Matches in SemOpenAlex for { <https://semopenalex.org/work/W3184696357> ?p ?o ?g. }
- W3184696357 endingPage "113283" @default.
- W3184696357 startingPage "113283" @default.
- W3184696357 abstract "Severe drought events in recent decades and their catastrophic effects have called for drought prediction and monitoring needed for developing drought readiness plans and mitigation measures. This study used a fusion-based framework for meteorological drought modeling for the historical (1983–2016) and future (2020–2050) periods using remotely sensed datasets versus ground-based observations and climate change scenarios. To this aim, high-resolution remotely sensed precipitation datasets, including PERSIANN-CDR and CHIRPS (multi-source products), ERA5 (reanalysis datasets), and GPCC (gauge-interpolated datasets), were employed to estimate non-parametric SPI (nSPI) as a meteorological drought index against local observations. For more accurate drought evaluation, all stations were classified into different clusters using the K-means clustering algorithm based on ground-based nSPI. Then, four Individual Artificial Intelligence (IAI) models, including Adaptive Neuro-Fuzzy Inference System (ANFIS), Group Method of Data Handling (GMDH), Multi-Layer Perceptron (MLP), and General Regression Neural Network (GRNN), were developed for drought modeling within each cluster. Finally, two advanced fusion-based methods, including Multi-Model Super Ensemble (MMSE) as a linear weighted model and a nonlinear model called machine learning Random Forest (RF), combined results by IAI models using different remotely sensed datasets. The proposed framework was implemented to simulate each remotely sensed precipitation data for the future based on CORDEX regional climate models (RCMs) under RCP4.5 and RCP8.5 scenarios for drought projection. The efficiency of IAI and fusion models was evaluated using statistical error metrics, including the coefficient of determination (R2), Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE). The proposed methodology was employed in the Gavkhooni basin of Iran, and results showed that the RF model with the lowest estimation error (RMSE of 0.391 and R2 of 0.810) had performed well compared to all other models. Finally, the resilience, vulnerability, and frequency of probability metrics indicated that the 12-month time scale of drought affected the basin more severely than other time scales." @default.
- W3184696357 created "2021-08-02" @default.
- W3184696357 creator A5015768486 @default.
- W3184696357 creator A5062207060 @default.
- W3184696357 creator A5079265224 @default.
- W3184696357 creator A5081654260 @default.
- W3184696357 date "2021-11-01" @default.
- W3184696357 modified "2023-10-03" @default.
- W3184696357 title "Fusion-based framework for meteorological drought modeling using remotely sensed datasets under climate change scenarios: Resilience, vulnerability, and frequency analysis" @default.
- W3184696357 cites W1584709029 @default.
- W3184696357 cites W1776911451 @default.
- W3184696357 cites W1964386488 @default.
- W3184696357 cites W1967064886 @default.
- W3184696357 cites W1985982446 @default.
- W3184696357 cites W1987213677 @default.
- W3184696357 cites W1989839776 @default.
- W3184696357 cites W1994169669 @default.
- W3184696357 cites W1999687643 @default.
- W3184696357 cites W2000167890 @default.
- W3184696357 cites W2015522309 @default.
- W3184696357 cites W2015927604 @default.
- W3184696357 cites W2020146279 @default.
- W3184696357 cites W2021510303 @default.
- W3184696357 cites W2024966118 @default.
- W3184696357 cites W2030811593 @default.
- W3184696357 cites W2030997108 @default.
- W3184696357 cites W2036133701 @default.
- W3184696357 cites W2039885219 @default.
- W3184696357 cites W2041490648 @default.
- W3184696357 cites W2051335173 @default.
- W3184696357 cites W2079436405 @default.
- W3184696357 cites W2079605058 @default.
- W3184696357 cites W2081491062 @default.
- W3184696357 cites W2082819436 @default.
- W3184696357 cites W2083082288 @default.
- W3184696357 cites W2099779069 @default.
- W3184696357 cites W2104157175 @default.
- W3184696357 cites W2108564811 @default.
- W3184696357 cites W2113457156 @default.
- W3184696357 cites W2118347479 @default.
- W3184696357 cites W2125500141 @default.
- W3184696357 cites W2150516764 @default.
- W3184696357 cites W2170280355 @default.
- W3184696357 cites W2171984413 @default.
- W3184696357 cites W2228116959 @default.
- W3184696357 cites W2261645655 @default.
- W3184696357 cites W2268461985 @default.
- W3184696357 cites W2288810513 @default.
- W3184696357 cites W2301692565 @default.
- W3184696357 cites W2313560559 @default.
- W3184696357 cites W2337744400 @default.
- W3184696357 cites W2417060394 @default.
- W3184696357 cites W2508360408 @default.
- W3184696357 cites W2512103537 @default.
- W3184696357 cites W2536008880 @default.
- W3184696357 cites W2564039347 @default.
- W3184696357 cites W2564251257 @default.
- W3184696357 cites W2586821267 @default.
- W3184696357 cites W2592397024 @default.
- W3184696357 cites W2593356872 @default.
- W3184696357 cites W2741291720 @default.
- W3184696357 cites W2753679582 @default.
- W3184696357 cites W2765835342 @default.
- W3184696357 cites W2781692195 @default.
- W3184696357 cites W2788411055 @default.
- W3184696357 cites W2802090116 @default.
- W3184696357 cites W2811389973 @default.
- W3184696357 cites W2904391595 @default.
- W3184696357 cites W2910752046 @default.
- W3184696357 cites W2911964244 @default.
- W3184696357 cites W2913267705 @default.
- W3184696357 cites W2922882700 @default.
- W3184696357 cites W2973015150 @default.
- W3184696357 cites W2976179756 @default.
- W3184696357 cites W2983317741 @default.
- W3184696357 cites W2991891763 @default.
- W3184696357 cites W3018887336 @default.
- W3184696357 cites W3036309822 @default.
- W3184696357 cites W3087438331 @default.
- W3184696357 cites W3108807865 @default.
- W3184696357 cites W3124635350 @default.
- W3184696357 cites W3124919081 @default.
- W3184696357 cites W3125866485 @default.
- W3184696357 doi "https://doi.org/10.1016/j.jenvman.2021.113283" @default.
- W3184696357 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34280857" @default.
- W3184696357 hasPublicationYear "2021" @default.
- W3184696357 type Work @default.
- W3184696357 sameAs 3184696357 @default.
- W3184696357 citedByCount "11" @default.
- W3184696357 countsByYear W31846963572021 @default.
- W3184696357 countsByYear W31846963572022 @default.
- W3184696357 countsByYear W31846963572023 @default.
- W3184696357 crossrefType "journal-article" @default.
- W3184696357 hasAuthorship W3184696357A5015768486 @default.
- W3184696357 hasAuthorship W3184696357A5062207060 @default.
- W3184696357 hasAuthorship W3184696357A5079265224 @default.
- W3184696357 hasAuthorship W3184696357A5081654260 @default.
- W3184696357 hasConcept C105795698 @default.