Improvement of Low Impact Development (LID) models coupled with watershed models: Application, performance, and optimization

Department of Urban and Environmental Engineering (Environmental Science and Engineering)Urbanization has influenced the environmental and hydrological cycles in local, global and regional scales. This can cause urban flooding including sewer water overflows and spilling pollutants that pose a risk of human health. Hence, urban water sustainability and planning have drawn public attention. Low Impact Development (LID) practices have been mentioned to improve the environmental, hydrological, and social outcomes. Therefore, our study developed LID modules to optimize and simulate LID facilities. We hope that developed modeling tools will establish effective LID strategies for improving urban management and sustainability. In an urban watershed, the first flush effect (FFE) is one of the greatest issues facing the urban environment. It indicates a greater discharge of the load of the early part in the rainfall event. LIDs have been suggested as strategies for managing urban stormwater and pollution. However, these practices require various monitoring and modeling approaches to identify characteristics and purpose to LID. These could produce proper guidelines to optimize LID management. Our study conducted stormwater monitoring and modeling to select the optimal LID sizes in the urban area. The optimal LID size was determined for reducing mass first flush (MFF), which is an index to quantify FFE. The optimal LID sizes showed range of 1.2 mm to 3.0 mm in terms of runoff depths. To increase the effectiveness of LID, the optimization process is important. Hence, this study developed optimizing tool for bioretention considering hydrological watershed properties. The model could calculate soil infiltration under hydrological conditions and hydraulic structure. Using this model, we could generate an optimized plan for bioretention based on Flow Duration Curve (FDC). The optimized result demonstrated that the soil texture, location and size are important factors affecting bioretention. Previous LID models have limitations to calculate the detailed water movement. Therefore, we developed a new LID model by coupling the stormwater management model and HYDRUS-1D (SWMM-H) models to enhance simulations of hydrological processes. Rainfall-runoff monitoring was conducted for a pilot-scale green roof and urban sub-catchment. The developed LID module was evaluated with the observed data, while the hydrologic performance of the developed model was assessed by considering scenarios of green roof sizes and soil types. Soil moisture in the green roof was simulated more accurately using SWMM-H than with SWMM. The scenario analysis demonstrated that SWMM-H estimated a more reasonable soil type (sandy loam) for managing runoff than SWMM (sand). The water quality module in LID was simplified in that water quality simulation only considered the dilution effect of rainfall. This study solved this limitation by modifying the LID-water quality module in the stormwater management model (SWMM). We evaluated the modified module by simulating TSS, COD, TN, and TP for LID facilities. The developed module applied the scenarios analysis of LID under climate change conditions. The modified module produced accurate simulation for pollutant, yielding an average ratio of root mean square error (RMSE) to the observation standard deviation ratio (RSR) of 0.52, while the original module presented an unacceptable performance with an RSR value of 1.11.This scenario analysis showed that the hydrological simulation was sensitive to the volume of rainfall while the water quality simulation was sensitive to the temporal distribution of rainfall.clos

Investigating Influence of Hydrological Regime on Organic Matters Characteristic in a Korean Watershed

Source tracking of dissolved organic matter (DOM) is important to manage water quality in rivers. However, it is difficult to find the source of this DOM because various DOMs can be added from the river watershed. Moreover, the DOM composition can be changed due to environmental conditions. This study investigated the change of organic matter characteristics in the Taewha River of Ulsan City, Korea, before and after rainfall. A Soil and Water Assessment Tool (SWAT) was used to simulate water flow from various sources, and dissolved organic matter characterization was conducted in terms of molecular size distribution, hydrophobicity, fluorescence excitation and emission, and molecular composition. From the results, it was found that lateral flow transported hydrophobic and large-molecule organic matter after rainfall. According to the orbitrap mass spectrometer analysis, the major molecular compound of the DOM was lignin. Coupling the SWAT model with organic matter characterization was an effective approach to find sources of DOM in river

An Integrative Remote Sensing Application of Stacked Autoencoder for Atmospheric Correction and Cyanobacteria Estimation Using Hyperspectral Imagery

