Snow water equivalent prediction in a mountainous area using hybrid bagging machine learning approaches

• Autorzy: Khosravi Khabat , Golkarian Ali , Omidvar Ebrahim , Hatamiafkoueieh Javad , Shirali Masoud

Identyfikatory

DOI

10.1007/s11600-022-00934-0

• Języki publikacji: EN

Abstrakty

EN

Snow Water Equivalent (SWE) is one of the most critical variables in mountainous watersheds and needs to be considered in water resources management plans. As direct measurement of SWE is difficult and empirical equations are highly uncertain, the present study aimed to obtain accurate predictions of SWE using machine learning methods. Five standalone algorithms of tree-based [M5P and random tree (RT)], rule-based [M5Rules (M5R)] and lazy-based learner (IBK and Kstar) and five novel hybrid bagging-based algorithms (BA) with standalone models (i.e., BA-M5P, BA-RT, BA-IBK, BA-Kstar and BA-M5R) were developed. A total of 2550 snow measurements were collected from 62 snow and rain-gauge stations located in 13 mountainous provinces in Iran. Data including ice beneath the snow (IBS), fresh snow depth (FSD), length of snow sample (LSS), snow density (SDN), snow depth (SD) and time of falling (TS) were measured. Based on the Pearson correlation between inputs (IBS, FSD, LSS, SDN, SD and TS) and output (SWE), six different input combinations were constructed. The dataset was separated into two groups (70% and 30% of the data) by a cross-validation technique for model construction (training dataset) and model evaluation (testing dataset), respectively. Different visual and quantitative metrics (e.g., Nash–Sutclife efficiency (NSE)) were used for evaluating model accuracy. It was found that SD had the highest correlation with SWE in Iran (r=0.73). In general, the bootstrap aggregation (i.e., bagging) hybrid machine learning methods (BA-M5P, BA-RT, BA-IBK, BA-Kstar and BA-M5R) increased prediction accuracy when compared to each standalone method. While BA-M5R had the highest prediction accuracy (NSE=0.83) (considering all six input variables), BA-IBK could predict SWE with high accuracy (NSE=0.71) using only two input variables (SD and LSS). Our findings demonstrate that SWE can be accurately predicted through a variety of machine learning methods using easily measurable variables and may be useful for applications in other mountainous regions across the globe.

Słowa kluczowe

EN

snow depth; machine learning; bagging; Iran; snow water equivalent

PL

grubość pokrywy śnieżnej; nauczanie maszynowe; parcianka; Iran

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2023
• Tom: Vol. 71, no. 2
• Strony: 1015--1031
• Opis fizyczny: Bibliogr. 80 poz.

Twórcy

autor

Khosravi Khabat

• Department of Earth and Environment, Florida International University, Miami, USA
• Department of Watershed Management, Ferdowsi University of Mashhad, Mashhad, Iran

autor

Golkarian Ali

• Department of Watershed Management, Ferdowsi University of Mashhad, Mashhad, Iran

autor

Omidvar Ebrahim

• Department of Range and Watershed Management, Faculty of Natural Resources and Earth Sciences, University of Kashan, Kashan, Iran

autor

Hatamiafkoueieh Javad

• [e-mail]
• Department of Mechanics and Control Processes, Academy of Engineering, Peoples’ Friendship University of Russia (RUDN University), Miklukho-Maklaya Str. 6, Moscow, Russian Federation 117198

• https://orcid.org/0000-0003-1237-4467

autor

Shirali Masoud

• Department of GIS and RS, Yazd Branch, Islamic Azad University, Hesabi Blv, Safaeie, Yazd, Iran

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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).