A novel algal bloom risk assessment framework by integrating environmental factors based on explainable machine learning

Ecological Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 103098 - 103098

Published: March 1, 2025

Language: Английский

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Investigation of Water Quality in Izmir Bay With Remote Sensing Techniques Using NDCI on Google Earth Engine Platform

Transactions in GIS, Journal Year: 2025, Volume and Issue: 29(1)

Published: Jan. 12, 2025

ABSTRACT In this study, the effects of algal blooms occurring in Izmir Bay summer 2024 on marine ecosystems were investigated using remote sensing techniques Google Earth Engine platform. The normalized difference chlorophyll index (NDCI) was calculated from January to end September and chlorophyll‐a density analyzed. Additionally, an NDCI time series analysis conducted between 2018 at designated points. values, which fluctuated narrowly until 2022, showed a sharp increase 2024. NDCI, vary −0.4 0.2 up 0.8 toward months, indicate that are occurring, concentrated critical areas such as Karşıyaka, Bayraklı, Alsancak Port. These findings revealed connection sudden fish deaths bay during blooms, well deterioration water quality.

Language: Английский

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Hybrid feature-based neural network regression method for load profiles forecasting

Energy Informatics, Journal Year: 2025, Volume and Issue: 8(1)

Published: Feb. 10, 2025

This study addresses the critical need for improved demand forecasting models that can accurately predict energy consumption, particularly in context of varying geographical and climatic conditions. The work introduces a novel model integrates clustering techniques feature engineering into neural network regression, with specific focus on incorporating correlations air temperature. Evaluation model's efficacy utilized benchmark dataset from Tetouan, Morocco, where existing methods yielded RMSE values ranging 6429 to 10,220 [MWh]. In contrast, proposed approach achieved significantly lower 5168, indicating its superiority. Subsequent application forecast Astana, Kazakhstan, as case study, showcased further. Comparative analysis against baseline method revealed notable improvement, exhibiting MAPE 5.19% compared baseline's 17.36%. These findings highlight potential enhance accuracy, across diverse contexts, by leveraging climate-related inputs, methodology also demonstrates broader applications, such flood forecasting, agricultural yield prediction, or water resource management.

Language: Английский

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Comparative analysis of k-nearest neighbors distance metrics for retrieving coastal water quality based on concurrent in situ and satellite observations

Marine Pollution Bulletin, Journal Year: 2025, Volume and Issue: 214, P. 117816 - 117816

Published: March 13, 2025

Language: Английский

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Application of Quantum Neural Network for Solar Irradiance Forecasting: A Case Study Using the Folsom Dataset, California

Published: July 2, 2024

Quantum machine learning applications have become viable with the recent advancements in quantum computing. Merging ML power of computing holds great potential for data-driven decision-making, as well development more powerful models capable handling complex datasets faster processing time. This area offers improving accuracy real-time forecasting renewable energy production. However, literature on this topic is sparse. Addressing knowledge gap, study aims to design, implement, and evaluate performance a neural network forecast model solar irradiance up 3-hours ahead. The proposed was compared Support Vector Regression, Group Method Data Handling, Extreme Gradient Boost classical models. Using best configuration found, framework could provide competitive results when its competitors, considering intervals 5- 120-minutes ahead, where it fourth best-performing paradigm. For ahead predictions, QNN able overcome clas-sical counterparts, but XGBoost. fact can be an indication that may identify retrieve relevant spatiotemporal information from input dataset such manner not attainable by current approaches.

Language: Английский

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Modelling phosphorus inputs and dynamics in a large tropical semiarid basin

Environmental Science and Pollution Research, Journal Year: 2025, Volume and Issue: unknown

Published: March 4, 2025

Language: Английский

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