Machine Learning Approaches for Streamflow Modeling in the Godavari Basin with CMIP6 Dataset

Saravanan, Subbarayan; Reddy, Nagireddy Masthan; Pham, Quoc Bao; Alodah, Abdullah; Abdo, Hazem Ghassan; Almohamad, Hussein; Dughairi, Ahmed Abdullah Al

doi:10.3390/su151612295

Cited by 4 publications

(2 citation statements)

References 84 publications

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“…They reported that RF model outperformed the other models in the prediction model. Saravanan et al (2023) modelled one‐day‐ahead streamflow in the Godavari basin of India using linear regression (LR), SVR, RF, multilayer perceptron (MLP) and M5‐pruned model (M5P). Both RF and M5P showed promising results on supplied CMIP6 datasets.…”

Section: Discussionmentioning

confidence: 99%

Machine learning‐based streamflow forecasting using CMIP6 scenarios: Assessing performance and improving hydrological projections and climate change

Kartal

2024

Hydrological Processes

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Water is essential for humans as well as for all living organisms to sustain their lives. Therefore, any climate‐driven change in available resources has significant impacts on the environment and life. Global climate models (GCMs) are one of the most practical methods to evaluate climate change. Based on this, this research evaluated the capability of GCMs from the Coupled Model Intercomparison Project 6 (CMIP6) to reproduce the historical flow of climate prediction centre data for the Konya Closed basin and to project the climate of the basin using the selected GCMs. Global climate models based on the CMIP6 under the scenario of common socioeconomic pathways (SSP245 and SSP 585) were used to analyse the climate change effect on streamflow of the study area by Bias Correction of GCM Models using Long Short‐Term Memory (LSTM), Bidirectional LSTM (BiLSTM), AdaBoost, Gradient Boosting, Regression Tree, and Random Forest methods. The coefficient of determination (R2), mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE) were used to assess the performance of the methods. Findings show that the Random Forest Model consistently outperformed other models in both the testing and training phases. A significant downward in the volume of water flowing through the region's rivers and streams in the next decades. It is critical to enhance climate‐resilient water infrastructure financing, establish an early warning system for drought, introduce best management practices, implement integrated water resource management, public awareness, and support water research to alleviate the negative consequences of drought and increase resilience against the effects of climate change on Turkey's water resources.

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Section: Discussionmentioning

confidence: 99%

Machine learning‐based streamflow forecasting using CMIP6 scenarios: Assessing performance and improving hydrological projections and climate change

Kartal

2024

Hydrological Processes

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“…Since the decision trees in the RF are generated independently from random samples, there is a low association between the trees. Afterward, voting takes place using the classifications generated by each tree, and the class with the most votes is used to predict the presented sample [27]. 4.…”

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confidence: 99%

Diagnostics of Early Faults in Wind Generator Bearings Using Hjorth Parameters

Santos,

Souza,

Barbara

et al. 2023

Sustainability

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Machine learning techniques are a widespread approach to monitoring and diagnosing faults in electrical machines. These techniques extract information from collected signals and classify the health conditions of internal components. Among all internal components, bearings present the highest failure rate. Classifiers commonly employ vibration data acquired from electrical machines, which can indicate different levels of bearing failure severity. Given the circumstances, this work proposes a methodology for detecting early bearing failures in wind turbines, applying classifiers that rely on Hjorth parameters. The Hjorth parameters were applied to analyze vibration signals collected from experiments to distinguish states of normal functioning and states of malfunction, hence enabling the classification of distinct conditions. After the labeling stage using Hjorth parameters, classifiers were employed to provide an automatic early fault identification model, with the decision tree, random forest, support vector machine, and k-nearest neighbors methods presenting accuracy levels of over 95%. Notably, the accuracy of the classifiers was maintained even after undergoing a dimensionality reduction process. Therefore, it can be stated that Hjorth parameters provide a feasible alternative for identifying early faults in wind generators through time-series analysis.

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Integrating conceptual and machine learning models to enhance daily-Scale streamflow simulation and assessing climate change impact in the watersheds of the Godavari basin, India

Reddy,

Saravanan,

Paneerselvam

2024

Environmental Research

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Machine Learning Approaches for Streamflow Modeling in the Godavari Basin with CMIP6 Dataset

Cited by 4 publications

References 84 publications

Machine learning‐based streamflow forecasting using CMIP6 scenarios: Assessing performance and improving hydrological projections and climate change

Machine learning‐based streamflow forecasting using CMIP6 scenarios: Assessing performance and improving hydrological projections and climate change

Diagnostics of Early Faults in Wind Generator Bearings Using Hjorth Parameters

Integrating conceptual and machine learning models to enhance daily-Scale streamflow simulation and assessing climate change impact in the watersheds of the Godavari basin, India

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