Enhancing Land Cover/Land Use (LCLU) classification through a comparative analysis of hyperparameters optimization approaches for deep neural network (DNN) DOI Creative Commons
Ali Azedou,

Aouatif Amine,

Isaya Kisekka

et al.

Ecological Informatics, Journal Year: 2023, Volume and Issue: 78, P. 102333 - 102333

Published: Oct. 11, 2023

Sustainable natural resources management relies on effective and timely assessment of conservation land practices. Using satellite imagery for Earth observation has become essential monitoring cover/land use (LCLU) changes identifying critical areas conserving biodiversity. Remote Sensing (RS) datasets are often quite large require tremendous computing power to process. The emergence cloud-based techniques presents a powerful avenue overcome limitations by allowing machine-learning algorithms process analyze RS the cloud. Our study aimed classify LCLU Talassemtane National Park (TNP) using Deep Neural Network (DNN) model incorporating five spectral indices differentiate six classes Sentinel-2 imagery. Optimization DNN was conducted comparative analysis three optimization algorithms: Random Search, Hyperband, Bayesian optimization. Results indicated that improved classification between with similar reflectance. Hyperband method had best performance, improving accuracy 12.5% achieving an overall 94.5% kappa coefficient 93.4%. dropout regularization prevented overfitting mitigated over-activation hidden nodes. initial results show machine learning (ML) applications can be tools management.

Language: Английский

Impact of vegetation cover loss on surface temperature and carbon emission in a fastest-growing city, Cumilla, Bangladesh DOI
Abdulla ‐ Al Kafy, Abdullah-Al- Faisal, Abdullah Al Rakib

et al.

Building and Environment, Journal Year: 2021, Volume and Issue: 208, P. 108573 - 108573

Published: Nov. 16, 2021

Language: Английский

Citations

99
Modelling microscale impacts assessment of urban expansion on seasonal surface urban heat island intensity using neural network algorithms DOI

Milan Saha,

Abdulla ‐ Al Kafy, Arpita Bakshi

et al.

Energy and Buildings, Journal Year: 2022, Volume and Issue: 275, P. 112452 - 112452

Published: Sept. 10, 2022

Language: Английский

Citations

51
Assessing the Spatial Mapping of Heat Vulnerability under Urban Heat Island (UHI) Effect in the Dhaka Metropolitan Area DOI Open Access

Rakin Abrar,

Showmitra Kumar Sarkar,

Kashfia Tasnim Nishtha

et al.

Sustainability, Journal Year: 2022, Volume and Issue: 14(9), P. 4945 - 4945

Published: April 20, 2022

The urban heat island (UHI) phenomenon gets intensified in the process of urbanization, which increases vulnerability dwellers to heatwaves. UHI-induced heatwaves has increased Bangladesh during past decades. Thus, this study aims examine UHI and city Dhaka using a index (HVI). HVI is constructed various demographic, socioeconomic, environmental risk variables at thana level. Principal component analysis (PCA) was applied 26 normalized for each 41 thanas prepare HVI. Result shows that more than 60% under built-up areas, while vegetation cover water bodies are low proportion. Analysis very high- high-risk zones comprise 6 11 thanas, low- low-risk only 5 8 thanas. correlation with such as exposure (0.62) sensitivity (0.80) found be highly positive, adaptive capacity had negative (−0.26) Findings can utilized mitigation maintaining thermal comfort Dhaka.

Language: Английский

Citations

46
Towards Sustainable and Livable Cities: Leveraging Remote Sensing, Machine Learning, and Geo-Information Modelling to Explore and Predict Thermal Field Variance in Response to Urban Growth DOI Open Access
Mirza Waleed, Muhammad Sajjad, Anthony Owusu Acheampong

et al.

Sustainability, Journal Year: 2023, Volume and Issue: 15(2), P. 1416 - 1416

Published: Jan. 11, 2023

Urbanization-led changes in land use cover (LULC), resulting an increased impervious surface, significantly deteriorate urban meteorological conditions compromising long-term sustainability. In this context, we leverage machine learning, spatial modelling, and cloud computing to explore predict the changing patterns growth associated thermal characteristics Bahawalpur, Pakistan. Using multi-source earth observations (1990–2020), field variance index (UTFVI) is estimated evaluate heat island effect quantitatively. From 1990 2020, area by ~90% at expense of vegetation barren land, which will further grow 2050 (50%), as determined artificial neural network-based prediction. The surface temperature summer winter seasons has experienced increase 0.88 °C ~5 °C, respectively. While there exists heterogeneity UTFVI 1990–2020, city expected experience a ~140% areas with severe response predicted LULC change 2050. study provides essential information on puts forth useful insights advance our understanding climate, can progressively help designing more livable sustainable cities face environmental changes.

