Machine Learning for Urban Heat Island (UHI) Analysis: Predicting Land Surface Temperature (LST) in Urban Environments

Urban Climate, Journal Year: 2024, Volume and Issue: 55, P. 101962 - 101962

Published: May 1, 2024

Language: Английский

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Towards Improving Sustainable Water Management in Geothermal Fields: SVM and RF Land Use Monitoring

Journal of Human Earth and Future, Journal Year: 2024, Volume and Issue: 5(2), P. 216 - 242

Published: June 1, 2024

The management and monitoring of land use in geothermal fields are crucial for the sustainable utilization water resources, as well striking a balance between production renewable energy preservation environment. This study primarily compared Support Vector Machine (SVM) Random Forest (RF) machine learning methods, using satellite imagery from Landsat 8 Sentinel 2 2021 2023, to monitor Patuha area. objective is improve practices by accurately categorizing different cover types. comparative analysis assessed efficacy these techniques upholding sustainability regions. examined application SVM RF techniques, with particular emphasis on parameter refinement model assessment, enhance classification accuracy. By employing Kernlab e1071 algorithm comparison, research sought produce precise Land Use Model Map, which underscores significance advanced analytical environmental management. approach was utmost importance improving reinforcing practices. evaluation methods demonstrates superiority terms accuracy, stability, precision, particularly intricate urban settings, hence establishing it preferred tasks demanding high reliability. areas alignment Sustainable Development Goals (SDGs) 6 15, fosters conservation ecosystems. Doi: 10.28991/HEF-2024-05-02-06 Full Text: PDF

Language: Английский

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Predicting urban Heat Island in European cities: A comparative study of GRU, DNN, and ANN models using urban morphological variables

Urban Climate, Journal Year: 2024, Volume and Issue: 56, P. 102061 - 102061

Published: July 1, 2024

Continued urbanization, along with anthropogenic global warming, has and will increase land surface temperature air anomalies in urban areas when compared to their rural surroundings, leading Urban Heat Islands (UHI). UHI poses environmental health risks, affecting both psychological physiological aspects of human health. Thus, using a deep learning approach that considers morphological variables, this study predicts intensity 69 European cities from 2007 2021 projects impacts for 2050 2080. The research employs Artificial Neural Networks, Deep Gated Recurrent Units, combining high-resolution 3D models data analyze trends. results indicate strong associations between form, weather patterns, intensity, highlighting the need customized planning policy measures reduce foster sustainable settings. This enhances understanding dynamics serves as valuable tool planners policymakers address challenges climate change, pollution, ultimately aiding improvement outcomes building energy consumption. Moreover, methodology effectively demonstrates ability GRU link its scores projections, offering crucial insights into potential impacts.

Language: Английский

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Diurnal variation of air pollutants and their relationship with land surface temperature in Bengaluru and Hyderabad cities of India

Remote Sensing Applications Society and Environment, Journal Year: 2024, Volume and Issue: 35, P. 101204 - 101204

Published: April 25, 2024

Language: Английский

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Prediction of land surface temperature using spectral indices, air pollutants, and urbanization parameters for Hyderabad city of India using six machine learning approaches

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

Published: June 2, 2024

Language: Английский

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Machine Learning Application for Nutrient Removal Rate Coefficient Analyses in Horizontal Flow Constructed Wetlands

ACS ES&T Water, Journal Year: 2024, Volume and Issue: 4(6), P. 2619 - 2631

Published: May 1, 2024

Land area optimization for horizontal flow constructed wetlands (HFCWs) with a low organic loading rate (OLR) needs special considerations as the microflora changes dramatically OLR. The P-k-C* approach does not lead to an accurate calculation of k-values in these wetlands. In this research, nonlinear machine learning models [Support Vector Regression (SVR), Random Forest (RF), and Artificial Neural Networks (ANN)] are applied predict realistic k-values. Data from 37 low-OLR HFCWs (n = 544) were analyzed, calculated found vary markedly (0.059–0.249 average 0.113 ± 0.090 m/day). classification based on OLR, rate, media depth leads reduction standard deviations (SDs) 83.40 35.27%. least SDs needed optimal design CWs. SVR, RF, ANN tested, best prediction efficiency testing datasets was achieved through model R2(kTKN)= 0.768 (RMSE 0.067) total Kjeldahl nitrogen (TKN), R2(kTN)= 0.835 0.043) (TN), R2(kTP) 0.723 0.087) phosphorus (TP). outcome validated using primary data HFCWs, which also confirmed superiority ANN-based model, can be used customization HFCWs.

Language: Английский

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A Futuristic Approach to Subsurface-Constructed Wetland Design for the South-East Asian Region Using Machine Learning

ACS ES&T Water, Journal Year: 2024, Volume and Issue: 4(9), P. 4061 - 4074

Published: Aug. 29, 2024

This study investigates the optimized design of horizontal flow constructed wetlands (HFCWs) to enhance pollutant removal efficiency while minimizing surface area requirements, particularly in Southeast Asian region. By refining first-order rate coefficient (k) for organics and nutrients, research aims meet specific performance benchmarks across three scenarios, ensuring compliance with discharge or reuse standards. Utilizing a data set comprising 1680 entries, five machine learning models─multiple linear regression (MLR), eXtreme Gradient Boosting (XGBoost), random forest (RF), artificial neural network (ANN), support vector (SVR)─were employed predict k values. Pearson's correlation, heat maps, ANOVA analysis identified most influential parameters affecting k-value predictions. The values ranged from 0.01 0.52 per day using P–k–C* method, essential effective removal. SVR model demonstrated highest predictive accuracy, R2 0.91 kBOD, 0.90 kTN, 0.82 kTKN, 0.76 kTP. optimization reduced standard deviations significantly, 136.90% 2.28%. Consequently, required wetland was by up 68% biochemical oxygen demand (BOD), 60% TN (total nitrogen), 67% TP phosphorus) larger systems, supporting tailored HFCWs targeted

Language: Английский

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Predicting temperature variability in major Indian cities using Random Forest Regression (RFR) Model

Journal of Earth System Science, Journal Year: 2025, Volume and Issue: 134(1)

Published: Jan. 28, 2025

Language: Английский

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Revolutionizing air quality forecasting: Fusion of state-of-the-art deep learning models for precise classification

Urban Climate, Journal Year: 2025, Volume and Issue: 59, P. 102308 - 102308

Published: Jan. 28, 2025

Language: Английский

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From Data to Insights: Modeling Urban Land Surface Temperature Using Geospatial Analysis and Interpretable Machine Learning

Sensors, Journal Year: 2025, Volume and Issue: 25(4), P. 1169 - 1169

Published: Feb. 14, 2025

This study introduces an innovative machine learning method to model the spatial variation of land surface temperature (LST) with a focus on urban center Da Nang, Vietnam. Light Gradient Boosting Machine (LightGBM), support vector machine, random forest, and Deep Neural Network are employed establish functional relationships between LST its influencing factors. The approaches trained validated using remote sensing data from 2014, 2019, 2024. Various explanatory variables representing topographical characteristics, as well landscapes, used. Experimental results show that LightGBM outperforms other benchmark methods. In addition, Shapley Additive Explanations utilized clarify impact factors affecting LST. analysis outcomes indicate while importance these changes over time, density greenspace consistently emerge most influential attained R2 values 0.85, 0.92, 0.91 for years 2024, respectively. findings this work can be helpful deeper understanding heat stress dynamics facilitate planning.

Language: Английский

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