Review on environmental aspects in smart city concept: Water, waste, air pollution and transportation smart applications using IoT techniques

Sustainable Cities and Society, Год журнала: 2023, Номер 94, С. 104567 - 104567

Опубликована: Апрель 2, 2023

Язык: Английский

---

Integrating Model Predictive Control With Stormwater System Design: A Cost‐Effective Method of Urban Flood Risk Mitigation During Heavy Rainfall

Water Resources Research, Год журнала: 2024, Номер 60(4)

Опубликована: Апрель 1, 2024

Abstract The integration of green‐gray infrastructures with advanced control approaches is revolutionizing the stormwater system retrofitting, emerging as an innovative strategy to mitigate urban flood risks. However, a major challenge lies in balancing substantial investments these infrastructure projects their environmental benefits, such reduced flooding volume and lower peak flow. Model predictive (MPC), dynamic intelligent approach, optimizes benefits but underutilized design phase for cost‐effectiveness analysis. This study introduces multi‐scenario model framework that incorporates MPC other into designs, including implementation controlled storage tanks green infrastructures. provides comprehensive modeling tools practitioners evaluate costs across various designs scenarios, ultimately identifying solutions are both environmentally economically viable. A case conducted small catchment area Shenzhen City, China, demonstrates effectiveness this framework. results indicate outperforms particularly under heavy or extreme rainfall conditions. Notably, not only superior also yields considerable cost savings, ranging from 1,787 9,371 USD per hectare compared static control, equating 5% reduction relative rule‐based control. Such findings suggest integrating cost‐effective alternative extensive expansion management, which significantly enhances benefit contribution without additional expenses.

Язык: Английский

---

Geographic heterogeneity of activation functions in urban real-time flood forecasting: Based on seasonal trend decomposition using Loess-Temporal Convolutional Network-Gated Recurrent Unit model

Journal of Hydrology, Год журнала: 2024, Номер 636, С. 131279 - 131279

Опубликована: Май 7, 2024

Язык: Английский

---

Real-time regulation of detention ponds via feedback control: Balancing flood mitigation and water quality

Journal of Hydrology, Год журнала: 2024, Номер 643, С. 131866 - 131866

Опубликована: Авг. 23, 2024

Язык: Английский

---

An integrated framework of deep learning and entropy theory for enhanced high-dimensional permeability field identification in heterogeneous aquifers

Water Research, Год журнала: 2024, Номер 268, С. 122706 - 122706

Опубликована: Окт. 31, 2024

Язык: Английский

---

A Comprehensive Framework for Evaluation of Skeletonization Impacts on Urban Drainage Network Model Simulations

Water Resources Research, Год журнала: 2025, Номер 61(2)

Опубликована: Янв. 30, 2025

Abstract Urban drainage network models (UDNMs) have been widely used to facilitate flood management. Typically, a UDNM is developed using data from Geographic Information Systems (GIS), and hence it consists of many short pipes connection nodes or manholes. To improve modeling efficiency, GIS‐based model generally skeletonized by removing elements. However, there has surprisingly lack knowledge on what extent such skeletonization can affect the model's simulation accuracy, resulting in uncertainty risk estimation. This paper makes first attempt quantitatively evaluate multidimensional impacts different levels hydraulic properties UDNMs. goal achieved new evaluation framework comprising eight existing metrics that make use hydrographs, main pipe hydraulics distribution properties. A real‐life illustrate under various rainfall conditions levels. The also compare performance two compensation methods mitigating caused skeletonization. Results obtained show that: (a) significantly magnitude peak flow at outfall, with maximum overestimation up 20%, (b) be affected increasing 35%, (c) may alter which largely ignored past studies. These findings provide guidance for skeletonization, where their associated should aware engineering practice.

Язык: Английский

---

ECP-IEM: Enhancing seasonal crop productivity with deep integrated models

PLoS ONE, Год журнала: 2025, Номер 20(2), С. e0316682 - e0316682

Опубликована: Фев. 5, 2025

Accurate crop yield forecasting is vital for ensuring food security and making informed decisions. With the increasing population global warming, addressing has become a priority, so accurate very important. Artificial Intelligence (AI) increased accuracy significantly. The existing Machine Learning (ML) methods are using statistical measures as regression, correlation chi square test predicting yield, all such model’s leads to low when number of factors (variables) weather soil conditions, wind, fertilizer quantity, seed quality climate increased. proposed methodology consists different stages, like Data Collection, Preprocessing, Feature Extraction with Support Vector (SVM), Normalized Google Distance (NGD), feature ranking rising star. This study combines Bidirectional Gated Recurrent Unit (Bi-GRU) Time Series CNN predict then recommendation further improvement. model showed good results in datasets significant improvement compared baseline models. ECP-IEM achieved an 96.34%, precision 94.56% recall 95.23% on datasets. Moreover, was also evaluated based MAE, MSE, RMSE, which produced values 0.191, 0.0674, 0.238, respectively. will help improving production crops by giving early look about than farmer yield.

Язык: Английский

---

High-throughput determination Methodology of Multiple pesticide residues in surface water and its application in environmental risk assessment

Microchemical Journal, Год журнала: 2025, Номер unknown, С. 113519 - 113519

Опубликована: Март 1, 2025

Язык: Английский

---

Machine learning assisted precise prediction of algae bloom in large-scale water diversion engineering

Desalination, Год журнала: 2025, Номер unknown, С. 118880 - 118880

Опубликована: Апрель 1, 2025

Язык: Английский

---

A digital twin model for contaminant fate and transport in urban and natural drainage networks with online state estimation

Environmental Modelling & Software, Год журнала: 2023, Номер 171, С. 105868 - 105868

Опубликована: Ноя. 3, 2023

Increased pollutant loads caused by urbanization and climate change have led to widespread impairment of surface water systems. To better manage these threats, managers are seeking digital twins that combine online models with sensor data respond quality hazards in real-time. This study introduces Pipedream-WQ, a new model for contaminant transport drainage networks combines novel implicit solver the unsteady advection-reaction-diffusion(ARD) equation an efficient assimilation scheme based on Kalman Filtering. We show this reliably reproduces analytical solutions ARD steady conditions, accurately captures behavior complex network. Furthermore, we enables estimation concentrations at ungauged locations compared model-only approach. will enable improved tracking source identification, active management through real-time control hydraulic infrastructure.

Язык: Английский

---