Monitoring landuse change in Uchhali and Khabeki wetland lakes, Pakistan using remote sensing data

Gondwana Research, Journal Year: 2024, Volume and Issue: 129, P. 252 - 267

Published: Jan. 4, 2024

Language: Английский

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Coupling Remote Sensing Insights With Vegetation Dynamics and to Analyze NO₂ Concentrations: A Google Earth Engine-Driven Investigation

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Journal Year: 2024, Volume and Issue: 17, P. 9858 - 9875

Published: Jan. 1, 2024

Rapid urbanization and industrialization in Lahore Faisalabad have intensified air pollution issues, influencing nitrogen dioxide (NO2) concentrations, land surface temperature (LST), vegetation. The study aims to comprehensively assess changes NO2, LST, vegetation induced by industrialization, focusing on seasonal variations from 2019-2022. evaluates NO2 concentrations health using indices Normalized Difference Vegetation Index (NDVI), Enhanced (EVI), Atmospherically Resistant (ARVI), LST variations. analysis reveals a notable increase during both summer winter, with approximately 0.021 (×103 mol/m2) 0.03 rises observed Lahore. In comparison, experienced more modest increases of around 0.0034 0.007 the respective seasons. Simultaneously, decline cities, indicating substantial deterioration. Moreover, upward trend occurred, experiencing an 1.59 ℃ 0.92°C winter. also showed 1.64 0.54 corresponding Pearson correlation highlights robust negative between indices, underlining impact declining quality. A positive indicates interconnected nature rising temperatures pollution. findings emphasize need for environmental regulations Faisalabad. Addressing levels is critical policymakers urban planners. These insights contribute Sustainable Development Goal (SDG-11), fostering strategies sustainable cities communities combat pressing challenges these areas.

Language: Английский

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Exploring machine learning solutions for overcoming challenges in IoT-based wireless sensor network routing: a comprehensive review

Wireless Networks, Journal Year: 2024, Volume and Issue: 30(4), P. 2647 - 2673

Published: Feb. 29, 2024

Language: Английский

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Addressing the impact of land use land cover changes on land surface temperature using machine learning algorithms

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Aug. 13, 2024

Over the past two and a half decades, rapid urbanization has led to significant land use cover (LULC) changes in Kabul province, Afghanistan. To assess impact of LULC on surface temperature (LST), province was divided into four classes applying Support Vector Machine (SVM) algorithm using Landsat satellite images from 1998 2022. The LST assessed data thermal band. Cellular Automata-Logistic Regression (CA-LR) model applied predict future patterns for 2034 2046. Results showed classes, as built-up areas increased about 9.37%, while bare soil vegetation decreased 7.20% 2.35%, respectively, analysis annual revealed that highest mean LST, followed by vegetation. simulation results indicate an expected increase 17.08% 23.10% 2046, compared 11.23% Similarly, indicated area experiencing class (≥ 32 °C) is 27.01% 43.05% 11.21% increases considerably decreases, revealing direct link between rising temperatures.

Language: Английский

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Unravelling blue landscape fragmentation effects on ecosystem services in urban agglomerations

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 102, P. 105192 - 105192

Published: Jan. 10, 2024

Language: Английский

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Enhancing grid-connected photovoltaic system performance with novel hybrid MPPT technique in variable atmospheric conditions

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: April 8, 2024

Abstract This paper proposes an innovative approach to improve the performance of grid-connected photovoltaic (PV) systems operating in environments with variable atmospheric conditions. The dynamic nature parameters poses challenges for traditional control methods, leading reduced PV system efficiency and reliability. To address this issue, we introduce a novel integration fuzzy logic sliding mode methodologies. Fuzzy enables effectively handle imprecise uncertain data, allowing decision-making based on qualitative inputs expert knowledge. Sliding control, known its robustness against disturbances uncertainties, ensures stability responsiveness under varying Through these methodologies, our proposed offers comprehensive solution complexities posed by real-world dynamics. We anticipate applications across various geographical locations climates. By harnessing synergistic benefits promises significantly enhance reliability presence On grid side, both PSO (Particle Swarm Optimization) GA (Genetic Algorithm) algorithms were employed tune current controller PI (Proportional-Integral) (inverter control). Simulation results, conducted using MATLAB Simulink, demonstrate effectiveness hybrid MPPT technique optimizing system. exhibits superior tracking efficiency, achieving convergence time 0.06 s 99.86%, less oscillation than classical methods. comparison other techniques highlights advantages approach, including higher faster response times. simulation outcomes are analyzed strategies sides (the array side). Both offer effective methods tuning controller. According considered IEEE standards low-voltage networks, total harmonic distortion values (THD) obtained considerably high (8.33% 10.63%, algorithms, respectively). Comparative analyses terms stability, rapid changes.

