Monthly Streamflow Forecasting Using Machine Learning

Tosunoğlu, Fatih; Hanay, Sinan; Çintaş, Emre; Ozyer, Baris

doi:10.18185/erzifbed.780477

Cited by 5 publications

(2 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By analyzing water and atmospheric data, including groundwater and surface runoff, researchers can obtain meaningful results to enhance water resource management [39,40]. Time series analysis, which involves examining numerical values over a specific timeframe, plays a pivotal role in understanding water movements and estimating future water potential [41,42].…”

Section: Related Workmentioning

confidence: 99%

Hybrid Modeling for Stream Flow Estimation: Integrating Machine Learning and Federated Learning

Akbulut,

Cifci,

Aslan

2023

Applied Sciences

View full text Add to dashboard Cite

In the face of mounting global challenges stemming from population growth and climate fluctuations, the sustainable management of water resources emerges as a paramount concern. This scientific endeavor casts its gaze upon the Upper Euphrates basin, homing in on the Tunceli Munzur water sub-basin and the Sakarya Basin’s Kütahya Porsuk Stream Beşdeğirmen rivers. The investigation unfolds through the intricate analysis of daily average flow data, total daily precipitation, and daily average air temperature values, with the objective of unraveling the complexities of future water potential estimation. Central to our exploration are a series of well-established techniques including linear regression (LR), support vector regression (SVR), decision tree (DT), random forest (RF), and extra trees regression (ETR). We employ these methodologies diligently to decipher patterns woven within the dataset, fostering an informed understanding of water dynamics. To ascend the pinnacle of estimation accuracy, we introduce a groundbreaking hybrid approach, wherein the enigmatic wavelet transform (WT) technique assumes a pivotal role. Through systematic stratification of our dataset into training, validation, and test sets, comprising roughly 65%, 15%, and 20% of the data, respectively, a comprehensive experiment takes shape. Our results unveil the formidable performance of the ETR method, achieving a striking 88% estimation accuracy for the Porsuk Stream Beşdeğirmen, while the RF method garners a commendable 85.2% success rate for the Munzur water Melekbahçe. The apex of innovation unfolds within our hybrid model, a harmonious fusion of methodologies that transcends their individual capacities. This composite entity elevates estimation success rates by a remarkable 20% for the Munzur water Melekbahçe and an appreciable 11% for the Porsuk Stream Beşdeğirmen. This amalgamation culminates in an extraordinary overall success rate of 97.7%. Our findings transcend mere insights, resonating as guiding beacons for navigating the intricate maze of water resource management in an era marked by uncertainties. This study underscores the indispensability of advanced mathematical paradigms and machine learning frontiers, fortifying the bedrock of sustainable water resource management for the generations to come. By harnessing the fusion of federated learning and a constellation of innovative techniques, we endeavor to illuminate the path towards deciphering the complex tapestry of water resource estimation and management, facilitating a resilient and enduring aquatic world.

show abstract

Section: Related Workmentioning

confidence: 99%

Hybrid Modeling for Stream Flow Estimation: Integrating Machine Learning and Federated Learning

Akbulut,

Cifci,

Aslan

2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Based on this research, our results show that algorithms like KNN are the most effective (95.5% accuracy) for evenly distributed and simple datasets such as the Iris dataset. For the Wine Quality dataset, algorithms like Decision Tree give a high accuracy of 100% and K-nearest neighbors give a minimum accuracy which was 82.29% [10].…”

Section: Introductionmentioning

confidence: 99%

Prediction Model based on Iris Dataset Via Some Machine Learning Algorithms

Aziz,

Awrahman

2023

Jour. Kufa Math. Comp.

View full text Add to dashboard Cite

Abstract— Supervised Machine Learning algorithm has an important approach to Classification. We are predicting the deal type of the Iris plant using various algorithms of machine learning. Iris plants are determined by numerous factors such as the size of the length and width of the property. A horticultural skill announces that some of the plants are different in some physical appearances like size, shape, and color. Hence it is difficult to recognize any species. Versicolor, Setosa, and Virginica have three identical subspecies of The Iris flower species. This paper uses machine learning algorithms to recognize all classes of the flower with an accuracy degree of %100 for KNN, %95 for RF, %97 for DT, and %98 for LR. The Iris dataset is frequently available, and it is implemented using Scikit tools. and build the prediction model for Plants. Here, algorithms of machine learning such as Logistic Regression (LR), Decision Tree (DT), K Nearest Neighbor (KNN), and Random Forest (RF) are employed to construct a predictive model.

show abstract

Prediction of missing temperature data using different machine learning methods

Katipoğlu

2021

Arab J Geosci

View full text Add to dashboard Cite

Monthly Streamflow Forecasting Using Machine Learning

Cited by 5 publications

References 22 publications

Hybrid Modeling for Stream Flow Estimation: Integrating Machine Learning and Federated Learning

Hybrid Modeling for Stream Flow Estimation: Integrating Machine Learning and Federated Learning

Prediction Model based on Iris Dataset Via Some Machine Learning Algorithms

Prediction of missing temperature data using different machine learning methods

Contact Info

Product

Resources

About