The role of artificial intelligence in the implementation of the UN Sustainable Development Goal 11: Fostering sustainable cities and communities

Cities, Год журнала: 2024, Номер 150, С. 105021 - 105021

Опубликована: Апрель 15, 2024

Язык: Английский

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Automation of image processing through ML algorithms of GRASS GIS using embedded Scikit-Learn library of Python

Examples and Counterexamples, Год журнала: 2025, Номер 7, С. 100180 - 100180

Опубликована: Фев. 3, 2025

Язык: Английский

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Quantitative assessment of Land use/land cover changes in a developing region using machine learning algorithms: A case study in the Kurdistan Region, Iraq

Heliyon, Год журнала: 2023, Номер 9(11), С. e21253 - e21253

Опубликована: Окт. 24, 2023

The identification of land use/land cover (LULC) changes is important for monitoring, evaluating, and preserving natural resources. In the Kurdistan region, utilization remotely sensed data to assess effectiveness machine learning algorithms (MLAs) LULC classification change detection analysis has been limited. This study monitors analyzes in area from 1991 2021 using a quantitative approach with multi-temporal Landsat imagery. Five MLAs were applied: Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), Extreme Gradient Boosting (XGBoost). results showed that RF algorithm produced most accurate maps three-decade period, accompanied by high kappa coefficient (0.93-0.97) compared SVM (0.91-0.95), ANN (0.91-0.96), KNN (0.92-0.96), XGBoost (0.92-0.95) algorithms. Consequently, classifier was implemented categorize all obtainable satellite images. Socioeconomic throughout these transition periods revealed results. Rangeland barren areas decreased 11.33 % (-402.03 km2) 6.68 (-236.8 km2), respectively. transmission increases 13.54 (480.18 3.43 (151.74 0.71 (25.22 occurred agricultural land, forest, built-up areas, outcomes this contribute significantly monitoring developing regions, guiding stakeholders identify vulnerable better use planning sustainable environmental protection.

Язык: Английский

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Land Use and Land Cover Classification for Change Detection Studies Using Convolutional Neural Network

Applied Computing and Geosciences, Год журнала: 2025, Номер unknown, С. 100227 - 100227

Опубликована: Янв. 1, 2025

Язык: Английский

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Enhancing Land Cover/Land Use (LCLU) classification through a comparative analysis of hyperparameters optimization approaches for deep neural network (DNN)

Ecological Informatics, Год журнала: 2023, Номер 78, С. 102333 - 102333

Опубликована: Окт. 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.

Язык: Английский

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ENVINet5 deep learning change detection framework for the estimation of agriculture variations during 2012–2023 with Landsat series data

Environmental Monitoring and Assessment, Год журнала: 2024, Номер 196(3)

Опубликована: Фев. 5, 2024

Язык: Английский

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Prediction of interfacial wetting behavior of H2/mineral/brine; implications for H2 geo-storage

Journal of Energy Storage, Год журнала: 2023, Номер 72, С. 108567 - 108567

Опубликована: Авг. 9, 2023

Язык: Английский

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Machine Learning and Deep Learning in Remote Sensing Data Analysis

Elsevier eBooks, Год журнала: 2024, Номер unknown

Опубликована: Янв. 1, 2024

Язык: Английский

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Remote sensing of 50 years of coastal urbanization and environmental change in the Arabian Gulf: a systematic review

Frontiers in Remote Sensing, Год журнала: 2024, Номер 5

Опубликована: Сен. 12, 2024

Since the 1970s oil boom, nations surrounding Arabian Gulf have witnessed rapid coastal urbanization, which accelerated in early 2000s with emergence of large-scale ‘mega-projects’ designed to accommodate growing populations, attract international investments, and promote tourism. This development surge has had profound environmental impacts, including significant habitat modification, land use cover (LULC) change, increased pressure. Remote sensing (RS) technologies become indispensable tools for monitoring these changes, offering cost-effective non-intrusive methods map assess zones. However, RS applications across been spatially limited, often focusing narrowly on specific cities or habitats while neglecting broader geographical dimensions urbanization. study addresses this gap by conducting a systematic review peer-reviewed literature from 1971 2022, covering regions eight bordering Gulf. A total 186 publications were categorized into three focal areas: 1) urbanization LULC, 2) marine habitats, 3) pressures state changes. The results reveal increase studies recent years, around two-thirds (64.3%) appearing between 2016 2022. Studies predominantly focused changes (35%), followed modification (27%), (20%). Geographically, research primarily concentrated coasts southern (UAE Qatar) western (Bahrain Saudi Arabia), where major urban centers are located, northern (Kuwait Iraq) Iranian coast less studied. highlights need integrated GIS-based systems that combine different sources data situ measurements evaluate as unified system. Expanding spatial coverage, enhancing temporal analysis, fostering regional collaboration necessary improve understanding management approach will more effectively inform decision-makers, support sustainable long-term resilience region.

Язык: Английский

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Multimodal dementia identification using lifestyle and brain lesions, a machine learning approach

AIP Advances, Год журнала: 2024, Номер 14(6)

Опубликована: Июнь 1, 2024

Dementia diagnosis often relies on expensive and invasive neuroimaging techniques that limit access to early screening. This study proposes an innovative approach for facilitating dementia screening by estimating diffusion tensor imaging (DTI) measures using accessible lifestyle brain factors. Conventional DTI analysis, though effective, is hindered high costs limited accessibility. To address this challenge, fuzzy subtractive clustering identified 14 influential variables from the Lifestyle Brain Health Atrophy Lesion Index frameworks, encompassing demographics, medical conditions, factors, structural markers. A multilayer perceptron (MLP) neural network was developed these selected predict fractional anisotropy (FA), a metric reflecting white matter integrity cognitive function. The MLP model achieved promising results, with mean squared error of 0.000 878 test set FA prediction, demonstrating its potential accurate estimation without costly techniques. values in dataset ranged 0 1, higher indicating greater integrity. Thus, suggests model’s predictions were highly compared observed values. multifactorial aligns current understanding dementia’s complex etiology influenced various biological, environmental, By integrating readily available data into predictive model, method enables widespread, cost-effective risk assessment. proposed tool could facilitate timely interventions, preventive strategies, efficient resource allocation public health programs, ultimately improving patient outcomes caregiver burden.

Язык: Английский

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