Hydrochemical properties of groundwater and land use and land cover changes impact on agricultural productivity: An empirical observation and integrated framework approaches

Journal of Geochemical Exploration, Journal Year: 2024, Volume and Issue: 258, P. 107402 - 107402

Published: Jan. 8, 2024

Language: Английский

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Spatial and temporal classification and prediction of LULC in Brahmani and Baitarni basin using integrated cellular automata models

Environmental Monitoring and Assessment, Journal Year: 2024, Volume and Issue: 196(2)

Published: Jan. 6, 2024

Language: Английский

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GIS-based assessment of soil erosion and sediment yield using the revised universal soil loss equation (RUSLE) model in the Murredu Watershed, Telangana, India

HydroResearch, Journal Year: 2024, Volume and Issue: 7, P. 315 - 325

Published: Jan. 1, 2024

The current investigation was conducted in the Murredu watershed, situated India. essential datasets, such as digital elevation model (DEM), soil, land use cover (LULC), and rainfall parameters, were processed analysed using a Geographic Information System (GIS) environment. research utilised revised universal soil loss equation (RUSLE) analysis to assess mean watershed. annual calculated be 14.06 t/ha/year, which is high erosion risk. RUSLE results indicate good outcome with an accuracy of 72.8%. Furthermore, area revealed that sub-watersheds (SW) 2 SW 14 had maximum minimum loss, respectively. SDR for known Murredu, 0.227. watershed outlet received sediment transfer rate 3.19 t/ha/year. Through investigation, it determined average yield, while 11 minimum. This provides valuable insights stakeholders, decision-makers, policymakers regarding sustainable ways managing watersheds.

Language: Английский

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Towards improved USLE-based soil erosion modelling in India: A review of prevalent pitfalls and implementation of exemplar methods

Earth-Science Reviews, Journal Year: 2021, Volume and Issue: 221, P. 103786 - 103786

Published: Aug. 28, 2021

Language: Английский

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Deep learning and boosting framework for piping erosion susceptibility modeling: spatial evaluation of agricultural areas in the semi-arid region

Geocarto International, Journal Year: 2021, Volume and Issue: 37(16), P. 4628 - 4654

Published: Feb. 19, 2021

Piping erosion is one of the water erosions that cause significant changes in landscape, leading to environmental degradation. To prevent losses resulting from tube growth and enable sustainable development, developing high-precision predictive algorithms for piping essential. Boosting a classic algorithm has been successfully applied diverse computer vision tasks. Therefore, this work investigated performance Boosted Linear Model (BLM), Regression Tree (BRT), Generalized (Boost GLM), Deep models susceptibility mapping Zarandieh Watershed located Markazi province Iran. A inventory map including 152 locations was prepared training testing. 18 initial predisposing factors (altitude, slope, plan curvature, profile distance river, drainage density, road, rainfall, land use, soil type, bulk CEC, pH, clay, silt, sand, topographical position index (TPI), topographic wetness (TWI)) derived multiple remote sensing (RS) sources determine prone areas. The most were selected using multi-collinearity analysis which indicates linear correlations between factors. Finally, results evaluated Sensitivity, Specificity, Positive values (PPV) Negative value (NPV), Receiver Operation characteristic (ROC) curve. best Sensitivity (0.80), Specificity (0.84), PPV (0.85), NPV (0.79), ROC (0.93), obtained by model. study agricultural use showed 41% lands are very sensitive erosion. This outcome will natural resource managers local planners assess take effective decisions minimize damages accurately identifying vulnerable Hence, research proved model's ability comparison other popular methods such as BLM, BRT, Boost GLM.

Language: Английский

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An Integrated Modelling Approach to Urban Growth and Land Use/Cover Change

Land, Journal Year: 2022, Volume and Issue: 11(10), P. 1715 - 1715

Published: Oct. 3, 2022

Long-term sustainable development in developing countries requires researching and projecting urban physical growth land use/land cover change (LUCC). This research fills a gap the literature by exploring issues of modelling coupled LUCC growth, their causes, role policymakers. Tabriz metropolitan area (TMA), located at north-west Iran, was chosen as case study to design an integrated framework using four well-established methods: cellular automata (CA), Markov chains (MC), logistic regression (LR), stepwise weight assessment ratio analysis (SWARA). Northern, north-west, central TMA were affected worst urbanisation loss cultivated grassland between 1990 2020. The accessibility arterial roadways proximity major cities influenced these changes. Three scenarios characterise dynamics: uncontrolled scenario (UGS) historical trend (HTGS) foresee significant continued expansion above long-term average 2050, while environmental protection (EPGS) promotes compact urbanisation. methods used this may be various contexts examine temporal spatial dynamics growth.

Language: Английский

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Integrated Use of GIS and USLE Models for LULC Change Analysis and Soil Erosion Risk Assessment in the Hulan River Basin, Northeastern China

Water, Journal Year: 2024, Volume and Issue: 16(2), P. 241 - 241

Published: Jan. 10, 2024

The Hulan River Basin is located in the black soil region of northeast China. This an important food-producing area and susceptibility to erosion increases risk erosion, which a serious environmental problem that affects agricultural productivity, water supply, other aspects region. In this paper, changes LULC (land use land cover) basin between 2001 2020 were thoroughly analysed using GIS (geographic information system) USLE (universal loss equation) models. was also studied hot spots identified target those remained significant even under implementation conservation measures. Precipitation data used obtain R factor distribution, classification adopted assess C employed estimate K DEM (Digital Elevation Model) generate LS map, slope considered produce P distribution map. These factors based on model parameters USLE. findings change analysis over last 20 years indicated that, while there have been nonobvious changes, has continued occupy bulk Basin. increase areas for human activities most notable trend. 2001, model-predicted rate varied 0 120 t/ha/yr, with average 4.63 t/ha/yr. By 2020, estimated 193 7.34 classified into five categories. Most categories encompassed extremely low-risk levels and, past years, northeastern hilly regions experienced highest concentration areas. mountainous comprised are susceptible exhibited high production activities. fact, combined modelling yielded mapping classes; these results could further assist local governments improving efforts.

Language: Английский

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A RUSLE-based comprehensive strategy to assess soil erosion in a riverine country, Bangladesh

Environmental Earth Sciences, Journal Year: 2024, Volume and Issue: 83(6)

Published: March 1, 2024

Language: Английский

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An integrated GIS approach to analyze the impact of land use change and land cover alteration on ground water potential level: A study in Kangsabati Basin, India

Groundwater for Sustainable Development, Journal Year: 2020, Volume and Issue: 11, P. 100399 - 100399

Published: April 21, 2020

Language: Английский

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Measuring Urban Land Use Change and Sprawl Using Geospatial Techniques: A Study on Purulia Municipality, West Bengal, India

Journal of the Indian Society of Remote Sensing, Journal Year: 2020, Volume and Issue: 49(2), P. 433 - 448

Published: Nov. 4, 2020

Language: Английский

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