Influence of Land Surface Temperature and Rainfall on Surface Water Change: An Innovative Machine Learning Approach

Autor: Sardar M N Islam, Eeshita Gupta, Rachna Jain, Ish Takkar, Aarushi Dhingra, Vanita Jain
Rok vydání: 2023
Předmět:
Zdroj: Water Resources Management.
ISSN: 1573-1650
0920-4741
DOI: 10.1007/s11269-023-03476-2
Popis: Surface water is the world’s largest store of consumable water. It plays an important role in sustaining ecosystems and enabling human adaptation to various climate changes. Although surface freshwater is necessary for life, there is a startling lack of understanding in the current literature of the spatial and temporal dynamics of surface freshwater outflow and storage variations across a large country: India. Current studies have several limitations, such as the use of relatively short data series and inappropriate machine learning methods. This study develops an innovative machine learning approach. It uses a long data series to generate a correct understanding of the spatial and temporal dynamics of surface freshwater outflow and storage variations in a large geographical area. Using the JRC dataset, the authors mapped the change in intensity of surface water, performed an analysis of the thematic maps (Indian subcontinent) generated using ArcMap 10.8 from a period of June 2000 to June 2020. The research presented in this paper devised Google Earth Engine in Python API and remote sensing techniques to detect changes in the trends of surface water levels and quantify changing map trends. ArcMap is used for the viewing and editing of Raster images and to perform raster calculations. Values of the attributes are averaged over 13 rivers over the period of 20 years to find the correlation between the decrease in Surface water levels with the rainfall intensity and land surface temperature changes. The authors use the correlation between the parameters to obtain an average R-squared adjusted value of 0.402 with the dependence of surface water change to the independent variables of change in land surface temperature and rainfall intensity. The results of the study provide an improved understanding of the effects of the change in the land surface temperature and rainfall levels or the causes of the decline and changes in the surface waters of India. The innovative machine learning and data analysis approach developed in this study can be applied effectively to other regions of the world.
Databáze: OpenAIRE
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