Unraveling climate trends in the mediterranean: a hybrid machine learning and statistical approach DOI Creative Commons
Mutaz AlShafeey

Modeling Earth Systems and Environment, Journal Year: 2024, Volume and Issue: 10(5), P. 6255 - 6277

Published: Aug. 6, 2024

Abstract This study presents a comprehensive spatiotemporal analysis of sea surface temperatures (SST) and air (TAS) across 15 Mediterranean coastal stations, leveraging centennial-scale data to analyze regional climate dynamics. The modeling framework integrates three sequential phases: preprocessing, statistical analysis, advanced machine learning techniques, creating robust analytical pipeline. preprocessing phase harmonizes diverse datasets, addresses missing values, applies transformations ensure consistency. employs the Pettitt test for change point detection linear trend unveil underlying patterns. utilizes K-means clustering regime classification implements tailored Convolutional Neural Networks (CNNs) cluster-specific future anomaly projections. Results marked anthropogenic signal, with contemporary observations consistently surpassing historical baselines. Breakpoint analyses assessments reveal heterogeneous climatic shifts, pronounced warming in northern Mediterranean. Notably, Nice Ajaccio exhibit highest SST increases (0.0119 0.0113 °C/decade, respectively), contrasting more modest trends Alexandria (0.0052 °C/decade) Antalya (0.0047 eastern application CNN projections provides granular insights into differential trajectories. By 2050, cooler northwestern zones are projected experience dramatic anomalies approximately 3 °C above average, corresponding TAS 2.5 °C. In contrast, warmer southern regions display subdued patterns, 1.5–2.5 by mid-century. research’s importance is highlighted its potential inform adaptation strategies contribute theoretical understanding dynamics, advancing efforts.

Language: Английский

Data driven pathway analysis and forecast of global warming and sea level rise DOI Creative Commons
Jiecheng Song, Guanchao Tong, Jiayou Chao

et al.

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: April 4, 2023

Climate change is a critical issue of our time, and its causes, pathways, forecasts remain topic broader discussion. In this paper, we present novel data driven pathway analysis framework to identify the key processes behind mean global temperature sea level rise, forecast magnitude their increase from 2100. Based on historical dynamic statistical modeling alone, have established causal pathways that connect increasing greenhouse gas emissions level, with intermediate links encompassing humidity, ice coverage, glacier mass, but not for sunspot numbers. Our results indicate if no action taken curb anthropogenic emissions, average would rise an estimated 3.28 °C (2.46-4.10 °C) above pre-industrial while be 573 mm (474-671 mm) 2021 by However, countries adhere emission regulations outlined in United Nations Conference Change (COP26), lessen 1.88 (1.43-2.33 albeit still higher than targeted 1.5 °C, reduce 449 (389-509

Language: Английский

Citations

37
A Machine Learning-Based Framework for Water Quality Index Estimation in the Southern Bug River DOI Open Access
Adil Masood, Majid Niazkar, Mohammad Zakwan

et al.

Water, Journal Year: 2023, Volume and Issue: 15(20), P. 3543 - 3543

Published: Oct. 11, 2023

River water quality is of utmost importance because the river not only one key resources but also a natural habitat serving its surrounding environment. In bid to address whether it has qualified quality, various analytics are required be considered, challenging measure all them frequently along reach. Therefore, estimating index (WQI) incorporating several weighted useful approach assess in rivers. This study explored applications ten machine learning (ML) models estimate WQI for Southern Bug River, which second-longest Ukraine. The ML methods considered this include artificial neural networks (ANNs), Support Vector Regressor (SVR), Extreme Learning Machine, Decision Tree Regressor, random forest, AdaBoost (AB), Gradient Boosting XGBoost (XGBR), Gaussian process (GP), and K-nearest neighbors (KNN). Each data measurement consists nine (NH4, BOD5, suspended solids, DO, NO3, NO2, SO4, PO4, Cl), while quantity more than 2700 points. results indicated that demonstrate satisfactory performance predicting WQI. However, GP outperformed other models, followed by XGBR, SVR, KNN. Furthermore, ANN AB demonstrated relatively weaker performance. Moreover, reliability assessment conducted on both training testing datasets confirmed comparative analysis. Overall, enhance assertion can sufficiently predict WQI, thereby enhancing management.

Language: Английский

Citations

21
Atmosphere air temperature forecasting using the honey badger optimization algorithm: on the warmest and coldest areas of the world DOI Creative Commons
Jincheng Zhou, Dan Wang, Shahab S. Band

et al.

Engineering Applications of Computational Fluid Mechanics, Journal Year: 2023, Volume and Issue: 17(1)

Published: Feb. 24, 2023

Precisely forecasting air temperature as a significant meteorological parameter has critical role in environment quality management. Hence, this study employs hybrid intelligent model for accurately monthly one to three times ahead the hottest and coldest regions of world. The contains artificial neural network (ANN) hybridized with powerful hetaeristic Honey Badger Algorithm (HBA-ANN). average mutual information (AMI) technique is employed find optimal time delay values variable different horizons. Finally, performance developed compared classical ANN Gene Expression Programming (GEP) using some statistical criteria, Taylor scatter diagrams. Results indicated that each horizon, HBA-ANN lowest distance from observation points based on diagram, high NSE R2, low RMSE, MAE, RSR outperformed GEP models both training testing phases. could increase accuracy model. This model's precise supports case it be forecast other environmental parameters.

