Ni-Bin Chang

ABSTRACT This paper presents a modified formulation of a fuzzy multiobjective programming model in order to illustrate the tendency of nonlinearity in many environmental problems. The genetic algorithm is described as a tool to solve a typical water pollution control problem

Abstract— Heavy convective rainfall often results in flooding in urban regions. The rainfall runoff process, however, is highly complex, nonlinear, and temporally and spatially varying because of the variability of the terrain and climate attributes. An intelligent traffic system covering ...

Urban environmental conditions are strongly dependent on the land use and land cover properties. Urban and rural areas normally exhibit obvious difference in land surface temperature (LST). The Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua (PM satellite) ...

This paper presents a neural-fuzzy inference approach to identify the land use and land cover (LULC) patterns in large urban areas with the 8-meter resolution of multi-spectral images collected by Formosat-2 satellite. Texture and feature analyses support the retrieval of fuzzy rules in the context of data mining to discern the embedded LULC patterns via a neural-fuzzy inference approach. The case study for Taichung City in central Taiwan shows the application potential based on five LULC classes. With the aid of integrated fuzzy rules and a neural network model, the optimal weights associated with these achievable rules can be determined with phenomenological and theoretical implications. Through appropriate model training and validation stages with respect to a groundtruth data set, research findings clearly indicate that the proposed remote sensing technique can structure an improved screening and sequencing procedure when selecting rules for LULC classification. There is no limitation of using broad spectral bands for category separation by this method, such as the ability to reliably separate only a few (4-5) classes. This normalized difference vegetation index (NDVI)-based data mining technique has shown potential for LULC pattern recognition in different regions, and is not restricted to this sensor, location or date.

ABSTRACT Lake Okeechobee, located in south Florida and originated about 6000 years ago during oceanic recession, is the second largest freshwater lake contained entirely within the United States. During recent years, four major hurricanes, Irene (1999), Frances (2004), Jeanne (2004), and Wilma (2005), made landfall near Lake Okeechobee. As a result, the lake's hydrodynamic patterns, water level, and water quality have been extensively influenced by hurricanes in the past decade. The direct landfall of Hurricane Wilma on the Lake Okeechobee area led to a drastic structural change to the ecosystem, resulting in an abrupt population decrease of submerged aquatic vegetation (SAV). During the same time period, south Florida also suffered from intermittent droughts in 2000–2001 and 2006–2008 due to climate variability. Drought stress and hurricane impacts induce complex ecosystem responses, including pervasive changes in species composition and productivity that may persist as changes in habitat, lake trophic state, light penetration depth, water levels, and nutrient cycling. After the 2006–2008 drought, decreased turbidity due to reduced sediment hydrodynamics, along with the increased runoff and nutrient inflows to the aquatic ecosystem, led to a range of ecological responses associated with gradual increases in primary productivity and the recovery of SAV in lake shore environments. The ecosystem then recovered after the population increase of SAV, triggering active ecodynamics among phytoplankton, zooplankton, and fish that had abruptly vanished after successive hurricanes in 2004 and 2005. To address such a unique structural change in the lake ecosystem, this study developed a structurally dynamic model to quantify the ecodynamics of the four main aquatic system organisms (SAV, phytoplankton, zooplankton, and fish) based on a thermodynamics index (exergy) and elucidated the ecosystem recovery pathways under the coupled impact of hurricanes and droughts.

ABSTRACT Urban growth and agricultural production have caused an influx of nutrients into Lake Erie, leading to eutrophication in the water body. These conditions result in the formation of algal blooms, some of which are toxic due to the presence of Microcystis (a cyanobacteria), which produces the hepatotoxin microcystin. The hepatotoxin microcystin threatens human health and the ecosystem, and it is a concern for water treatment plants using the lake water as a tap water source. This study demonstrates the prototype of a near real-time early warning system using integrated data fusion and mining (IDFM) techniques with the aid of both hyperspectral (MERIS) and multispectral (MODIS and Landsat) satellite sensors to determine spatiotemporal microcystin concentrations in Lake Erie. In the proposed IDFM, the MODIS images with high temporal resolution are fused with the MERIS and Landsat images with higher spatial resolution to create synthetic images on a daily basis. The spatiotemporal distributions of microcystin within western Lake Erie were then reconstructed using the band data from the fused products with machine learning or data mining techniques such as genetic programming (GP) models. The performance of the data mining models derived using fused hyperspectral and fused multispectral sensor data are quantified using four statistical indices. These data mining models were further compared with traditional two-band models in terms of microcystin prediction accuracy. This study confirmed that GP models outperformed traditional two-band models, and additional spectral reflectance data offered by hyperspectral sensors produces a noticeable increase in the prediction accuracy especially in the range of low microcystin concentrations.

