Artificial Intelligence Based Flood Forecasting for River Hunza at Danyor Station in Pakistan

• Autorzy: Yaseen Muhammad Waseem , Awais Muhammad , Riaz Khuram , Rasheed Muhammad Babar , Waqar Muhammad , Rasheed Sajid

Identyfikatory

DOI

10.2478/heem-2022-0005

• Języki publikacji: EN

Abstrakty

EN

Floods can cause significant problems for humans and can damage the economy. Implementing a reliable flood monitoring warning system in risk areas can help to reduce the negative impacts of these natural disasters. Artificial intelligence algorithms and statistical approaches are employed by researchers to enhance flood forecasting. In this study, a dataset was created using unique features measured by sensors along the Hunza River in Pakistan over the past 31 years. The dataset was used for classification and regression problems. Two types of machine learning algorithms were tested for classification: classical algorithms (Random Forest, RF and Support Vector Classifier, SVC) and deep learning algorithms (Multi-Layer Perceptron, MLP). For the regression problem, the result of MLP and Support Vector Regression (SVR) algorithms were compared based on their mean square, root mean square and mean absolute errors. The results obtained show that the accuracy of the RF classifier is 0.99, while the accuracies of the SVC and MLP methods are 0.98; moreover, in the case of flood prediction, the SVR algorithm outperforms the MLP approach.

Słowa kluczowe

EN

hydrometeorology; random forest; support vector; multilayer perceptron; machine learning; flood forecasting

• Wydawca: Institute of Hydro-Engineering of Polish Academy of Sciences
• Czasopismo: Archives of Hydro-Engineering and Environmental Mechanics
• Rocznik: 2022
• Tom: Vol. 69, nr 1
• Strony: 59--77
• Opis fizyczny: Bibliogr. 35 poz., rys., tab., wykr

Twórcy

autor

Yaseen Muhammad Waseem

• The University of Lahore, 1-km off Defence Road, Lahore, Pakistan, 54000
• The University of Punjab, Quaid-e-Azam campus Canal Bank Road, Lahore, Pakistan, 54000

autor

Awais Muhammad

• The University of Lahore, 1-km off Defence Road, Lahore, Pakistan, 54000

autor

Riaz Khuram

• The University of Lahore, 1-km off Defence Road, Lahore, Pakistan, 54000
• The University of Punjab, Quaid-e-Azam campus Canal Bank Road, Lahore, Pakistan, 54000

autor

Rasheed Muhammad Babar

• The University of Lahore, 1-km off Defence Road, Lahore, Pakistan, 54000

autor

Waqar Muhammad

• The University of Punjab, Quaid-e-Azam campus Canal Bank Road, Lahore, Pakistan, 54000

autor

Rasheed Sajid

• The University of Punjab, Quaid-e-Azam campus Canal Bank Road, Lahore, Pakistan, 54000

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).