A Machine Learning Approach to Predict Site Selection from the Perspective of Vitality Improvement DOI Creative Commons
Bin Zhao, Hao Zheng, Xuesong Cheng

et al.

Land, Journal Year: 2024, Volume and Issue: 13(12), P. 2113 - 2113

Published: Dec. 6, 2024

The selection of construction sites for Cultural and Museum Public Buildings (CMPBs) has a profound impact on their future operations development. To enhance site planning efficiency, we developed predictive model integrating Artificial Neural Networks (ANNs) Genetic Algorithms (GAs). Taking Shanghai as our case study, utilized over 1.5 million points interest data from Amap Visiting Vitality Values (VVVs) Dianping Shanghai’s administrative area map. We analyzed compiled 344 sites, each containing 39 infrastructure sets one visit vitality set the ANN input. was then tested with untrained to predict VVVs based input sets. conducted multi-precision analysis simulate various scenarios, assessing model’s applicability at different scales. Combining GA approach, predicted improvements. This method can significantly contribute early planning, design, development, operational management CMPBs in future.

Language: Английский

Machine Learning-Based Wetland Vulnerability Assessment in the Sindh Province Ramsar Site Using Remote Sensing Data DOI Creative Commons
Rana Waqar Aslam, Hong Shu, Iram Naz

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(5), P. 928 - 928

Published: March 6, 2024

Wetlands provide vital ecological and socioeconomic services but face escalating pressures worldwide. This study undertakes an integrated spatiotemporal assessment of the multifaceted vulnerabilities shaping Khinjhir Lake, ecologically significant wetland ecosystem in Pakistan, using advanced geospatial machine learning techniques. Multi-temporal optical remote sensing data from 2000 to 2020 was analyzed through spectral water indices, land cover classification, change detection risk mapping examine moisture variability, modifications, area changes proximity-based threats over two decades. The random forest algorithm attained highest accuracy (89.5%) for classification based on rigorous k-fold cross-validation, with a training 91.2% testing 87.3%. demonstrates model’s effectiveness robustness vulnerability modeling area, showing 11% shrinkage open bodies since 2000. Inventory zoning revealed 30% present-day areas under moderate high vulnerability. cellular automata–Markov (CA–Markov) model predicted continued long-term declines driven by swelling anthropogenic like 29 million population growth surrounding Lake. research integrating satellite analytics, algorithms spatial generate actionable insights into guide conservation planning. findings robust baseline inform policies aimed at ensuring health sustainable management Lake wetlands human climatic that threaten functioning these ecosystems.

Language: Английский

Citations

41
Spatio-Temporal Dynamics of Rangeland Transformation using machine learning algorithms and Remote Sensing data DOI

Ningde Wang,

Iram Naz, Rana Waqar Aslam

et al.

Rangeland Ecology & Management, Journal Year: 2024, Volume and Issue: 94, P. 106 - 118

Published: March 23, 2024

Language: Английский

Citations

18
Assessing climatic impacts on land use and land cover dynamics in Peshawar, Khyber Pakhtunkhwa, Pakistan: a remote sensing and GIS approach DOI
Rana Waqar Aslam, Iram Naz, Abdul Quddoos

et al.

GeoJournal, Journal Year: 2024, Volume and Issue: 89(5)

Published: Aug. 31, 2024

Language: Английский

Citations

18
Optimal selection of CSP site for desalination system using GIS and AHP method in Hormozgan province, Iran DOI

Fateme Rasaei,

Hossein Yousefi,

Marziyeh Razeghi

et al.

Energy Reports, Journal Year: 2025, Volume and Issue: 13, P. 2255 - 2268

Published: Feb. 5, 2025

Language: Английский

Citations

2
Geospatial insights into groundwater contamination from urban and industrial effluents in Faisalabad DOI Creative Commons
Abdul Quddoos,

Khalid Muhmood,

Iram Naz

et al.

Discover Water, Journal Year: 2024, Volume and Issue: 4(1)

Published: July 24, 2024

Abstract Groundwater remains the most dependable resource for various essential uses such as drinking, cleansing, agricultural irrigation, and industrial applications. In urban areas, dependency on groundwater to meet water demands is significant. However, this faces threats from overuse poor management, leading a degradation in quality primarily due unchecked release of household wastes. The escalation activities rapid growth have amplified volume wastewater, adversely affecting purity freshwater sources within aquifers. This investigation focuses evaluating impact effluents city Faisalabad. main contributors pollution include indiscriminate disposal through unlined drains extensive application chemical agents agriculture, fertilizers, pesticides. To understand physiochemical properties both, drain groundwater, samples were collected at distances 50 m, 100 150 m outlets. study utilized Geographic Information Systems (GIS) accurately map analyze distribution contaminants. Parameters pH, electrical conductivity (EC), total dissolved solids (TDS), hardness, bicarbonates, calcium magnesium chloride levels examined. findings indicated that contaminant highest increased concentration closer they drainage sources, with exception pH levels. All exceeded World Health Organization's (WHO) safe limits, deeming them unfit use. finding indicates widespread contamination, posing significant public health risks highlighting urgent need improved waste management treatment practices It underscores critical importance implementing effective control measures safeguard ensure security region. notable correlation was observed between pollutants key indicators EC, TDS, their role deteriorating aquifer quality. Moreover, exhibited pollutant concentrations compared those taken further away, distances.

