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Precipitation Modeling of Gwadar Port Baluchistan for Environmental Sustainability using Multilinear Regression Analysis Technique


Article Information

Title: Precipitation Modeling of Gwadar Port Baluchistan for Environmental Sustainability using Multilinear Regression Analysis Technique

Authors: Erum Aamir, Farah Naz, Fasiha Safdar

Journal: NUST Journal of Social Sciences and Humanities (NJSSH)

HEC Recognition History
Category From To
Y 2024-10-01 2025-12-31
Y 2023-07-01 2024-09-30
Y 2022-07-01 2023-06-30
Y 2021-07-01 2022-06-30
Y 2020-07-01 2021-06-30

Publisher: National University of Sciences & Technology, Rawalpindi/Islamabad (NUST)

Country: Pakistan

Year: 2022

Volume: 8

Issue: 2

Language: en

DOI: 10.51732/njssh.v8i2.142

Keywords: TrendPrecipitationSustainable Development Goals (SDGs)Variablesmultiple linear regressionStatistical ModelingMann-Kendal

Categories

Abstract

Precipitation is the main source of fresh water in the water cycle. Climate change, because of global warming and the consequent change in the water cycle, is a global security issue. It would significantly influence water and food security. Disasters such as floods and droughts would lead to an adverse effect on the economy, peace, and geo-political situation around the world. In the present study, the change in precipitation patterns at Gwadar port is quantified in the context of climate change since it is the crown jewel in the China-Pakistan Economic Corridor (CPEC), as well as a vital part of One Belt One Road (OBOR) project. A data set of 40 years (1979-2018) is analyzed utilizing Mann-Kendall (MK) technique for precipitation trend detection, and Multi-Linear Regression Analysis (MLR) to develop a model of the study area. The model presents the potential determinants causing the variability in the precipitation patterns of the selected region. The model developed accounts for 80.47% (51.16%) of precipitation variability. The model successfully passed all estimation tests. This research will help the policymakers in legislation. The research also addresses United Nations Sustainable Development Goals (SDG): SDG# 13 (Climate Action).


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