Language: Английский

Citations

19
Improving PM2.5 prediction in New Delhi using a hybrid extreme learning machine coupled with snake optimization algorithm DOI Creative Commons
Adil Masood, Mohammed Majeed Hameed, Aman Srivastava

et al.

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: Nov. 29, 2023

Fine particulate matter (PM2.5) is a significant air pollutant that drives the most chronic health problems and premature mortality in big metropolitans such as Delhi. In context, accurate prediction of PM2.5 concentration critical for raising public awareness, allowing sensitive populations to plan ahead, providing governments with information alerts. This study applies novel hybridization extreme learning machine (ELM) snake optimization algorithm called ELM-SO model forecast concentrations. The has been developed on quality inputs meteorological parameters. Furthermore, hybrid compared individual models, Support Vector Regression (SVR), Random Forest (RF), Extreme Learning Machines (ELM), Gradient Boosting Regressor (GBR), XGBoost, deep known Long Short-Term Memory networks (LSTM), forecasting results suggested exhibited highest level predictive performance among five testing value squared correlation coefficient (R2) 0.928, root mean square error 30.325 µg/m3. study's findings suggest technique valuable tool accurately concentrations could help advance field forecasting. By developing state-of-the-art pollution models incorporate ELM-SO, it may be possible understand better anticipate effects human environment.

Language: Английский

Citations

19
Improving multi-month hydrological drought forecasting in a tropical region using hybridized extreme learning machine model with Beluga Whale Optimization algorithm DOI
Mohammed Majeed Hameed, Siti Fatin Mohd Razali, Wan Hanna Melini Wan Mohtar

et al.

Stochastic Environmental Research and Risk Assessment, Journal Year: 2023, Volume and Issue: 37(12), P. 4963 - 4989

Published: Sept. 9, 2023

Language: Английский

Citations

16
Daily air temperature forecasting using LSTM-CNN and GRU-CNN models DOI
İhsan Uluocak, Mehmet Bilgili

Acta Geophysica, Journal Year: 2023, Volume and Issue: 72(3), P. 2107 - 2126

Published: Dec. 5, 2023

Language: Английский

Citations

14
Machine learning models development for accurate multi-months ahead drought forecasting: Case study of the Great Lakes, North America DOI Creative Commons
Mohammed Majeed Hameed, Siti Fatin Mohd Razali, Wan Hanna Melini Wan Mohtar

et al.

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

Citations

11
Validation of machine learning models for heavy metals bioavailability prediction: A comparative study DOI
Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Mohammed Majeed Hameed, Ziaul Haq Doost

et al.

Journal of environmental chemical engineering, Journal Year: 2025, Volume and Issue: unknown, P. 116749 - 116749

Published: April 1, 2025

Language: Английский

Citations

0
Deep learning versus hybrid regularized extreme learning machine for multi-month drought forecasting: A comparative study and trend analysis in tropical region DOI Creative Commons
Mohammed Majeed Hameed, Siti Fatin Mohd Razali, Wan Hanna Melini Wan Mohtar

et al.

Heliyon, Journal Year: 2023, Volume and Issue: 10(1), P. e22942 - e22942

Published: Nov. 28, 2023

Drought is a hazardous natural disaster that can negatively affect the environment, water resources, agriculture, and economy. Precise drought forecasting trend assessment are essential for management to reduce detrimental effects of drought. However, some existing modeling techniques have limitations hinder precise forecasting, necessitating exploration suitable approaches. This study examines two models, Long Short-Term Memory (LSTM) hybrid model integrating regularized extreme learning machine Snake algorithm, forecast hydrological droughts one six months in advance. Using Multivariate Standardized Streamflow Index (MSSI) computed from 58 years streamflow data drier Malaysian stations, models were compared classical such as gradient boosting regression K-nearest validation purposes. The RELM-SO outperformed other month ahead at station S1, with lower root mean square error (RMSE = 0.1453), absolute (MAE 0.1164), higher Nash-Sutcliffe efficiency index (NSE 0.9012) Willmott (WI 0.9966). Similarly, S2, had 0.1211 MAE 0.0909), 0.8941 WI 0.9960), indicating improved accuracy comparable models. Due significant autocorrelation data, traditional statistical metrics may be inadequate selecting optimal model. Therefore, this introduced novel parameter evaluate model's effectiveness accurately capturing turning points data. Accordingly, significantly 19.32 % 21.52 when LSTM. Besides, reliability analysis showed was most accurate providing long-term forecasts. Additionally, innovative analysis, an effective method, used analyze trends. revealed October, November, December experienced occurrences than months. research advances assessment, valuable insights decision-making drought-prone regions.

Language: Английский

Citations

10
Decompose-deep-recompose models and genetic algorithm based optimal ensemble method (GAE) to enhance the air temperature forecasting of world’s major urban cities DOI
Vipin Kumar

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(2)

Published: Feb. 1, 2025

Language: Английский

Citations

0