Improvement of drought forecasting by means of various machine learning algorithms and wavelet transformation

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

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Examining evolutionary scale modeling‐derived different‐dimensional embeddings in the antimicrobial peptide classification through a KNIME workflow

Protein Science, Journal Year: 2024, Volume and Issue: 33(4)

Published: March 19, 2024

Abstract Molecular features play an important role in different bio‐chem‐informatics tasks, such as the Quantitative Structure–Activity Relationships (QSAR) modeling. Several pre‐trained models have been recently created to be used downstream either by fine‐tuning a specific model or extracting feed traditional classifiers. In this regard, new family of Evolutionary Scale Modeling (termed ESM‐2 models) was introduced, demonstrating outstanding results protein structure prediction benchmarks. Herein, we studied usefulness different‐dimensional embeddings derived from classify antimicrobial peptides (AMPs). To end, built KNIME workflow use same modeling methodology across experiments order guarantee fair analyses. As result, 640‐ and 1280‐dimensional 30‐ 33‐layer models, respectively, are most valuable since statistically better performances were achieved QSAR them. We also fused it concluded that fusion contributes getting than using single model. Frequency studies revealed only portion is for tasks between 43% 66% never used. Comparisons regarding state‐of‐the‐art deep learning (DL) confirm when performing methodologically principled AMPs, non‐DL based yield comparable‐to‐superior DL‐based models. The developed available‐freely at https://github.com/cicese-biocom/classification-QSAR-bioKom . This can avoid unfair comparisons computational methods, well propose

Language: Английский

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Modeling of meteorological, agricultural, and hydrological droughts in semi-arid environments with various machine learning and discrete wavelet transform

Theoretical and Applied Climatology, Journal Year: 2023, Volume and Issue: 154(1-2), P. 413 - 451

Published: Aug. 18, 2023

Language: Английский

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Short-term drought Index forecasting for hot and semi-humid climate Regions: A novel empirical Fourier decomposition-based ensemble Deep-Random vector functional link strategy

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 217, P. 108609 - 108609

Published: Jan. 11, 2024

Language: Английский

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Seasonal rainfall pattern using coupled neural network-wavelet technique of southern Uttarakhand, India

Theoretical and Applied Climatology, Journal Year: 2024, Volume and Issue: 155(6), P. 5185 - 5201

Published: March 28, 2024

Language: Английский

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Field scale wheat yield prediction using ensemble machine learning techniques

Smart Agricultural Technology, Journal Year: 2024, Volume and Issue: 9, P. 100543 - 100543

Published: Sept. 7, 2024

Language: Английский

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Machine learning models development for accurate multi-months ahead drought forecasting: Case study of the Great Lakes, North America

PLoS ONE, Journal Year: 2023, Volume and Issue: 18(10), P. e0290891 - e0290891

Published: Oct. 31, 2023

The Great Lakes are critical freshwater sources, supporting millions of people, agriculture, and ecosystems. However, climate change has worsened droughts, leading to significant economic social consequences. Accurate multi-month drought forecasting is, therefore, essential for effective water management mitigating these impacts. This study introduces the Multivariate Standardized Lake Water Level Index (MSWI), a modified index that utilizes level data collected from 1920 2020. Four hybrid models developed: Support Vector Regression with Beluga whale optimization (SVR-BWO), Random Forest (RF-BWO), Extreme Learning Machine (ELM-BWO), Regularized ELM (RELM-BWO). forecast droughts up six months ahead Superior Michigan-Huron. best-performing model is then selected remaining three lakes, which have not experienced severe in past 50 years. results show incorporating BWO improves accuracy all classical models, particularly turning points. Among RELM-BWO achieves highest accuracy, surpassing both by margin (7.21 76.74%). Furthermore, Monte-Carlo simulation employed analyze uncertainties ensure reliability forecasts. Accordingly, reliably forecasts lead time ranging 2 6 months. study's findings offer valuable insights policymakers, managers, other stakeholders better prepare mitigation strategies.

Language: Английский

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Machine learning-driven habitat suitability modeling of Suaeda aegyptiaca for sustainable industrial cultivation in saline regions

Industrial Crops and Products, Journal Year: 2025, Volume and Issue: 225, P. 120427 - 120427

Published: Jan. 11, 2025

Language: Английский

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Modeling the effect of meteorological drought on lake level changes with machine learning techniques

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 227 - 246

Published: Jan. 1, 2025

Language: Английский

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Predicting Agricultural Drought in Central Europe by Using Machine Learning Algorithms

Journal of Agriculture and Food Research, Journal Year: 2025, Volume and Issue: unknown, P. 101783 - 101783

Published: March 1, 2025

Language: Английский

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