Data mining techniques for LULC analysis using sparse labels and multisource data integration for the hilly terrain of Nilgiris district, Tamil Nadu, India DOI
R. Kumaraperumal,

M. Nivas Raj,

S. Pazhanivelan

et al.

Earth Science Informatics, Journal Year: 2024, Volume and Issue: 18(1)

Published: Dec. 9, 2024

Language: Английский

Ecosystem health assessment of East Kolkata Wetlands, India: Implications for environmental sustainability DOI
Pawan Kumar Yadav, Priyanka Jha, Md Saharik Joy

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 366, P. 121809 - 121809

Published: July 14, 2024

Language: Английский

Citations

15
Developing flood mapping procedure through optimized machine learning techniques. Case study: Prahova river basin, Romania DOI Creative Commons
Daniel Constantin Diaconu, Romulus Costache, Abu Reza Md. Towfiqul Islam

et al.

Journal of Hydrology Regional Studies, Journal Year: 2024, Volume and Issue: 54, P. 101892 - 101892

Published: July 13, 2024

Prahova river basin located in the central-southern region of Romania. This study aims to assess susceptibility flooding by using state-of-the-art machine learning and optimization procedures. To achieve this goal, we employed ten flood-related variables as independent our models. These include slope angle, convergence index, distance from river, elevation, plan curvature, hydrological soil group, lithology, topographic wetness rainfall, land use. We used 158 flood locations dependent training four hybrid models: Deep Learning Neural Network-Statistical Index (DLNN-SI), Particle Swarm Optimization-Deep (PSO-DLNN-SI), Support Vector Machine-Statistical (SVM-SI), Optimization-Support (PSO-SVM-SI). Utilizing Statistical method, calculated coefficients for each predictor class or category. The PSO-DLNN-SI model demonstrated best performance, achieving an AUC-ROC curve 0.952. It's worth noting that application PSO algorithm significantly enhanced model's performance. Additionally, it's crucial highlight approximately 25 % exhibits a high very events. Taking into account precise results models applied present study, can state point view, current research contributes better understanding intensity with which floods affect different areas basin.

Language: Английский

Citations

5
Comparison of random forest, gradient tree boosting, and classification and regression trees for mangrove cover change monitoring using Landsat imagery DOI Creative Commons
Nirmawana Simarmata, Ketut Wikantika,

Trika Agnestasia Tarigan

et al.

The Egyptian Journal of Remote Sensing and Space Science, Journal Year: 2025, Volume and Issue: 28(1), P. 138 - 150

Published: March 1, 2025

Language: Английский

Citations

0
Tracking long-term wetland dynamics based on sample migration and two-stage hierarchical classification: A case study of Jiangsu Province DOI
Jingtai Li,

Xiaorou He,

Yao Liu

et al.

CATENA, Journal Year: 2025, Volume and Issue: 254, P. 108993 - 108993

Published: April 3, 2025

Language: Английский

Citations

0
Deviation of hydrological regimes caused by blockage of hydrological connectivity: Implications for wetland restoration and management DOI
Qiang Liu,

Luoyang Gan,

J. Q. Wu

et al.

CATENA, Journal Year: 2025, Volume and Issue: 255, P. 109025 - 109025

Published: April 15, 2025

Language: Английский

Citations

0
Long-term wetland mapping at 10 m resolution using super-resolution and hierarchical classification – a case study in Jianghan Plain, China DOI Creative Commons
Yifei Han, Hong Chi,

Jinliang Huang

et al.

International Journal of Digital Earth, Journal Year: 2025, Volume and Issue: 18(1)

Published: May 4, 2025

Language: Английский

Citations

0
Mongolian Freshwater Ecosystems Under Climate Change and Anthropogenic Pressure: A Case Study of Ugii Lake DOI Creative Commons

Itgelt Navaandorj,

Erdenetsetseg Tsogtbayar, Solongo Tsogtbaatar

et al.

Land, Journal Year: 2025, Volume and Issue: 14(5), P. 998 - 998

Published: May 5, 2025

This study investigates the hydrological, ecological, and socio-economic responses of Ugii Lake—a freshwater body in semi-arid Central Mongolia—to climate variability anthropogenic pressures. Seasonal field surveys conducted during spring, summer, fall 2023–2024 revealed notable spatial temporal variation water quality, with pH ranging from 7.54 to 8.87, EC 316 645 µS/cm, turbidity between 0.36 5.76 NTU. Total dissolved solids (TDS) values ionic compositions indicated increased salinization some zones, particularly those exposed high evaporation shoreline disturbance. Heavy metal analysis identified elevated levels aluminum, manganese, zinc at several sampling points; however, concentrations generally remained within national environmental standards. Vegetation showed that disturbed areas—especially affected by grazing tourism—exhibited reduced native plant diversity dominance invasive species. Socio-economic interviews local herders stakeholders 67.3% households experienced declining livestock productivity, 37.1% reported allergies or respiratory symptoms linked deteriorating conditions. Despite ongoing conservation efforts, respondents expressed dissatisfaction enforcement impact. These findings highlight need for community-driven, integrated lake management strategies address degradation, adaptation, rural livelihood security.

Language: Английский

Citations

0
Protecting wetlands for future generations: A comprehensive approach to the water-climate-society nexus in South Asia DOI
Shivukumar Rakkasagi,

Manish Kumar Goyal

Environmental Impact Assessment Review, Journal Year: 2025, Volume and Issue: 115, P. 107988 - 107988

Published: May 16, 2025

Language: Английский

Citations

0
Predicting land use land cover changes and impact on urban wetlands using cellular automata and artificial neural networks approach, a case study in Greater Accra, Ghana DOI Creative Commons
Martin Amoah, Pece V. Gorsevski

Scientific African, Journal Year: 2025, Volume and Issue: unknown, P. e02767 - e02767

Published: May 1, 2025

Language: Английский

Citations

0
Assessing climate change threats and urbanization impacts on surface runoff in Gdańsk (Poland): insights from remote sensing, machine learning and hydrological modeling DOI Creative Commons
Khansa Gulshad, Michał Szydłowski, Andam Mustafa

et al.

Stochastic Environmental Research and Risk Assessment, Journal Year: 2024, Volume and Issue: 38(12), P. 4825 - 4842

Published: Nov. 13, 2024

Abstract This study investigates the impacts of Land Use/Land Cover (LULC) changes and climate change on surface runoff in Gdańsk, Poland, which is crucial for local LULC planning urban flood risk management. The analysis employs two primary methodologies: remote sensing hydrological modeling. Remote was conducted using Google Earth Engine Change Modeler IDRISI Terrset software to analyze historical (1985–2022) future (2050–2100) LULC. Hydrological modeling performed Natural Resources Conservation Service curve number method assess overall impact Gdańsk’s hydrology at scale. Orunia basin, a critical area due intensive development, selected detailed Hydrologic Modeling System (HEC-HMS). encompassed three scenarios: changes, change, combined effects. revealed marked increase area, shift forest vegetation cover, reduction agricultural land. HEC-HMS simulations showed an coefficient across decades, attributed effect change. projected increases under Representative Concentration Pathway (RCP) 4.5 RCP 8.5 scenarios 2050 2100 are surpass those observed during baseline period. findings highlight that synergistic effects have more significant both basin scales than their separate These insights into shifts responses hold implications sustainable effective management Gdańsk similar settings.

Language: Английский

Citations

3