Language: Английский

Citations

26
Evaluating the impact of landscape configuration, patterns and composition on land surface temperature: an urban heat island study in the Megacity Lahore, Pakistan DOI
Muhammad Nasar-u-Minallah, Dagmar Haase, Salman Qureshi

et al.

Environmental Monitoring and Assessment, Journal Year: 2024, Volume and Issue: 196(7)

Published: June 18, 2024

Language: Английский

Citations

16
The creation of multi-level urban ecological cooling network to alleviate the urban heat island effect DOI
Yingying Li, Shumei Wang, Shujun Zhang

et al.

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 114, P. 105786 - 105786

Published: Aug. 30, 2024

Language: Английский

Citations

16
Modeling land use/land cover changes using quad hybrid machine learning model in Bangweulu wetland and surrounding areas, Zambia DOI Creative Commons
Misheck Lesa Chundu, Kawawa Banda,

Chisanga Lyoba

et al.

Environmental Challenges, Journal Year: 2024, Volume and Issue: 14, P. 100866 - 100866

Published: Jan. 1, 2024

Wetlands are among the most productive natural ecosystems globally, providing crucial ecosystem services to people. Regrettably, a substantial 64% –71% of wetlands have been lost worldwide since 1900, mainly due changes in land use and cover (LULC). This issue is not unique Zambia's Bangweulu Wetland System (BWS), which faces similar challenges. However, there limited information about LULC BWS. Furthermore, finding accurate cost-effective methods understand dynamics complicated by multitude available techniques for classification. Non-parametric like Machine Learning (ML) offer greater accuracy, but different ML models come with distinct strengths weaknesses. Combining multiple has potential create more precise classification model. Open-source software QGIS spatial data Landsat also play significant role this endeavour. The primary objective study was enhance accuracy modeling wetland areas. Six models: Support Vector (SVM), Naive Bayes (NB), Decision Tree (DT), Artificial Neural Network (ANN), Random Forest (RF), K-Nearest Neighbour (KNN) were used image 8 (2020 image) 5 (1990, 2000, 2010 images) QGIS. Four SVM, NB, DT, KNN, performed better than other models. Consequently, Quad (4) hybrid model created fusing maps from these four highest performance. Results revealed that fusion classified KNN (Quad model) showcased superior performance compared individual Kappa Index scores 0.87, 0.72, 0.84 0.87 years 1990, 2020, respectively. analysis 1990 2020 showed yearly decline -1.17%, -1.01%, -0.12% forest, grassland, water body coverage, In contrast, built-up areas cropland increased at rates 1.70% 2.70%, underscores consistent growth alongside reduction forest grassland. Although experienced gradual decrease over period, minimal. Long-term monitoring will be essential evaluating success interventions, guiding conservation efforts, mitigating negative impacts on ecosystem, determining whether bodies sustained trend or short-term phenomenon.

Language: Английский

Citations

10
Prediction of land surface temperature using spectral indices, air pollutants, and urbanization parameters for Hyderabad city of India using six machine learning approaches DOI
Gourav Suthar, Saurabh Singh,

Nivedita Kaul

et al.

Remote Sensing Applications Society and Environment, Journal Year: 2024, Volume and Issue: 35, P. 101265 - 101265

Published: June 2, 2024

Language: Английский

Citations

10
Geospatial analysis of unplanned urbanization: impact on land surface temperature and habitat suitability in Cuttack, India DOI Creative Commons
Prasanta Kumar Patra, Duryadhan Behera, Vishal Chettry

et al.

Discover Sustainability, Journal Year: 2025, Volume and Issue: 6(1)

Published: Feb. 19, 2025

Language: Английский

Citations

1
Mitigation Pathways of Urban Heat Islands and Simulation of Their Effectiveness from a Perspective of Connectivity DOI

Zhao Qiuyue,

Tao Ling,

Hak Jun Song

et al.

Sustainable Cities and Society, Journal Year: 2025, Volume and Issue: unknown, P. 106300 - 106300

Published: March 1, 2025

Language: Английский

Citations

1