Hyperspectral image sensing can be used to effectively detect the distribution of harmful cyanobacteria. To accomplish this, physical- and/or model-based simulations have been conducted to perform an atmospheric correction (AC) and an estimation of pigments, including phycocyanin (PC) and chlorophyll-a (Chl-a), in cyanobacteria. However, such simulations were undesirable in certain cases, due to the difficulty of representing dynamically changing aerosol and water vapor in the atmosphere and the optical complexity of inland water. Thus, this study was focused on the development of a deep neural network model for AC and cyanobacteria estimation, without considering the physical formulation. The stacked autoencoder (SAE) network was adopted for the feature extraction and dimensionality reduction of hyperspectral imagery. The artificial neural network (ANN) and support vector regression (SVR) were sequentially applied to achieve AC and estimate cyanobacteria concentrations (i.e., SAE-ANN and SAE-SVR). Further, the ANN and SVR models without SAE were compared with SAE-ANN and SAE-SVR models for the performance evaluations. In terms of AC performance, both SAE-ANN and SAE-SVR displayed reasonable accuracy with the Nash???Sutcliffe efficiency (NSE) > 0.7. For PC and Chl-a estimation, the SAE-ANN model showed the best performance, by yielding NSE values > 0.79 and > 0.77, respectively. SAE, with fine tuning operators, improved the accuracy of the original ANN and SVR estimations, in terms of both AC and cyanobacteria estimation. This is primarily attributed to the high-level feature extraction of SAE, which can represent the spatial features of cyanobacteria. Therefore, this study demonstrated that the deep neural network has a strong potential to realize an integrative remote sensing application

Modeling the Fate and Transport of Malathion in the Pagsanjan-Lumban Basin, Philippines

Exposure to highly toxic pesticides could potentially cause cancer and disrupt the development of vital systems. Monitoring activities were performed to assess the level of contamination; however, these were costly, laborious, and short-term leading to insufficient monitoring data. However, the performance of the existing Soil and Water Assessment Tool (SWAT model) can be restricted by its two-phase partitioning approach, which is inadequate when it comes to simulating pesticides with limited dataset. This study developed a modified SWAT pesticide model to address these challenges. The modified model considered the three-phase partitioning model that classifies the pesticide into three forms: dissolved, particle-bound, and dissolved organic carbon (DOC)-associated pesticide. The addition of DOC-associated pesticide particles increases the scope of the pesticide model by also considering the adherence of pesticides to the organic carbon in the soil. The modified SWAT and original SWAT pesticide model was applied to the Pagsanjan-Lumban (PL) basin, a highly agricultural region. Malathion was chosen as the target pesticide since it is commonly used in the basin. The pesticide models simulated the fate and transport of malathion in the PL basin and showed the temporal pattern of selected subbasins. The sensitivity analyses revealed that application efficiency and settling velocity were the most sensitive parameters for the original and modified SWAT model, respectively. Degradation of particulate-phase malathion were also significant to both models. The rate of determination (R2) and Nash-Sutcliffe efficiency (NSE) values showed that the modified model (R2 = 0.52; NSE = 0.36) gave a slightly better performance compared to the original (R2 = 0.39; NSE = 0.18). Results from this study will be able to aid the government and private agriculture sectors to have an in-depth understanding in managing pesticide usage in agricultural watersheds

Comparative Studies of Different Imputation Methods for Recovering Streamflow Observation

Faulty field sensors cause unreliability in the observed data that needed to calibrate and assess hydrology models. However, it is illogical to ignore abnormal or missing values if there are limited data available. This study addressed this problem by applying data imputation to replace incorrect values and recover missing streamflow information in the dataset of the Samho gauging station at Taehwa River (TR), Korea from 2004 to 2006. Soil and Water Assessment Tool (SWAT) and two machine learning techniques, Artificial Neural Network (ANN) and Self Organizing Map (SOM), were employed to estimate streamflow using reasonable flow datasets of Samho station from 2004 to 2009. The machine learning models were generally better at capturing high flows, while SWAT was better at simulating low flows.open

Gene Expression Profile of the Hypothalamus in DNP-KLH Immunized Mice Following Electroacupuncture Stimulation

Clinical evidence indicates that electroacupuncture (EA) is effective for allergic disorder. Recent animal studies have shown that EA treatment reduces levels of IgE and Th2 cytokines in BALB/c mice immunized with 2,4-dinitrophenylated keyhole limpet protein (DNP-KLH). The hypothalamus, a brain center of the neural-immune system, is known to be activated by EA stimulation. This study was performed to identify and characterize the differentially expressed genes in the hypothalamus of DNP-KLH immunized mice that were stimulated with EA or only restrained. To this aim, we conducted a microarray analysis to evaluate the global gene expression profiles, using the hypothalamic RNA samples taken from three groups of mice: (i) normal control group (no treatments); (ii) IMH group (DNP-KLH immunization + restraint); and (iii) IMEA group (immunization + EA stimulation). The microarray analysis revealed that total 39 genes were altered in their expression levels by EA treatment. Ten genes, including T-cell receptor alpha variable region family 13 subfamily 1 (Tcra-V13.1), heat shock protein 1B (Hspa1b) and 2′–5′ oligoadenylate synthetase 1F (Oas1f), were up-regulated in the IMEA group when compared with the IMH group. In contrast, 29 genes, including decay accelerating factor 2 (Daf2), NAD(P)H dehydrogenase, quinone 1 (Nqo1) and programmed cell death 1 ligand 2 (Pdcd1lg2) were down-regulated in the IMEA group as compared with the IMH group. These results suggest that EA treatment can modulate immune response in DNP-KLH immunized mice by regulating expression levels of genes that are associated with innate immune, cellular defense and/or other kinds of immune system in the hypothalamus

