Pollution Source Management Resources Incorporating Microscopy Images and Clustering Algorithms

2021

WPPCP

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The main source of water resources in China is agricultural irrigation. With the increase of agricultural scale operation and agricultural mechanization, agricultural irrigation and water resources protection have become one of the main causes of current water environment pollution problems. Traditional water pollution source management methods have serious problems of resource waste and inefficiency. The main purpose of this paper is to integrate microscopic technology images and clustering algorithms to conduct research on the construction of water pollution source management resources. In this paper, we use microscopy technology and clustering algorithm to fuse high-resolution images with GIS data to classify water classes, land and rivers in the images into classes and construct a comprehensive remediation resource construction plan for polluted rivers. The results show that the water pollution source management resource construction scheme based on microscopic technology image and clustering algorithm can significantly improve the efficiency of water resources monitoring; at the same time, it can realize the use of higher resolution land water quality monitoring resources; realize the low investment and operation cost of water pollution source management.

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Model-Based Clustering of Nonparametric Weighted Networks With Application to Water Pollution Analysis

Cited by 18 publications

References 67 publications

Assessing Contamination of Stream Networks near Shale Gas Development Using a New Geospatial Tool

Assessing Contamination of Stream Networks near Shale Gas Development Using a New Geospatial Tool

Model‐based clustering of semiparametric temporal exponential‐family random graph models

Water Pollution Source Management Resources Incorporating Microscopy Images and Clustering Algorithms

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