scholarly journals Urban surface water system in coastal areas: A comparative study between Almere and Tianjin Eco-city

2013 ◽  
Vol 03 (06) ◽  
pp. 407-416 ◽  
Author(s):  
Tao Zou ◽  
Zhengnan Zhou
2020 ◽  
Vol 27 (14) ◽  
pp. 16718-16730 ◽  
Author(s):  
Muhammad Irfan ◽  
Abdul Qadir ◽  
Mehvish Mumtaz ◽  
Sajid Rashid Ahmad

2009 ◽  
Vol 8 (4) ◽  
pp. 859-863 ◽  
Author(s):  
Daniela Simina Stefan ◽  
Cristina Costache ◽  
Viorica Ruxandu ◽  
Monica Balas ◽  
Mircea Stefan

1993 ◽  
Vol 27 (5-6) ◽  
pp. 61-67 ◽  
Author(s):  
E. Jacobs ◽  
J. W. van Sluis

The surface water system of Amsterdam is very complicated. Of two characteristic types of water systems the influences on water and sediment quality are investigated. The importance of the sewer output to the total loads is different for both water systems. In a polder the load from the sewers is much more important than in the canal basin. Measures to reduce the emission from the sewers are much more effective in a polder. The effect of these measures on sediment quality is more than the effect on water quality. Some differences between a combined sewer system and a separate sewer system can be found in sediment quality.


2018 ◽  
Vol 10 (11) ◽  
pp. 1704 ◽  
Author(s):  
Wei Wu ◽  
Qiangzi Li ◽  
Yuan Zhang ◽  
Xin Du ◽  
Hongyan Wang

Urban surface water mapping is essential for studying its role in urban ecosystems and local microclimates. However, fast and accurate extraction of urban water remains a great challenge due to the limitations of conventional water indexes and the presence of shadows. Therefore, we proposed a new urban water mapping technique named the Two-Step Urban Water Index (TSUWI), which combines an Urban Water Index (UWI) and an Urban Shadow Index (USI). These two subindexes were established based on spectral analysis and linear Support Vector Machine (SVM) training of pure pixels from eight training sites across China. The performance of the TSUWI was compared with that of the Normalized Difference Water Index (NDWI), High Resolution Water Index (HRWI) and SVM classifier at twelve test sites. The results showed that this method consistently achieved good performance with a mean Kappa Coefficient (KC) of 0.97 and a mean total error (TE) of 2.28%. Overall, classification accuracy of TSUWI was significantly higher than that of the NDWI, HRWI, and SVM (p-value < 0.01). At most test sites, TSUWI improved accuracy by decreasing the TEs by more than 45% compared to NDWI and HRWI, and by more than 15% compared to SVM. In addition, both UWI and USI were shown to have more stable optimal thresholds that are close to 0 and maintain better performance near their optimum thresholds. Therefore, TSUWI can be used as a simple yet robust method for urban water mapping with high accuracy.


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