On the Distributional Characterization of Graph Models of Water Distribution Networks in Wasserstein Spaces

This paper is focused on two topics very relevant in water distribution networks (WDNs): vulnerability assessment and the optimal placement of water quality sensors. The main novelty element of this paper is to represent the data of the problem, in this case all objects in a graph underlying a water distribution network, as discrete probability distributions. For vulnerability (and the related issue of re-silience) the metrics from network theory, widely studied and largely adopted in the water research community, reflect connectivity expressed as closeness centrality or, betweenness centrality based on the average values of shortest paths between all pairs of nodes. Also network efficiency and the related vulnerability measures are related to average of inverse distances. In this paper we propose a different approach based on the discrete probability distribution, for each node, of the node-to-node distances. For the optimal sensor placement, the elements to be represented as dis-crete probability distributions are sub-graphs given by the locations of water quality sensors. The objective functions, detection time and its variance as a proxy of risk, are accordingly represented as a discrete e probability distribution over contamination events. This problem is usually dealt with by EA algorithm. We’ll show that a probabilistic distance, specifically the Wasserstein (WST) distance, can naturally allow an effective formulation of genetic operators. Usually, each node is associated to a scalar real number, in the optimal sensor placement considered in the literature, average detection time, but in many applications, node labels are more naturally expressed as histograms or probability distributions: the water demand at each node is naturally seen as a histogram over the 24 hours cycle. The main aim of this paper is twofold: first to show how different problems in WDNs can take advantage of the representational flexibility inherent in WST spaces. Second how this flexibility translates into computational procedures.

Monitoring Methods of Marine Pollution Range Based on Big Data Technology

With the development of big data technology, traditional monitoring methods for the scope of marine pollution can no longer meet the current needs of accuracy and timeliness. In light of the outstanding topic, this study proposed to use big data technology to monitor the scope of marine pollution. The intelligent digital remote sensing technology was used for multi-dimensional monitoring of ocean water quality and completed the calculation of data collected by water quality sensors through the improved big data comparative analysis method. Finally, the scope of pollution monitoring was realized. The results verified that the proposed monitoring method could achieve high-precision and time-sensitive monitoring of the range of marine pollutants, and could identify the basic information of pollutants.

A Dimensional Comparison between Evolutionary Algorithm and Deep Reinforcement Learning Methodologies for Autonomous Surface Vehicles with Water Quality Sensors

The monitoring of water resources using Autonomous Surface Vehicles with water-quality sensors has been a recent approach due to the advances in unmanned transportation technology. The Ypacaraí Lake, the biggest water resource in Paraguay, suffers from a major contamination problem because of cyanobacteria blooms. In order to supervise the blooms using these on-board sensor modules, a Non-Homogeneous Patrolling Problem (a NP-hard problem) must be solved in a feasible amount of time. A dimensionality study is addressed to compare the most common methodologies, Evolutionary Algorithm and Deep Reinforcement Learning, in different map scales and fleet sizes with changes in the environmental conditions. The results determined that Deep Q-Learning overcomes the evolutionary method in terms of sample-efficiency by 50–70% in higher resolutions. Furthermore, it reacts better than the Evolutionary Algorithm in high space-state actions. In contrast, the evolutionary approach shows a better efficiency in lower resolutions and needs fewer parameters to synthesize robust solutions. This study reveals that Deep Q-learning approaches exceed in efficiency for the Non-Homogeneous Patrolling Problem but with many hyper-parameters involved in the stability and convergence.

Human Health Impact Analysis of Contaminant in IoT-Enabled Water Distributed Networks

This paper aims to assess and analyze the health impact of consuming contaminated drinking water in a water distributed system (WDS). The analysis was based on qualitative simulation performed in two different models named hydraulic and water quality in a WDS. The computation focuses on quantitative analysis for chemically contaminated water impacts by analyzing the dose level in various locations in the water network and the mass of the substance that entered the human body. Several numerical experiments have been applied to evaluate the impact of water pollution on human life. They analyzed the impact on human life according to various factors, including the location of the injected node (pollution occurrence) and the ingested dose level. The results show a significant impact of water contaminant on human life in multiple areas in the water network, and the level of this impact changed from one location to another in WDSs based on several factors such as the location of the pollution occurrence, the contaminant concentration, and the dose level. In order to reduce the impact of this contaminant, water quality sensors have been used and deployed on the water network to help detect this contaminant. The sensors were optimally deployed based on the time-detection of water contamination and the volume of polluted water consumed. Numerical experiments were carried out to compare water pollution’s impact with and without using water quality sensors. The results show that the health impact was reduced by up to 98.37% by using water quality sensors.

Robust Placement of Water Quality Sensor for Long-Distance Water Transfer Projects Based on Multi-Objective Optimization and Uncertainty Analysis

It is important to place water quality sensors along open channels in long-distance water transfer projects optimally for rapid source identification and efficient management of sudden water contamination. A new framework which considers multiple objectives, including earliest detection time, lowest missing detection rate and lowest sensor cost, and combines the randomness of injected contaminant type and contaminant incident consisting of contaminant intrusion location, time and mass, was established to obtain optimal placement of water quality sensor with better robustness in this paper. The middle route of the South-to-North Water Diversion Project in China was chosen as a case study, and it was found that both missing detection rate and detection time decrease with sensor cost gradually; furthermore, given the higher detecting precision, the detection accuracy and efficiency would be improved, a smaller number of water quality sensors would be needed, and the ten key placement positions where sensor with different detecting precision placed could be identified. Under the constraints of the allowable maximum missing detection rate, 1.00%, and detection time, 120.00 min, the detecting precision of 0.20 mg/L and 38 sensors placed could be selected as the optimal sensor placement scheme. Finally, with the consideration of contaminant uncertainty, the sensor placement scheme with better robustness could be constructed. The proposed framework would be helpful in solving the problem of water quality sensor placement with high practicality and efficiency in long-distance water transfer projects.