Prediction of weighted arithmetic water quality index for urban water quality using ensemble machine learning model

Chemosphere, Год журнала: 2024, Номер 352, С. 141393 - 141393

Опубликована: Фев. 5, 2024

Язык: Английский

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Integrated Remote Sensing for Enhanced Drought Assessment: A Multi‐Index Approach in Rajasthan, India

Earth and Space Science, Год журнала: 2025, Номер 12(2)

Опубликована: Фев. 1, 2025

Abstract This study investigates land use, cover (LULC) changes, vegetation health, and drought severity in Rajasthan, India, from 1985 to 2020 using remote sensing techniques. By analyzing satellite imagery with the normalized difference index (NDVI), temperature condition (TCI), (VCI), NDVI deviation (Dev_NDVI), we assess spatial temporal dynamics of region's landscape conditions. Our findings indicate significant LULC including a decrease water bodies 6412.87 2248.51 km 2 dense forests by 61.37%, while built‐up areas expanded 890.50%, reflecting substantial human impact environmental change. Drought analysis revealed that nearly 49% area experienced moderate severe conditions, VCI levels below 40%, indicating widespread across different regions time periods. The employs weighted sum Dev_NDVI, VCI, TCI create detailed map, revealing extreme necessitate immediate action for sustainable management. novelty this approach lies its integrated multi‐index method assessing over 35 year period, providing robust framework resilience ecosystems climatic stresses. research emphasizes value continuous monitoring highlights future implications integrating advanced technologies enhance management strategies, ultimately informing policy decisions resource Rajasthan similar semi‐arid globally.

Язык: Английский

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Impact of land use/land cover changes on evapotranspiration and model accuracy using Google Earth engine and classification and regression tree modeling

Geomatics Natural Hazards and Risk, Год журнала: 2023, Номер 15(1)

Опубликована: Дек. 22, 2023

This research uses a Classification and Regression Tree (CART) model with Google Earth Engine (GEE) to assess the winter season's land cover change detection mapping impact on evapotranspiration (crop water requirement) parameters. Winter seasons, crucial for agricultural planning, irrigation requirement challenges in accurately detecting changes due dynamic nature of farming practices during this period. In study, Landsat-8 OLI images have been combined map Land use (LULC) other Akola Block, Maharashtra, India, 2018–2022 season. As an discoverer researcher that found detailed information LULC classes last 2018 2022 CART combination cloud-computing GEE demonstrates be practical approach accurate classification maps create pixel-based seasons study area. The novelty lies its innovative GEE, powerful platform remote sensing geospatial analysis, remarkable accuracy. Achieving 100% training accuracy across four years under consideration is exceptional feat, highlighting reliability stability methodology. Furthermore, validation values, ranging from 89 94% 2022, underscore robustness approach. Such consistently high over time groundbreaking achievement offers new dimension field hydrology. For hydrological community, implications are profound. Accurate provide critical data modeling analyzing effects resources, watershed management, quality. User, Kappa, Producer metrics used highlight model's performance suitability applications. These can aid development models, forecasting, decision-making processes, ultimately contributing more effective resource management environmental conservation. summary, study's mapping, relevance community demonstrate potential advanced tools significantly improve our understanding their resources management.

Язык: Английский

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Integrating community perceptions, scientific data and geospatial tools for sustainable water quality management

Results in Engineering, Год журнала: 2024, Номер 23, С. 102563 - 102563

Опубликована: Июль 14, 2024

Globally, ecosystems and human health are at risk due to declining river water quality. The current study focuses on the River Asan, Uttarakhand, which faces significant quality challenges various environmental, industrial, domestic factors. This research presents an exhaustive that intricately blends local community perceptions with scientific data, employing Geographic Information Systems map across seven critical locations along river. Participatory Rural Appraisal (PRA) systematic test methods were applied find objective of this study. highlights importance considering social, cultural, environmental factors in understanding issues. detailed, location-specific analysis, enriched by vast array insights, offers a unique lens through each site examined. Significant findings represent Nayagaon, from 2019 2023, rising temperature (1.6 °C increase) decreasing pH (7.3–6.5) observed. Reduced dissolved oxygen (9.7–6.1 mg/L) aligns concerns about quality, highlighting urgent need for interventions protect Asan its dependent communities. Integrating data provides nuanced issues, emphasizing targeted safeguard ecosystem well-being communities it, thereby offering valuable insights sustainable management.

