Effects of fragmentation characters on wetland hydrological changes in Rarh region, West Bengal, India

• Autorzy: Khatun Rumki , Das Somen

Identyfikatory

DOI

10.1007/s11600-024-01350-2

• Języki publikacji: EN

Abstrakty

EN

The current study aims to quantify the relationship between hydrological richness in river and riparian wetland habitats and fragmentation analysis. Eight relevant parameters, such as the frequency of water presence, hydro-period, and proximity to the river, have been incorporated into four models—two statistical models (Shannon entropy and Logistic Regression) and two machine learning models (artificial neural network and random forest)—in order to investigate wetland hydrological richness. The models are evaluated using statistical techniques such as ROC curves, and field-based validation is also performed. The information about the best-performing models (random forest for machine learning and logistic regression for statistical models) is valuable for understanding the predictive capabilities of the models applied. RF model identified 168.43 km², 110.91 km², 70.13 km², and 39.15 km² areas as having very rich and rich water richness zones in 1990, 2000, 2010, and 2020, respectively. The percentage of poor and very poor areas has rapidly increased from 29.7% in 1990 to 55.35% in 2020. Additionally, the relationship between wetland fragmentation and hydrological richness is assessed. Wetland fragmentation and shrinking core areas due to anthropogenic intrusion significantly impact the hydrological richness of wetlands. This study will provide important insights into the changing state of wetlands over time, especially concerning the impact of anthropogenic activities on hydrological richness. The findings of this study would have significant implications for wetland management and precautions on manmade hydrological modification.

Słowa kluczowe

EN

wetland hydrological richness; statistical models; machine learning models; fragmentation; hydrological richness; field-based hydrological richness

PL

bogactwo hydrologiczne terenów podmokłych; modele statystyczne; modele uczenia maszynowego; fragmentacja; bogactwo hydrologiczne

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2025
• Tom: Vol. 73, no. 1
• Strony: 661--685
• Opis fizyczny: Bibliogr. 130 poz.

Twórcy

autor

Khatun Rumki

• Department of Geography, Kazi Nazrul University, Asansol, West Bengal 713340, India

autor

Das Somen

• Department of Geography, Kazi Nazrul University, Asansol, West Bengal 713340, India

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).