Advancing Agricultural Land Suitability in Urbanized Semi-Arid Environments: Insights from Geospatial and Machine Learning Approaches DOI Creative Commons
S. Sathiyamurthi, Subbarayan Saravanan,

M. Ramya

и другие.

ISPRS International Journal of Geo-Information, Год журнала: 2024, Номер 13(12), С. 436 - 436

Опубликована: Дек. 3, 2024

Rising food demands are increasingly threatened by declining crop yields in urbanizing riverine regions of Southern Asia, exacerbated erratic weather patterns. Optimizing agricultural land suitability (AgLS) offers a viable solution for sustainable productivity such challenging environments. This study integrates remote sensing and field-based geospatial data with five machine learning (ML) algorithms—Naïve Bayes (NB), extra trees classifier (ETC), random forest (RF), K-nearest neighbors (KNN), support vector machines (SVM)—alongside land-use/land-cover (LULC) considerations the food-insecure Dharmapuri district, India. A grid searches optimized hyperparameters using factors as slope, rainfall, temperature, texture, pH, electrical conductivity, organic carbon, available nitrogen, phosphorus, potassium, calcium carbonate. The tuned ETC model showed lowest root mean squared error (RMSE = 0.15), outperforming RF 0.18), NB 0.20), SVM 0.22), KNN 0.23). AgLS-ETC map identified 29.09% area highly suitable (S1), 19.06% moderately (S2), 16.11% marginally (S3), 15.93% currently unsuitable (N1), 19.21% permanently (N2). By incorporating Landsat-8 derived LULC to exclude forests, water bodies, settlements, these estimates were adjusted 19.08% 14.45% 11.40% 10.48% 9.58% Focusing on model, followed land-use analysis, provides robust framework optimizing planning, ensuring protection ecological social developing countries.

Язык: Английский

Machine learning-enhanced GALDIT modeling for the Nile Delta aquifer vulnerability assessment in the Mediterranean region DOI
Zenhom E. Salem,

Nesma A. Arafa,

Abdelaziz Abdeldayem

и другие.

Groundwater for Sustainable Development, Год журнала: 2025, Номер 28, С. 101403 - 101403

Опубликована: Янв. 7, 2025

Язык: Английский

Процитировано

2
Synergy of Remote Sensing and Geospatial Technologies to Advance Sustainable Development Goals for Future Coastal Urbanization and Environmental Challenges in a Riverine Megacity DOI Creative Commons
Minza Mumtaz,

Syed Humayoun Jahanzaib,

Waqar Hussain

и другие.

ISPRS International Journal of Geo-Information, Год журнала: 2025, Номер 14(1), С. 30 - 30

Опубликована: Янв. 14, 2025

Riverine coastal megacities, particularly in semi-arid South Asian regions, face escalating environmental challenges due to rapid urbanization and climate change. While previous studies have examined urban growth patterns or impacts independently, there remains a critical gap understanding the integrated of land use/land cover (LULC) changes on both ecosystem vulnerability sustainable development achievements. This study addresses this through an innovative integration multitemporal Landsat imagery (5, 7, 8), SRTM-DEM, historical use maps, population data using MOLUSCE plugin with cellular automata–artificial neural networks (CA-ANN) modelling monitor LULC over three decades (1990–2020) project future for 2025, 2030, 2035, supporting Sustainable Development Goals (SDGs) Karachi, southern Pakistan, one world’s most populous megacities. The framework integrates analysis SDG metrics, achieving overall accuracy greater than 97%, user producer accuracies above 77% Kappa coefficient approaching 1, demonstrating high level agreement. Results revealed significant expansion from 13.4% 23.7% total area between 1990 2020, concurrent reductions vegetation cover, water bodies, wetlands. Erosion along riverbank has caused Malir River’s decrease 17.19 5.07 km2 by highlighting key factor contributing flooding during monsoon season. Flood risk projections indicate that urbanized areas will be affected, 66.65% potentially inundated 2035. study’s contribution lies quantifying achievements, showing varied progress: 26% 9 (Industry, Innovation, Infrastructure), 18% 11 (Sustainable Cities Communities), 13% 13 (Climate Action), 16% 8 (Decent Work Economic Growth). However, declining bodies pose 15 (Life Land) 6 (Clean Water Sanitation), 11%, respectively. approach provides valuable insights planners, offering novel adaptive planning strategies advancing practices similar stressed megacity regions.

Язык: Английский

Процитировано

1
Advancing Agricultural Land Suitability in Urbanized Semi-Arid Environments: Insights from Geospatial and Machine Learning Approaches DOI Creative Commons
S. Sathiyamurthi, Subbarayan Saravanan,

M. Ramya

и другие.

ISPRS International Journal of Geo-Information, Год журнала: 2024, Номер 13(12), С. 436 - 436

Опубликована: Дек. 3, 2024

Rising food demands are increasingly threatened by declining crop yields in urbanizing riverine regions of Southern Asia, exacerbated erratic weather patterns. Optimizing agricultural land suitability (AgLS) offers a viable solution for sustainable productivity such challenging environments. This study integrates remote sensing and field-based geospatial data with five machine learning (ML) algorithms—Naïve Bayes (NB), extra trees classifier (ETC), random forest (RF), K-nearest neighbors (KNN), support vector machines (SVM)—alongside land-use/land-cover (LULC) considerations the food-insecure Dharmapuri district, India. A grid searches optimized hyperparameters using factors as slope, rainfall, temperature, texture, pH, electrical conductivity, organic carbon, available nitrogen, phosphorus, potassium, calcium carbonate. The tuned ETC model showed lowest root mean squared error (RMSE = 0.15), outperforming RF 0.18), NB 0.20), SVM 0.22), KNN 0.23). AgLS-ETC map identified 29.09% area highly suitable (S1), 19.06% moderately (S2), 16.11% marginally (S3), 15.93% currently unsuitable (N1), 19.21% permanently (N2). By incorporating Landsat-8 derived LULC to exclude forests, water bodies, settlements, these estimates were adjusted 19.08% 14.45% 11.40% 10.48% 9.58% Focusing on model, followed land-use analysis, provides robust framework optimizing planning, ensuring protection ecological social developing countries.

Язык: Английский

Процитировано

3