Multispectral remote sensing inversion for city landscape water eutrophication based on Genetic Algorithm-Support Vector Machine

Abstract

Eutrophication has become the primary water quality issue for many urban landscape waters in the world. It is a focus in this paper which analyzes Enhanced Thematic Mapper images and quality observation data for 12 consecutive years in 20 parts of the urban landscape water in Xi'an City, China. A water quality model for urban landscape water based on Support Vector Machine (SVM) was established. Based on in situ monitoring data, the model is compared with water quality retrieving methods of multiple regression and back propagation neural network. Results show that the Genetic Algorithm-SVM (GA-SVM) method has better prediction accuracy than the inversion results of the neural network and the traditional statistical regression method. In short, GA-SVM provides a new method for remote sensing monitoring of urban water eutrophication and has more accurate predictions in inversion results [such as chlorophyll a (Chl-a)] in the Xi'an area. Additionally, remote sensing results highly agreed with in situ monitoring data, indicating that the technology is effective and less costly than in situ monitoring. The technology also can be used to evaluate large lake eutrophication.

Toward Urban Water Security: Broadening the Use of Machine Learning Methods for Mitigating Urban Water Hazards

“…SVRs showed potential in solving problems with small sample size, non-linearity, or high dimension (Vapnik, 1995). Huo et al (2014) stated that the lakes near urban areas or inside urban areas are becoming eutrophied or even hypereutrophied due to excessive urbanization and a fast growing economy. The authors used genetic algorithms combined with support vector machines (GA-SVM) to build an inversion model for eutrophic indicators such as Chl-a from Landsat ETM imagery.…”

Urban Water Quality Assessment Based on Remote Sensing Reflectance Optical Classification

“…The black-odorous phenomenon reduces the clarity of water, resulting in a decrease in SD. Thus, SD is often lower in black-odorous water, which makes the R rs of the water surface lower than that of ordinary water [20,66]. Studies have found a distinctive difference between the R rs values of urban black-odorous water and ordinary water [26].…”

“…Owing to these advantages, remote sensing has been widely applied to urban water quality assessment. Water quality parameters such as chlorophyll-a (Chla) [17,18], total suspended matter [19], colored dissolved organic matter (CDOM) [19], and transparency (SD) [18] have been retrieved from the remote sensing images of water areas significantly affected by urban activities, and the mapping results have been used in dynamic monitoring and the evaluation of water quality [15,20]. It is worth noting that only the water quality parameters with optical signals can be precisely retrieved by remote sensing.…”

Impact of Future Climate Change (2020–2059) on the Hydrological Regime in the Heihe River Basin in Shaanxi Province, China

“…From the viewpoint of specific greenhouse gas emissions, the emissions of A1B scenarios are in the middle of the intensity range, and this is the scenario that was selected for use in this study. The parameters corresponding to the A1B emission scenario were then applied to an ensemble of GCM models using data from the Heihe River basin (Huo et al, 2014). The effects of climate change on streamflow in the Heihe River basin were analyzed using the climate scenario generated from the A1B emission scenario and by evaluating GCM performance in the study area.…”