Beyond Traditional Metrics: Exploring the Potential of Hybrid Algorithms for Drought Characterization and Prediction in the Tromso Region, Norway DOI Creative Commons
Sertaç Oruç, Türker Tuğrul, Mehmet Ali Hınıs

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(17), P. 7813 - 7813

Published: Sept. 3, 2024

Meteorological drought, defined as a decrease in the average amount of precipitation, is among most insidious natural disasters. Not knowing when drought will occur (its onset) makes it difficult to predict and monitor it. Scientists face significant challenges accurately predicting monitoring global droughts, despite using various machine learning techniques indices developed recent years. Optimization methods hybrid models are being overcome these create effective policies. In this study, analysis was conducted The Standard Precipitation Index (SPI) with monthly precipitation data from 1920 2022 Tromsø region. Models different input structures were created obtained SPI values. These then analyzed Adaptive Neuro-Fuzzy Inference System (ANFIS) by means optimization methods: Particle Swarm (PSO), Genetic Algorithm (GA), Grey Wolf (GWO), Artificial Bee Colony (ABC), PSO Support Vector Machine (SVM-PSO). Correlation coefficient (r), Root Mean Square Error (RMSE), Nash–Sutcliffe efficiency (NSE), RMSE-Standard Deviation Ratio (RSR) served performance evaluation criteria. results study demonstrated that, while successful all commonly used algorithms except for ANFIS-GWO, best values SPI12 achieved ANFIS-ABC-M04, exhibiting r: 0.9516, NSE: 0.9054, RMSE: 0.3108.

Language: Английский

Prediction of meteorological drought and standardized precipitation index based on the random forest (RF), random tree (RT), and Gaussian process regression (GPR) models DOI
Ahmed Elbeltagi,

Chaitanya B. Pande,

Manish Kumar

et al.

Environmental Science and Pollution Research, Journal Year: 2023, Volume and Issue: 30(15), P. 43183 - 43202

Published: Jan. 17, 2023

Language: Английский

Citations

73
Assessment and prediction of meteorological drought using machine learning algorithms and climate data DOI Creative Commons

Khalid En-nagre,

Mourad Aqnouy, Ayoub Ouarka

et al.

Climate Risk Management, Journal Year: 2024, Volume and Issue: 45, P. 100630 - 100630

Published: Jan. 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.

Language: Английский

Citations

20
Comparison of LSTM and SVM methods through wavelet decomposition in drought forecasting DOI
Türker Tuğrul, Mehmet Ali Hınıs, Sertaç Oruç

et al.

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(1)

Published: Jan. 1, 2025

Language: Английский

Citations

3
Spatio-temporal analysis of drought in Southern Italy: a combined clustering-forecasting approach based on SPEI index and artificial intelligence algorithms DOI
Fabio Di Nunno, Francesco Granata

Stochastic Environmental Research and Risk Assessment, Journal Year: 2023, Volume and Issue: 37(6), P. 2349 - 2375

Published: Feb. 11, 2023

Language: Английский

Citations

26
Utilizing machine learning and CMIP6 projections for short-term agricultural drought monitoring in central Europe (1900–2100) DOI Creative Commons
Safwan Mohammed, Sana Arshad, Firas Alsilibe

