Priorization of River Restoration by Coupling Soil and Water Assessment Tool (SWAT) and Support Vector Machine (SVM) Models in the Taizi River Basin, Northern China

Abstract

Identifying priority zones for river restoration is important for biodiversity conservation and catchment management. However, limited data due to the difficulty of field collection has led to research to better understand the ecological status within a catchment and develop a targeted planning strategy for river restoration. To address this need, coupling hydrological and machine learning models were constructed to identify priority zones for river restoration based on a dataset of aquatic organisms (i.e., algae, macroinvertebrates, and fish) and physicochemical indicators that were collected from 130 sites in September 2014 in the Taizi River, northern China. A process-based model soil and water assessment tool (SWAT) was developed to model the temporal-spatial variations in environmental indicators. A support vector machine (SVM) model was applied to explore the relationships between aquatic organisms and environmental indicators. Biological indices among different hydrological periods were simulated by coupling SWAT and SVM models. Results indicated that aquatic biological indices and physicochemical indicators exhibited apparent temporal and spatial patterns, and those patterns were more evident in the upper reaches compared to the lower reaches. The ecological status of the Taizi River was better in the flood season than that in the dry season. Priority zones were identified for different hydrological seasons by setting the target values for ecological restoration based on biota organisms, and the results suggest that hydrological conditions significantly influenced restoration prioritization over other environmental parameters. Our approach could be applied in other seasonal river ecosystems to provide important preferences for river restoration.

A Hybrid PSO–SVM Model Based on Safety Risk Prediction for the Design Process in Metro Station Construction

“…If there are construction safety risks in the preliminary design documents and these are not effectively dealt with, these defects will lead to an unsafe state of objects (including the environment) and the unsafe behavior of field operators in the construction process [51]; (2) some traditional risk-prediction methods, such as the neural network method, fuzzy comprehensive evaluation method, Bayesian network, and so on, have some shortcomings, such as low accuracy and low prediction efficiency [52]. In recent years, the support vector machine (SVM) model based on particle swarm optimization (PSO-SVM) has been widely used in many fields, which can overcome these problems [53,54]. For example, Zhou et al [55] built the prediction model of PSO-SVM to predict the landslide displacement, and demonstrated that the proposed PSO-SVM model can better represent the response relationship between the factors and the periodic displacement.…”

Prediction of Aquatic Ecosystem Health Indices through Machine Learning Models Using the WGAN-Based Data Augmentation Method

“…Each raw dataset was composed of physicochemical factors (i.e., flow, dissolved oxygen (DO), biochemical oxygen demand in five days (BOD 5 ), total nitrogen (TN), total phosphorus (TP), suspended solids (SS), and water temperature (WT)) and AEH grade data (A to E), which are target data in ML training process. The physicochemical factors were selected as factors that can have a relatively significant effect on the health of aquatic ecosystems by referring to previous research results [30,31]. However, in Korea, the AEH monitoring data, collected twice a year during Spring and Autumn, are insufficient compared with water quality data in terms of the number of datasets and timing of data collection.…”

Application of Fuzzy Best Worse Multi Criteria Decision Making Method for Flood Prioritization

“…Based on sad ekbatan climatology data, the average annual rainfall and temperature is 343.11 mm and +10.75 0 C. From viewpoint of geology, the case study has located in Sanandaj-Sirjan metamorphic zone which has been categorized with sedimentary rock units, including Sl ،Mb ،Schg ،Schan ،Schst ،hc ،Schsp ،K1s,c ،Qt. Rangeland is one the most important covers in the case study, but in two last decade due to economic and social problems, this cover has been transferred to farming areas, leading to an increase of 30% in rate of runoff volume (Farokhzadeh et al, 2015).…”

“…In scale of catchment, given the inherent complexity of formulating flood risk management strategies and its high uncertainty due to some reasons such as large input data and long processing time, it is necessity to select sub-watershedsas a small-scale hydrological unit to prioritize them based on their flood potential (Aher et al, 2014;Anees et al, 2019;Shivhare et al, 2018). In this context, there are variety of approaches available to analysis and prioritize sub-watersheds using Multi Criteria Decision Analysis (MCDA) (Akay and Koçyiğit, 2020;Chitsaz and Banihabib, 2015;Ghaleno et al, 2020;Sepehri et al, 2019c), Soil and Water Assessment Tool (SWAT) (Mishra et al, 2007;Talebi et al, 2019a), artificial neural network (ANN) (Dehghanian et al, 2020), Storm Water Management Model (SWMM) (Babaei et al, 2018), support vector machine (SVM) (Fan et al, 2018;Tehrany et al, 2014) and The Hydrologic Modeling System (HEC-HMS) (Malekinezhad et al, 2017;Talebi et al, 2019b). Among aforementioned methods, MCDA has been taking into account due to its capability to handle nonlinear and complex problems and its usability to prioritize ungauged watershed.…”