Suppr超能文献

利用元胞自动机-人工神经网络预测和监测恰尔肯德邦儒马流域土地利用/土地覆被变化。

Prediction and monitoring of LULC shift using cellular automata-artificial neural network in Jumar watershed of Ranchi District, Jharkhand.

机构信息

Department of Civil and Environmental Engineering, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India, 835215.

出版信息

Environ Monit Assess. 2022 Nov 21;195(1):130. doi: 10.1007/s10661-022-10623-6.

Abstract

Jumar watershed of Ranchi district is agrarian in nature. The unplanned and exponentially growing urban sprawl has become one of the probable threats in achieving sustainable development goals (SDG-15). The purpose of this research study is to monitor the urban sprawl in Jumar watershed within three decades i.e. from the year 1990 to 2021. Land use land cover (LULC) change has been monitored using satellite data from LANDSAT (4, 5 and 8). Various indices are calculated like normalised difference vegetation index (NDVI), normalised difference built-up index (NDBI), normalised difference water index (NDWI) and built-up index (BUI) to monitor LULC change in the area. For prediction of urban sprawl, cellular automata and artificial neural network (CA-ANN) with GIS application technique is used. The model is validated by using Kappa coefficient. The prediction results showed increase in built-up area by 8.23 sq. km in the next decade. The built-up and barren land together increase up to 42.85 sq. km by 2030 and 34.61 sq. km in 2021. The NDVI for 3-decade period showed significant decrease in the healthy vegetation and increase in sparse vegetation. The NDBI showed a slight increase in urban area but massive increase in uncultivated and barren land. NDWI showed a decrease in area of the surface water. The LULC studies showed a major shift from healthy vegetation to agriculture and then to barren land. To assess the impact of urbanisation on water quality, water samples are taken seasonally from J1to J11 sampling locations and are analysed as per APHA procedure. The sites are classified as urban, semi urban and rural area as per their location. The water quality index (WQI) varied between 42.14 to 61.42 during pre-monsoon, 62.20 to 68.7995 during monsoon and 43.48 to 60.12 during post-monsoon. The quality of water is found poor in all seasons at all sampling sites. The water is found highly turbid and alkaline throughout the year. Overall, it can be concluded that the water needs to be pre-treated for drinking purposes throughout the year.

摘要

兰契县朱马流域以农业为主。无计划、呈指数级增长的城市扩张已成为实现可持续发展目标(SDG-15)的一个潜在威胁。本研究旨在监测朱马流域三十年来(即 1990 年至 2021 年)的城市扩张情况。利用来自 LANDSAT(4、5 和 8)的卫星数据监测土地利用/土地覆盖(LULC)变化。计算了归一化差异植被指数(NDVI)、归一化差异建筑指数(NDBI)、归一化差异水体指数(NDWI)和建筑指数(BUI)等各种指数,以监测该地区的 LULC 变化。为了预测城市扩张,使用了带有 GIS 应用技术的元胞自动机和人工神经网络(CA-ANN)。该模型通过使用 Kappa 系数进行验证。预测结果显示,未来十年,建成区面积将增加 8.23 平方公里。到 2030 年,建成区和荒地总面积将增加到 42.85 平方公里,到 2021 年将增加 34.61 平方公里。在 30 年期间,NDVI 显示健康植被的显著减少和稀疏植被的增加。NDBI 显示城市地区略有增加,但未开垦和荒地大量增加。NDWI 显示地表水面积减少。土地利用/土地覆盖研究表明,从健康植被向农业再向荒地的主要转变。为了评估城市化对水质的影响,按 APHA 程序从 J1 到 J11 采样点季节性采集水样并进行分析。根据位置将站点分类为城市、半城市和农村地区。在旱季,水质指数(WQI)在 42.14 到 61.42 之间变化,在雨季在 62.20 到 68.7995 之间变化,在雨季在 43.48 到 60.12 之间变化。在所有季节和所有采样点,水质都很差。全年水质浑浊度高,碱性强。总的来说,可以得出结论,全年需要对水进行预处理才能饮用。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验