Language: Английский

Citations

12
Evaluation of Land Use Land Cover Changes in Response to Land Surface Temperature With Satellite Indices and Remote Sensing Data DOI
Qun Zhao, Muhammad Haseeb, Xinyao Wang

et al.

Rangeland Ecology & Management, Journal Year: 2024, Volume and Issue: 96, P. 183 - 196

Published: Aug. 2, 2024

Language: Английский

Citations

12
Research Status and Trends of Hydrodynamic Separation (HDS) for Stormwater Pollution Control: A Review DOI Open Access

Yum‐Shing Wong,

Yixiao Chen, Anurita Selvarajoo

et al.

Water, Journal Year: 2025, Volume and Issue: 17(4), P. 498 - 498

Published: Feb. 10, 2025

Growing urbanization has increased impermeable surfaces, raising and polluting stormwater runoff, exacerbating the risk of urban flooding. Effective management is essential to curb sedimentation, minimize pollution, mitigate This systematic literature review from Web Science Scopus between January 2000 June 2024 presents hydrodynamic separation (HDS) technologies. It sheds light on significant issues that water faces. HDS classified into four categories: screening, filtration, settling, flotation, based treatment mechanisms. The results show a shift traditional standalone physical separations multi-stage hybrid processes with nature-based solutions. great advantage these approaches they combine different mechanisms integrate ecological sustainability manage better. findings showed future research will examine AI-assisted technologies, biochar-enhanced green infrastructure systems. When adopting an integrated approach, system perform like natural remove pollutants effectively better monitoring controls. These technologies are intended fill existing voids, especially in removing biological contaminants new (e.g., microplastics pharmaceutical substances). In long term, help enforce Sustainable Development Goals (SDGs) orient areas developing countries towards meeting circular economy objective.

Language: Английский

Citations

0
Heatmaps to Guide Siting of Solar and Wind Farms DOI Creative Commons
Cheng Cheng, David Firnando Silalahi, Lucy Roberts

et al.

Energies, Journal Year: 2025, Volume and Issue: 18(4), P. 891 - 891

Published: Feb. 13, 2025

The decarbonization of the electricity system coupled with electrification transport, heat, and industry represents a practical cost-effective approach to deep decarbonization. A key question is as follows: where build new solar wind farms? This study presents cost-based evaluate land parcels for farm suitability using colour-coded heatmaps that visually depict favourable locations. An indicative cost calculated classified each pixel by focusing on factors including resource availability, proximity transmission infrastructure load centres, exclusion sensitive areas. proposed mitigates subjectivity associated traditional multi-criteria decision-making methods, in which both selection siting assignment their weightings rely highly subjective judgements experts. methodology applied Australia, South Korea, Indonesia, results show high-voltage centres factor affecting site Australia while connection costs are less critical Korea due its smaller area extensive infrastructure. outcomes this study, detailed statistics, made publicly available provide qualitative quantitative information allows comparisons between regions within region. aims empower policymakers, developers, communities, individual landholders make informed decisions and, ultimately, facilitate strategic renewable energy deployment contribute global

Language: Английский

Citations

0
Decadal Dynamics of Rangeland Cover Using Remote Sensing and Machine Learning Approach DOI
Yujing Yang, Zhiming Li, Abdul Quddoos

et al.

Rangeland Ecology & Management, Journal Year: 2025, Volume and Issue: 100, P. 1 - 13

Published: Feb. 14, 2025

Language: Английский

Citations

0
Spatio-temporal analysis of urban expansion and land use dynamics using google earth engine and predictive models DOI Creative Commons

Ai-Guo Zhang,

Aqil Tariq, Abdul Quddoos

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 27, 2025

Urban expansion and changes in land use/land cover (LULC) have intensified recent decades due to human activity, influencing ecological developmental landscapes. This study investigated historical projected LULC urban growth patterns the districts of Multan Sargodha, Pakistan, using Landsat satellite imagery, cloud computing, predictive modelling from 1990 2030. The analysis images was grouped into four time periods (1990–2000, 2000–2010, 2010–2020, 2020–2030). Google Earth Engine cloud-based platform facilitated classification 5 ETM (1990, 2000, 2010) 8 OLI (2020) Random Forest model. A simulation model integrating Cellular Automata an Artificial Neural Network Multilayer Perceptron MOLUSCE plugin QGIS employed forecast resulting maps showed consistently high accuracy levels exceeding 92% for both across all periods. revealed that Multan's built-up area increased 240.56 km2 (6.58%) 440.30 (12.04%) 2020, while Sargodha experienced more dramatic 730.91 (12.69%) 1,029.07 (17.83%). Vegetation remained dominant but significant variations, particularly peri-urban areas. By 2030, is stabilize at 433.22 km2, primarily expanding southeastern direction. expected reach 1,404.97 showing balanced multi-directional toward northeast north. presents effective analytical method processing, GIS, change modeling evaluate spatiotemporal changes. approach successfully identified main transformations trends areas highlighting potential urbanization zones where opportunities exist developing planned managed settlements.

Language: Английский

Citations

0