Assessment and prediction of meteorological drought using machine learning algorithms and climate data

Climate Risk Management, Год журнала: 2024, Номер 45, С. 100630 - 100630

Опубликована: Янв. 1, 2024

Monitoring drought in semi-arid regions due to climate change is of paramount importance. This study, conducted Morocco's Upper Drâa Basin (UDB), analyzed data spanning from 1980 2019, focusing on the calculation indices, specifically Standardized Precipitation Index (SPI) and Evapotranspiration (SPEI) at multiple timescales (1, 3, 9, 12 months). Trends were assessed using statistical methods such as Mann-Kendall test Sen's Slope estimator. Four significant machine learning (ML) algorithms, including Random Forest, Voting Regressor, AdaBoost K-Nearest Neighbors evaluated predict SPEI values for both three 12-month periods. The algorithms' performance was measured indices. study revealed that distribution within UDB not uniform, with a discernible decreasing trend values. Notably, four ML algorithms effectively predicted specified demonstrated highest Nash-Sutcliffe Efficiency (NSE) values, ranging 0.74 0.93. In contrast, algorithm produced range 0.44 0.84. These research findings have potential provide valuable insights water resource management experts policymakers. However, it imperative enhance collection methodologies expand measurement sites improve representativeness reduce errors associated local variations.

Язык: Английский

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Time series trend analysis and forecasting of climate variability using deep learning in Thailand

Results in Engineering, Год журнала: 2024, Номер unknown, С. 102997 - 102997

Опубликована: Сен. 1, 2024

Язык: Английский

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Integrating remote sensing derived indices and machine learning algorithms for precise extraction of small surface water bodies in the lower Thoubal river watershed, India

Journal of Cleaner Production, Год журнала: 2023, Номер 422, С. 138563 - 138563

Опубликована: Авг. 25, 2023

Язык: Английский

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Contribution and behavioral assessment of physical and anthropogenic factors for soil erosion using integrated deep learning and game theory

Journal of Cleaner Production, Год журнала: 2023, Номер 416, С. 137689 - 137689

Опубликована: Июнь 26, 2023

Язык: Английский

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Multi-decade land cover/land use dynamics and future predictions for Zambia: 2000–2030

Discover Environment, Год журнала: 2024, Номер 2(1)

Опубликована: Апрель 20, 2024

Abstract Human LULCC is the many driver of environmental changes. Accurate and up-to-date current predicted information on important in land use planning natural resource management; however, Zambia, detailed insufficient. Therefore, this study assessed dynamics LULC change (2000–2020) future projections (2020–2030) for Zambia. The ESA CCI cover maps, which have been developed from Sentinel-2 images were used study. This dataset has a grid spatial resolution 300 m 2000, 2010 2020. 31 Classification reclassified into ten (10) local Classifications using r.class module QGIS 2.18.14. 2000 maps to simulate 2020 scenario Artificial Neural Network (Multi-layer Perception) algorithms Modules Land Use Change Evaluation (MOLUSCE) plugin predict 2030 classes. reference validate model. Predicted against observed map, Kappa (loc) statistic was 0.9869. patterns successfully simulated ANN-MLP with accuracy level 95%. classes 2010–2020 calibration period. types shows an increase built-up (71.44%) decrease cropland (0.73%) map. Dense forest (0.19%), grassland (0.85%) bare (1.37%) will reduce 2020–2030. However, seasonally flooded, sparse forest, shrub land, wetland water body marginally. largest other types. insights show that can be LULCC, generated employed National Adaptation Plans at regional national scale.

Язык: Английский

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Effectiveness of machine learning ensemble models in assessing groundwater potential in Lidder watershed, India

Acta Geophysica, Год журнала: 2023, Номер 72(4), С. 2843 - 2856

Опубликована: Ноя. 30, 2023

Язык: Английский

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Spatiotemporal distribution of groundwater drought using GRACE-based satellite estimates: a case study of Lower Gangetic Basin, India

Environmental Monitoring and Assessment, Год журнала: 2024, Номер 196(2)

Опубликована: Янв. 16, 2024

Язык: Английский

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Analyzing and forecasting climate variability in Nainital district, India using non-parametric methods and ensemble machine learning algorithms

Theoretical and Applied Climatology, Год журнала: 2024, Номер 155(6), С. 4749 - 4765

Опубликована: Март 7, 2024

Язык: Английский

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The trends and spatiotemporal variability of temperature and rainfall in Hulbarag district, Silte Zone, Ethiopia

Heliyon, Год журнала: 2024, Номер 10(11), С. e31646 - e31646

Опубликована: Май 22, 2024

Ethiopia gets its agricultural water primarily from rainfall. This study was intended to investigate current climate variability and trends across space time. Daily gridded temperature rainfall data 1993 2022 in the Hulbarag district, Silte Zone of obtained Ethiopian National Metrological Institute Climate Hazard Group Infrared Precipitation with Station. The were analyzed using Mann-Kendall trend test, Sen's slope, coefficient variation, precipitation concentration index, anomaly index. results indicated that annual, spring, summer revealed statistically significant decreasing at Sankura stations, magnitude -13.4,-11.6, and-10.6mm per year -6.8,-3.6 -.10.9 mm respectively. Conversely, autumn winter season showed increasing 5.1 5.5mm 3.4 1.84 consecutively. Between 43% 47% observation periods had negative anomalies. average yearly temperature, minimum maximum temperatures Fonko stations all displayed trends, a 0.091°C, 0.009°C 0.051 °C 0.03°C,0.01°C 0.0022°C successively. It is advisable develop farming system climate-resilient by improving adaptive capacity wheat maize-growing farmers expanding availability early maturing seeds, changing crop calendars, enhancing proactive credible information services.

Язык: Английский

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A Comparative Spatiotemporal Analysis for Long-Term Trends of Hydrometeorological Variables in Maritsa River Basin

Doğal Afetler ve Çevre Dergisi, Год журнала: 2025, Номер 11(1), С. 268 - 289

Опубликована: Янв. 25, 2025

Revealing long-term trends in hydrometeorological variables plays a critical role the sustainable management and planning of water resources. These analyses are necessary to understand climate change impacts, taking precautions for natural disasters, plan agricultural activities, develop strategies. The aim this study is examine changes monthly annual total precipitation evapotranspiration values Maritsa River Basin, transboundary basin between Bulgaria, Greece, Türkiye. For this, 1982-2023 years were taken from Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) data set, European Reanalysis 5th Generation-Land (ERA5-Land) set. Mann-Kendall, Sen's slope estimator, Innovative Trend Analysis (ITA) methods used determine trends. According test results, there statistically significant increase within 95% confidence interval 99% interval. Specifically all three positive observed October, January, May June. In trend analysis, except November, December, June July. increases visualized using graphical method ITA. Significant increasing both reveal hydrological cycle basin. results can be solving problems related area.

Язык: Английский

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