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 633, P. 130968 - 130968

Published: Feb. 28, 2024

Water availability for agricultural practices is dynamically influenced by climatic variables, particularly droughts. Consequently, the assessment of drought events directly related to strategic water management in sector. The application machine learning (ML) algorithms different scenarios variables a new approach that needs be evaluated. In this context, current research aims forecast short-term i.e., SPI-3 from predictors under historical (1901–2020) and future (2021–2100) employing (bagging (BG), random forest (RF), decision table (DT), M5P) Hungary, Central Europe. Three meteorological stations namely, Budapest (BD) (central Hungary), Szeged (SZ) (east south Szombathely (SzO) (west Hungary) were selected agriculture Standardized Precipitation Index (SPI-3) long run. For purpose, ensemble means three global circulation models GCMs CMIP6 are being used get projected time series indicators (i.e., rainfall R, mean temperature T, maximum Tmax, minimum Tmin two socioeconomic pathways (SSP2-4.5 SSP4-6.0). results study revealed more severe extreme past decades, which increase near (2021–2040). Man-Kendall test (Tau) along with Sen's slope (SS) also an increasing trend period Tau = −0.2, SS −0.05, −0.12, −0.09 SSP2-4.5 −0.1, −0.08 SSP4-6.0. Implementation ML scenarios: SC1 (R + T Tmax Tmin), SC2 (R), SC3 T)) at BD station RF-SC3 lowest RMSE RFSC3-TR 0.33, highest NSE 0.89 performed best forecasting on dataset. Hence, was implemented remaining (SZ SzO) 1901 2100 Interestingly, forecasted SSP2-4.5, 0.34 0.88 SZ 0.87 SzO SSP2-4.5. our findings recommend using provide accurate predictions R projections. This could foster gradual shift towards sustainability improve resources. However, concrete plans still needed mitigate negative impacts 2028, 2030, 2031, 2034. Finally, validation RF prediction large dataset makes it significant use other studies facilitates making disaster strategies.

Language: Английский

Citations

15
Improvement of drought forecasting by means of various machine learning algorithms and wavelet transformation DOI Creative Commons
Türker Tuğrul, Mehmet Ali Hınıs

Acta Geophysica, Journal Year: 2024, Volume and Issue: unknown

Published: July 1, 2024

Abstract Drought, which is defined as a decrease in average rainfall amounts, one of the most insidious natural disasters. When it starts, people may not be aware it, why droughts are difficult to monitor. Scientists have long been working predict and monitor droughts. For this purpose, they developed many methods, such drought indices, Standardized Precipitation Index (SPI). In study, SPI was used detect droughts, machine learning algorithms, including support vector machines (SVM), artificial neural networks, random forest, decision tree, were addition, 3 different statistical criteria, correlation coefficient ( r ), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), investigate model performance values. The wavelet transform (WT) also applied improve performance. One areas impacted by Turkey Konya Closed Basin, geographically positioned center country among top grain-producing regions Turkey. Apa Dam significant water resources area. It provides fertile fields its vicinity affected selected study Meteorological data, monthly precipitation, that could represent region obtained between 1955 2020 from general directorate state works meteorology. According findings, M04 model, whose input structure using SPI, various time steps, data delayed up 5 months, precipitation preceding month (time t − 1), produced best results out all models examined algorithms. Among SVM has achieved successful only before applying WT but after WT. M04, with (NSE = 0.9942, RMSE 0.0764, R 0.9971).

Language: Английский

Citations

10
Spatio-temporal distribution and prediction of agricultural and meteorological drought in a Mediterranean coastal watershed via GIS and machine learning DOI
Siham Acharki, Sudhir Kumar Singh, Edivando Vítor do Couto

et al.

Physics and Chemistry of the Earth Parts A/B/C, Journal Year: 2023, Volume and Issue: 131, P. 103425 - 103425

Published: June 1, 2023

Language: Английский

Citations

20
Performance of machine learning algorithms for multi-step ahead prediction of reference evapotranspiration across various agro-climatic zones and cropping seasons DOI
Nehar Mandal, Kironmala Chanda

Journal of Hydrology, Journal Year: 2023, Volume and Issue: 620, P. 129418 - 129418

Published: March 22, 2023

Language: Английский

Citations

19
Standardized precipitation evapotranspiration index (SPEI) estimated using variant long short-term memory network at four climatic zones of China DOI

Juan Dong,

Liwen Xing, Ningbo Cui

et al.

Computers and Electronics in Agriculture, Journal Year: 2023, Volume and Issue: 213, P. 108253 - 108253

Published: Sept. 20, 2023

Language: Английский

Citations

18
Long-term drought prediction using deep neural networks based on geospatial weather data DOI

Alexander Marusov,

Vsevolod Grabar,

Yury Maximov

et al.

Environmental Modelling & Software, Journal Year: 2024, Volume and Issue: 179, P. 106127 - 106127

Published: June 28, 2024

Language: Английский

Citations

7