Язык: Английский

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Dynamics of LULC changes, LST, vegetation health and climate interactions in Wetland buffer zone: A remote sensing perspective

Physics and Chemistry of the Earth Parts A/B/C, Год журнала: 2024, Номер 135, С. 103660 - 103660

Опубликована: Июнь 12, 2024

Язык: Английский

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Evaluating interdependencies of lake water surface temperature and clarity

The Science of The Total Environment, Год журнала: 2025, Номер 966, С. 178695 - 178695

Опубликована: Фев. 1, 2025

Язык: Английский

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Segment-driven anomaly detection in hyperspectral data using watershed technique

The Egyptian Journal of Remote Sensing and Space Science, Год журнала: 2024, Номер 27(2), С. 288 - 297

Опубликована: Апрель 4, 2024

A significant portion of hyperspectral image (HSI) analysis involves detecting anomalous pixels, which are indicative interesting phenomena or objects. One the main challenges is presence outlier and noisy pixels in background data due to variety spectral signatures heterogeneous HSIs. This article presents an effective approach using both spatial features for anomaly detection. The median filter with appropriate size driven by principal component information used cleaning background. Then, segmented watershed approach. detection occurs based on resolution calculating each pixel's distance from its segment via angle Euclidean distance. proposed Watershed Anomaly Detector (WAD), employs HSI properly. It also uses within detect pixels. WAD outperforms other methods simplicity conceptual clarity. Notably, underlying equation offers broader applicability segmentation tasks. Experiments three benchmark datasets show achieves higher accuracy faster execution versus state-of-the-art techniques. On average across methods, attained a 6.45% area under receiver operating characteristic (ROC) curve ran 26.95 s than detectors. effectively detects anomalies varied resolutions. results highlight stability, robustness computational efficiency diverse data. simultaneous effectiveness make well-suited near real-time applications.

Язык: Английский

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Spatiotemporal and vertical variability of water quality in lentic small water bodies: implications of varying rainfall and land use conditions

Environmental Science and Pollution Research, Год журнала: 2024, Номер unknown

Опубликована: Авг. 20, 2024

Язык: Английский

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Performance Evaluation of Gradient Descent Optimizers in Estuarine Turbidity Estimation with Multilayer Perceptron and Sentinel-2 Imagery

Hydrology, Год журнала: 2024, Номер 11(10), С. 164 - 164

Опубликована: Окт. 3, 2024

Accurate monitoring of estuarine turbidity patterns is important for maintaining aquatic ecological balance and devising informed management strategies. This study aimed to enhance the prediction by enhancing performance multilayer perceptron (MLP) network through introduction stochastic gradient descent (SGD) momentum (MGD). To achieve this, Sentinel-2 multispectral imagery was used as base on which spectral radiance properties waters were analyzed against field-measured data. In this case, blue, green, red, red edge, near-infrared shortwave bands selected empirical relationship establishment model development. Inverse distance weighting (IDW) spatial interpolation employed produce raster-based data area based The IDW image subsequently binarized using bi-level thresholding technique a Boolean image. Prior development, calibrated neural trained with sigmoid activation function optimizer then optimizer. produced from pixels turbidity. Empirical models developed uncalibrated bands. results all generally revealed stronger channel measured than other Among these models, MLP MGD coefficient determination (r2) value 0.92 band, followed green band SGD r2 values 0.75 0.72, respectively. relative error mean (REM) accurate compared models. Overall, demonstrated prospect deploying ensemble techniques in spatially constructing missing

Язык: Английский

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A Novel Approach for Ex Situ Water Quality Monitoring Using the Google Earth Engine and Spectral Indices in Chilika Lake, Odisha, India

ISPRS International Journal of Geo-Information, Год журнала: 2024, Номер 13(11), С. 381 - 381

Опубликована: Окт. 30, 2024

Chilika Lake, a RAMSAR site, is an environmentally and ecologically pivotal coastal lagoon in India facing significant emerging environmental challenges due to anthropogenic activities natural processes. Traditional situ water quality monitoring methods are often labor intensive time consuming. This study presents novel approach for ex located on the east coast of India, utilizing Google Earth Engine (GEE) spectral indices, such as Normalized Difference Turbidity Index (NDTI), Chlorophyll (NDCI), total suspended solids (TSS). The methodology involves integration multi-temporal satellite imagery advanced indices assess key parameters, turbidity, chlorophyll-a concentration, sediments. NDTI value Lake increased from 2019 2021, Automatic Water Extraction (AWEI) method estimated TSS concentration. results demonstrate effectiveness this providing accurate comprehensive assessments, which crucial sustainable management Lake. Maps visualization presented using GIS software. can effectively detect floating algal blooms, identify pollution sources, determine changes over time. Developing intuitive dashboards tools help stakeholders engage with data-driven insights, increase community participation conservation, sources.

Язык: Английский

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