ABSTRACT Lake Erie has a history of algal blooms, due to eutrophic conditions attributed to urban and agricultural activities. Blue-green algae or cyanobacteria thrive in these eutrophic conditions, since they require little energy for cell maintenance and growth. Microcystis are a type of blue-green algae of particular concern, because they produce microcystin, a potent hepatotoxin. Microcystin not only presents a threat to the ecosystem, but it threatens commercial fishing operations and water treatment plants using the lake as a water source. In this paper, we have proposed an early warning system using Integrated Data Fusion and Machine-learning (IDFM) techniques to determine microcystin concentrations and distribution by measuring the surface reflectance of the water body using satellite sensors. The fine spatial resolution of Landsat is fused with the high temporal resolution of MODIS to create a synthetic image possessing both high temporal and spatial resolution. As a demonstration, the spatiotemporal distribution of microcystin within western Lake Erie is reconstructed using the band data from the fused products and applied machine-learning techniques. Analysis of the results through statistical indices confirmed that the Genetic Programming (GP) model has potential accurately estimating microcystin concentrations in the lake (R2 = 0.5699).

ABSTRACT: Six locations and five separate aquifer systems for Aquifer Storage Recovery (ASR) were evaluated before selecting the Carrizo Aquifer option in San Antonio, Texas in 2004. This site was selected since it provided the lowest costs for transmission, site ...

ABSTRACT The concentration of total organic carbon (TOC) in surface waters is subject to seasonal variation, as well as abrupt changes in concentration due to events. In drinking water treatment, TOC is a precursor to disinfection byproducts such as total trihalomethanes (TTHM). With the aid of an early warning system for the detection of TOC concentrations, water treatment operators could make more informed decisions and adjust the treatment processes to minimize the generation of disinfection byproducts. In this paper, a near real-time monitoring system is explored using the Integrated Data Fusion and Machine-learning (IDFM) technique to predict the spatial distribution of TOC in a lake based upon surface reflectance data from satellite imagery. Landsat 5 TM and MODIS Terra satellite imagery can be acquired free of charge, yet MODIS has coarse spatial resolution, while Landsat has a lengthy 16 day revisit time. This difficulty is solved using data fusion algorithms to fuse the fine spatial resolution of Landsat with the daily revisit time of MODIS to generate a synthetic image with both high spatial and temporal resolution. To demonstrate the capabilities of IDFM, this case study uses the fused surface reflectance band data and applied machine-learning techniques to reconstruct the spatiotemporal distribution of TOC in Harsha Lake, which serves as the source water intake for the McEwen Water Treatment Plant in Ohio. The results of this application of IDFM were analyzed using 4 statistical indices, which indicated that the Artificial Neural Network model is capable of reconstructing TOC concentrations throughout the lake.

ABSTRACT Urban growth and agricultural production have caused an influx of nutrients into Lake Erie, leading to eutrophic zones. These conditions result in the formation of algal blooms, some of which are toxic due to the presence of Microcystis (a cyanobacteria), which produces the hepatotoxin microcystin. Microcystis has a unique advantage over its competition as a result of the invasive zebra mussel population that filters algae out of the water column except for the toxic Microcystis. The toxin threatens human health and the ecosystem, and it is a concern for water treatment plants using the lake water as a tap water source. This presentation demonstrates the prototype of a near real-time early warning system using Integrated Data Fusion techniques with the aid of both hyperspectral remote sensing data to determine spatiotemporal microcystin concentrations. The temporal resolution of MODIS is fused with the higher spatial and spectral resolution of MERIS to create synthetic images on a daily basis. As a demonstration, the spatiotemporal distributions of microcystin within western Lake Erie are reconstructed using the band data from the fused products and applied machine-learning techniques. Analysis of the results through statistical indices confirmed that the this type of algorithm has better potential to accurately estimating microcystin concentrations in the lake, which is better than current two band models and other computational intelligence models.