Language: Английский

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GPT, large language models (LLMs) and generative artificial intelligence (GAI) models in geospatial science: a systematic review

International Journal of Digital Earth, Journal Year: 2024, Volume and Issue: 17(1)

Published: May 20, 2024

The launch of large language models (LLMs) like ChatGPT in late 2022 and the anticipated arrival future GPT-x iterations have marked beginning generative artificial intelligence (GAI) era. We conducted a systematic review how to integrate LLMs including GPT other GAI into geospatial science, based on 293 papers obtained from four databases academic publications – Web Science (WoS), Scopus, SSRN arXiv 26 were eventually included for analysis. statistically outlined share domains where models, type data that been used these modelling tasks roles they play. also pointed out challenges directions next research agenda along with which we could better position ourselves mainstream science cutting-edge paradigm as others leverage insights growing deluge.

Language: Английский

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Green policies and financial development in G7 economies: An in‐depth analysis of environmental regulations and green economic growth

Natural Resources Forum, Journal Year: 2024, Volume and Issue: unknown

Published: Feb. 25, 2024

Abstract This comprehensive study explores the nuanced relationship between financial development and its determinants within G7 nations, spanning years 1990 to 2020. Motivated by need understand long‐term trends, we meticulously analyze key variables including total natural resource rent, Environmental Policy Stringency Index, energy consumption, green gross domestic product (GDP), foreign direct investment inflow. Employing rigorous diagnostic tests ensure robustness of our findings, advanced methodologies such as “Method Moment Quantile Regression,” along with simulations “Bootstrap Regression," “Panel Corrected Standard Errors,” “Feasible Generalized Least Squares” regressions uncover statistical significance practical implications results. Our pivotal findings carry substantial for both individual member states collective group. Highlighting a positive correlation stringent environmental policies, measured development, emphasizes imperative these nations align economic policies. Striking harmonious balance management sustainable regulations not only fosters growth but also addresses global concerns. Furthermore, adverse impact consumption on underscores urgent prioritize efficiency transition sources, aligning trend towards eco‐friendly practices. In response critical propose actionable policy measures. To growing climate crisis standardize finance practices, advocate establishment jointly funded Climate Resilience Adaptation Fund unified Green Bond Framework G7. These measures enhance resilience streamline investments demonstrate G7's commitment greener more prosperous future.

Language: Английский

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Assessing forest cover changes and fragmentation in the Himalayan temperate region: implications for forest conservation and management

Journal of Forestry Research, Journal Year: 2024, Volume and Issue: 35(1)

Published: April 27, 2024

Language: Английский

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Assessment and prediction of meteorological drought using machine learning algorithms and climate data

Climate Risk Management, Journal Year: 2024, Volume and Issue: 45, P. 100630 - 100630

Published: Jan. 1, 2024

Monitoring drought in semi-arid regions due to climate change is of paramount importance. This study, conducted Morocco's Upper Drâa Basin (UDB), analyzed data spanning from 1980 2019, focusing on the calculation indices, specifically Standardized Precipitation Index (SPI) and Evapotranspiration (SPEI) at multiple timescales (1, 3, 9, 12 months). Trends were assessed using statistical methods such as Mann-Kendall test Sen's Slope estimator. Four significant machine learning (ML) algorithms, including Random Forest, Voting Regressor, AdaBoost K-Nearest Neighbors evaluated predict SPEI values for both three 12-month periods. The algorithms' performance was measured indices. study revealed that distribution within UDB not uniform, with a discernible decreasing trend values. Notably, four ML algorithms effectively predicted specified demonstrated highest Nash-Sutcliffe Efficiency (NSE) values, ranging 0.74 0.93. In contrast, algorithm produced range 0.44 0.84. These research findings have potential provide valuable insights water resource management experts policymakers. However, it imperative enhance collection methodologies expand measurement sites improve representativeness reduce errors associated local variations.

Language: Английский

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