In-stream Escherichia coli modeling using high-temporal-resolution data with deep learning and process-based models

Contamination of surface waters with microbiological pollutants is a major concern to public health. Although long-term and high-frequency Escherichia coli (E. coli) monitoring can help prevent diseases from fecal pathogenic microorganisms, such monitoring is time-consuming and expensive. Process-driven models are an alternative means for estimating concentrations of fecal pathogens. However, process-based modeling still has limitations in improving the model accuracy because of the complexity of relationships among hydrological and environmental variables. With the rise of data availability and computation power, the use of data-driven models is increasing. In this study, we simulated fate and transport of E. coli in a 0.6 km(2) tropical headwater catchment located in the Lao People's Democratic Republic (Lao PDR) using a deep-learning model and a process-based model. The deep learning model was built using the long short-term memory (LSTM) methodology, whereas the process-based model was constructed using the Hydrological Simulation Program-FORTRAN (HSPF). First, we calibrated both models for surface as well as for subsurface flow. Then, we simulated the E. coli transport with 6 min time steps with both the HSPF and LSTM models. The LSTM provided accurate results for surface and subsurface flow with 0.51 and 0.64 of the Nash-Sutcliffe efficiency (NSE) values, respectively. In contrast, the NSE values yielded by the HSPF were -0.7 and 0.59 for surface and subsurface flow. The simulated E. coli concentrations from LSTM provided the NSE of 0.35, whereas the HSPF gave an unacceptable performance with an NSE value of -3.01 due to the limitations of HSPF in capturing the dynamics of E. coli with land-use change. The simulated E. coli concentration showed the rise and drop patterns corresponding to annual changes in land use. This study showcases the application of deep-learning-based models as an efficient alternative to process-based models for E. coli fate and transport simulation at the catchment scale

Hierarchical Network Architecture for Non-Safety Applications in Urban Vehicular Ad-Hoc Networks

In the vehicular ad-hoc networks (VANETs), wireless access in vehicular environments (WAVE) as the core networking technology is suitable for supporting safety-critical applications, but it is difficult to guarantee its performance when transmitting non-safety data, especially high volumes of data, in a multi-hop manner. Therefore, to provide non-safety applications effectively and reliably for users, we propose a hybrid V2V communication system (HVCS) using hierarchical networking architecture: a centralized control model for the establishment of a fast connection and a local data propagation model for efficient and reliable transmissions. The centralized control model had the functionality of node discovery, local ad-hoc group (LAG) formation, a LAG owner (LAGO) determination, and LAG management. The local data propagation indicates that data are transmitted only within the LAG under the management of the LAGO. To support the end-to-end multi-hop transmission over V2V communication, vehicles outside the LAG employ the store and forward model. We designed three phases consisting of concise device discovery (CDD), concise provisioning (CP), and data transmission, so that the HVCS is highly efficient and robust on the hierarchical networking architecture. Under the centralized control, the phase of the CDD operates to improve connection establishment time, and the CP is to simplify operations required for security establishment. Our HVCS is implemented as a two-tier system using a traffic controller for centralized control using cellular networks and a smartphone for local data propagation over Wi-Fi Direct. The HVCS’ performance was evaluated using Veins, and compared with WAVE in terms of throughput, connectivity, and quality of service (QoS). The effectiveness of the centralized control was demonstrated in comparative experiments with Wi-Fi Direct. The connection establishment time measured was only 0.95 s for the HVCS. In the case of video streaming services through the HVCS, about 98% of the events could be played over 16 frames per second. The throughput for the streaming data was between 74% to 81% when the vehicle density was over 50%. We demonstrated that the proposed system has high throughput and satisfies the QoS of streaming services even though the end-to-end delay is a bit longer when compared to that of WAVE