Effect of uncontrolled industrialization on environmental parameter: A case study of Mongla EPZ using machine learning approach

IF 3.8 Q2 ENVIRONMENTAL SCIENCES Remote Sensing Applications-Society and Environment Pub Date : 2024-07-22 DOI:10.1016/j.rsase.2024.101307
Faishal Ahmed , Md Shihab Uddin , Ovi Ranjan Saha
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Abstract

Unplanned and uncontrolled industrialization leads to environmental pollution, which ends up impacting human life and destroying the economy. Especially in the era of global warming, coastal regions worldwide are the most vulnerable and hold significant ecological importance for human habitation. In 1998, the establishment of the Mongla Export Processing Zone (MEPZ) in the coastal town of Mongla Thana, which is already famous for its seaport, led the area to the challenges of salinity intrusion and the shrinking of agricultural land and its fertility. Unplanned industrialization in the area causes vegetation loss, severe droughts, and other environmental challenges, threatening local biodiversity and agricultural sustainability. In this paper, the effects of unplanned industrialization inside the Mongla EPZ on the area land surface temperature (LST), normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and urban heat island (UHI) spanning from 2007 to 2023 have been investigated. Along with that, a machine-learning-based artificial neural network (ANN) model was employed to forecast the situation in 2027 and 2031. Our industrial settlement analysis reveals that a substantial rise in industrial building was seen in 2015 in the EPZ area, whereas the EPZ area was almost settlement-free before 2011. With this increase in 2015, above 2% of the total municipal area faced drought, which will become over 30% by 2023. The NDVI values are decreasing year-wise, which reveals that the area is becoming less vegetation-rich. Also, the increasing industrial activities in the EPZ led to an LST increment. Our CA-ANN algorithm-based future prediction shows that about 30% of the whole municipality will face LST 27 °C by 2031. Along with that, the area's UHI value, over 2 °C higher than the rural surrounding area, will reach 6.5% by 2031. Our findings indicate that the municipal area will face a devastating future, including vegetation loss, a high probability of severe drought, and ultimately, environmental degradation. This study will help raising awareness and decision-making process to mitigate the environmental risks and supporting sustainable development.

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不受控制的工业化对环境参数的影响:使用机器学习方法的勐拉出口加工区案例研究
无计划、无节制的工业化导致环境污染,最终影响人类生活,破坏经济。特别是在全球变暖的时代,全球沿海地区是最脆弱的地区,对人类居住具有重要的生态意义。1998 年,勐拉出口加工区(Mongla Export Processing Zone,MEPZ)在以海港闻名的沿海城镇勐拉塔纳建立,使该地区面临盐分入侵、农田萎缩及其肥力下降的挑战。该地区无计划的工业化导致植被减少、严重干旱和其他环境挑战,威胁着当地的生物多样性和农业可持续性。本文研究了小勐拉出口加工区内无规划的工业化对该地区地表温度(LST)、归一化差异植被指数(NDVI)、归一化差异水指数(NDWI)和城市热岛(UHI)的影响,时间跨度为 2007 年至 2023 年。与此同时,还采用了基于机器学习的人工神经网络(ANN)模型来预测 2027 年和 2031 年的情况。我们的工业沉降分析表明,出口加工区地区的工业建筑在 2015 年出现了大幅增长,而在 2011 年之前,出口加工区地区几乎没有沉降。随着 2015 年工业建筑的增加,超过 2% 的城市总面积面临干旱,到 2023 年这一比例将超过 30%。归一化差异植被指数(NDVI)值逐年下降,表明该地区的植被越来越少。此外,出口加工区内不断增加的工业活动也导致了 LST 的增加。基于 CA-ANN 算法的未来预测显示,到 2031 年,整个城市约有 30% 的地区将面临 LST 27 °C。同时,到 2031 年,该地区的超高温指数值将达到 6.5%,比周边农村地区高出 2 ℃ 以上。我们的研究结果表明,该市地区将面临毁灭性的未来,包括植被丧失、极有可能发生严重干旱,并最终导致环境退化。这项研究将有助于提高认识和决策过程,以减轻环境风险,支持可持续发展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
8.00
自引率
8.50%
发文量
204
审稿时长
65 days
期刊介绍: The journal ''Remote Sensing Applications: Society and Environment'' (RSASE) focuses on remote sensing studies that address specific topics with an emphasis on environmental and societal issues - regional / local studies with global significance. Subjects are encouraged to have an interdisciplinary approach and include, but are not limited by: " -Global and climate change studies addressing the impact of increasing concentrations of greenhouse gases, CO2 emission, carbon balance and carbon mitigation, energy system on social and environmental systems -Ecological and environmental issues including biodiversity, ecosystem dynamics, land degradation, atmospheric and water pollution, urban footprint, ecosystem management and natural hazards (e.g. earthquakes, typhoons, floods, landslides) -Natural resource studies including land-use in general, biomass estimation, forests, agricultural land, plantation, soils, coral reefs, wetland and water resources -Agriculture, food production systems and food security outcomes -Socio-economic issues including urban systems, urban growth, public health, epidemics, land-use transition and land use conflicts -Oceanography and coastal zone studies, including sea level rise projections, coastlines changes and the ocean-land interface -Regional challenges for remote sensing application techniques, monitoring and analysis, such as cloud screening and atmospheric correction for tropical regions -Interdisciplinary studies combining remote sensing, household survey data, field measurements and models to address environmental, societal and sustainability issues -Quantitative and qualitative analysis that documents the impact of using remote sensing studies in social, political, environmental or economic systems
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