Migrating Storms and Optimal Control of Urban  Sewer Networks

Rathnayake, Upaka

doi:10.3390/hydrology2040230

Cited by 22 publications

(6 citation statements)

References 13 publications

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“…A large number of studies have shown that NPS pollution is the main cause of deterioration and eutrophication of surface water bodies such as rivers, lakes, and reservoirs [4][5][6]. Especially in agricultural areas, water pollution caused by excessive nitrogen input has become the most serious water quality problem [7][8][9]. Research on the control and management of NPS pollution has important theoretical and practical significance for protection of the water environment in river basins and ensuring the safety of urban drinking water.…”

Section: Introductionmentioning

confidence: 99%

Identification of Critical Source Areas of Nitrogen Load in the Miyun Reservoir Watershed under Different Hydrological Conditions

et al. 2020

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The spatiotemporal distribution of critical source areas (CSAs) will change with hydrological conditions. In this study, the CSAs of nitrogen load under different hydrological conditions in the Chaohe River watershed were identified using the cumulative pollution load curve method determined from the nitrogen pollution simulated using the Soil and Water Assessment Tool (SWAT) model. The results showed that: (1) The order of factors impacting nitrogen load intensity is as follows: fertilization intensity, rainfall, runoff, land use type, slope type, and soil type. (2) The primary and secondary CSAs are concentrated in the upper and lower areas of the watershed, where cultivated land (8.36%) and grassland (52.55%) are more abundant. The potential pollution source areas are concentrated in the upper and middle areas of the watershed, where cultivated land (6.99%), grassland (42.37%), and forest land (48.18%) are evenly distributed. The low-risk source areas are concentrated in the middle and left bank of the watershed, where forest land (67.65%) is dominant and the vegetation coverage is highest. The research results have significance for improving the accuracy of the implementation of best management practices, and can provide a reference for the formulation of drinking water protection policies for Beijing.

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Section: Introductionmentioning

confidence: 99%

Identification of Critical Source Areas of Nitrogen Load in the Miyun Reservoir Watershed under Different Hydrological Conditions

et al. 2020

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“…The model's accuracy, speed, and specialized focus on sewer blockage detection make it a highly promising solution for immediate and effective urban wastewater system management. In comparison, MOEAs such as the sensitivity-based adaptive procedure (SAP) [31], optimal control algorithms [32], and novel methodologies [33] have shown efficiency in various aspects of wastewater management, such as sewer rehabilitation and optimal scheduling. However, their computational demands and reliance on complex algorithms might hinder their real-time applicability.…”

Section: Comparing Ai-driven Approach To Moeasmentioning

confidence: 99%

AI-Driven High-Precision Model for Blockage Detection in Urban Wastewater Systems

Patil,

Calay,

Mustafa

et al. 2023

Electronics

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In artificial intelligence (AI), computer vision consists of intelligent models to interpret and recognize the visual world, similar to human vision. This technology relies on a synergy of extensive data and human expertise, meticulously structured to yield accurate results. Tackling the intricate task of locating and resolving blockages within sewer systems is a significant challenge due to their diverse nature and lack of robust technique. This research utilizes the previously introduced “S-BIRD” dataset, a collection of frames depicting sewer blockages, as the foundational training data for a deep neural network model. To enhance the model’s performance and attain optimal results, transfer learning and fine-tuning techniques are strategically implemented on the YOLOv5 architecture, using the corresponding dataset. The outcomes of the trained model exhibit a remarkable accuracy rate in sewer blockage detection, thereby boosting the reliability and efficacy of the associated robotic framework for proficient removal of various blockages. Particularly noteworthy is the achieved mean average precision (mAP) score of 96.30% at a confidence threshold of 0.5, maintaining a consistently high-performance level of 79.20% across Intersection over Union (IoU) thresholds ranging from 0.5 to 0.95. It is expected that this work contributes to advancing the applications of AI-driven solutions for modern urban sanitation systems.

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“…For example, the Mixed Integer Programming approach uses objective functions and constraints to formulate the task scheduling problem, exchanging computational and time complexity for an optimal solution [12]. Genetic algorithms and particle swarm algorithms based on heuristic stochastic search algorithms can find the optimal value of the parameter set through a large number of iterative experiments, and such algorithms can be applied to multi-objective optimization problems [13][14][15][16]. Heuristic-based list task scheduling algorithms demonstrate their high execution capabilities in a heterogeneous processor resource.…”

Section: Related Workmentioning

confidence: 99%

MASA: Multi-Application Scheduling Algorithm for Heterogeneous Resource Platform

Peng,

Wang

2023

Electronics

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Heterogeneous architecture-based systems-on-chip enable the development of flexible and powerful multifunctional RF systems. In complex and dynamic environments where applications arrive continuously and stochastically, real-time scheduling of multiple applications to appropriate processor resources is crucial for fully utilizing the heterogeneous SoC’s resource potential. However, heterogeneous resource-scheduling algorithms still face many problems in practical situations, including generalized abstraction of applications and heterogeneous resources, resource allocation, efficient scheduling of multiple applications in complex mission scenarios, and how to ensure the effectiveness combining with real-world applications of scheduling algorithms. Therefore, in this paper, we design the Multi-Application Scheduling Algorithm, named MASA, which is a two-phase scheduler architecture based on Deep Reinforcement Learning. The algorithm is made up of neural network scheduler-based task prioritization for dynamic encoding of applications and heuristic scheduler-based task mapping for solving the processor resource allocation problem. In order to achieve stable and fast training of the network scheduler based on the actor–critic strategy, we propose optimization methods for the training of MASA: reward dynamic alignment (RDA), earlier termination of the initial episodes, and asynchronous multi-agent training. The performance of the MASA is tested with classic directed acyclic graph and six real-world application datasets, respectively. Experimental results show that MASA outperforms other neural scheduling algorithms and heuristics, and ablation experiments illustrate how these training optimizations improve the network’s capacity.

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Migrating Storms and Optimal Control of Urban Sewer Networks

Cited by 22 publications

References 13 publications

Identification of Critical Source Areas of Nitrogen Load in the Miyun Reservoir Watershed under Different Hydrological Conditions

Identification of Critical Source Areas of Nitrogen Load in the Miyun Reservoir Watershed under Different Hydrological Conditions

AI-Driven High-Precision Model for Blockage Detection in Urban Wastewater Systems

MASA: Multi-Application Scheduling Algorithm for Heterogeneous Resource Platform

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