Recent advances in control engineering suggest that hybrid control strategies, integrating some ideas and paradigms existing in different soft computing techniques, such as fuzzy logic, genetic algorithms, rough set theory, and neural networks, may provide improved control performance in wastewater treatment processes. This paper presents an innovative hybrid control algorithm leading to integrate the distinct aspects of indiscernibility capability of rough set theory and search capability of genetic algorithms with conventional neural-fuzzy controller design. The methodology proposed in this study employs a three-stage analysis that is designed in series for generating a representative state function, searching for a set of multi-objective control strategies, and performing a rough set-based autotuning for the neural-fuzzy logic controller to make it applicable for controlling an industrial wastewater treatment process. Research findings in the case study clearly indicate that the ...

Assessing the potential of non-point source pollution to assist in the planning of Best Management Practice (BMP) is significant for improving pollution prevention and control in a river basin. In many cases, however, the grid-based modelling analysis is prohibitively laborious and hindered because of insufficient information. This paper presents a new and fast methodology for catchment land-use identification and waste load estimation by properly integrating the skills of remote sensing (RS), geographic information system (GIS), global positioning system (GPS), and the Generalized Watershed Loading Functions (GWLF) model. In this analysis, eight types of land-use patterns in the watershed area of the Kao-Ping River Basin were classified with the aid of SPOT satellite images, Erdas Imagine image processing system, and ArcView GIS system. Hydrologic and geographical features were obtained or derived by the Digital Elevation Model (DEM) and GIS technique simultaneously. The GWLF model...

In water scarce areas throughout the world, stormwater and wastewater used as an alternative water supply have been put into practice to promote the sustainability of our water infrastructure. Also, in water rich areas, stormwater management and on‐site wastewater ...

Nutrients, such as nitrate, nitrite, and phosphorus, are common contaminants in many aquatic systems in the United States. Ammonia and nitrate are both regulated by the drinking water standards in the US primarily because excess levels of nitrate might cause methemoglobinemia. ...

To support nutrient removal, various stormwater treatment technologies have been developed via the use of green materials, such as sawdust, tire crumbs, sand, clay, sulfur, and limestone, as typical constituents of filter media mixes. These materials aid in the physiochemical sorption and precipitation of orthophosphates as well as in the biological transformation of ammonia, nitrates and nitrites. However, these processes are dependent upon influent conditions such as hydraulic residence time, influent orthophosphate concentrations, and other chemical species present in the inflow. This study aims to compare the physiochemical removal of orthophosphate by isotherm and column tests under differing influent conditions to realize the reliability of orthophosphate removal process with the aid of green sorption media. The green sorption media of interest in this study is composed of a 5:2:2:1 (by volume) mixture of cement sand, tire crumb, fine expanded clay, and limestone. Scenarios of...

Conventional location∕allocation models for solid-waste management usually focus on economic optimization with respect to mass balance, capacity limitation, operating, and financial constraints. But the siting of important facilities, such as landfills, incinerators, and transfer ...

Various deterministic mathematical programming models were developed to evaluate single objective or multiple objectives planning alternatives for municipal solid waste management. The common objective of minimizing the present value of overall management cost/benefit was extended to deal explicitly with environmental considerations, such as air pollution, traffic flow limitation, and leachate and noise impacts. But uncertainty plays an important role in

The emphasis on waste reduction and recycling requirements prior to incineration and the promulgation of Good Combustion Practice (GCP) for emission control of trace organic compounds during incineration have created conflicting solid-waste management goals. The most critical questions in system planning include: to what extent are recycling and incineration compatible? And what are the subsequent economic impacts on the private

ABSTRACT This paper explores spectral decomposition of environmental data for use in ad hoc artificial neural networks for predicting precipitation patterns by exploiting the nonlinear dynamic signals of oceanic teleconnection patterns found in the Northern Atlantic and Pacific. Using sophisticated ground and satellite remote sensing, including the Advanced Very High Resolution Radiometer (AVHRR) instrument onboard the NOAA satellites for sea surface temperature detection and the GOES geostationary satellite for precipitation correction of in-situ data, high predictive skill is demonstrated during the winter months within the Adirondack state Park in upstate New York, USA. Results show winter months with up to 67% of the land area accurately forecasting precipitation trends with a lead time of 3 months.