Chetan Maringanti

Application of a Multi-Objective Optimization Method to Provide Least Cost Alternatives for NPS Pollution Control

Nonpoint source (NPS) pollutants such as phosphorus, nitrogen, sediment, and pesticides are the foremost sources of water contamination in many of the water bodies in the Midwestern agricultural watersheds. This problem is expected to increase in the future with the increasing demand to provide corn as grain or stover for biofuel production. Best management practices (BMPs) have been proven to effectively reduce the NPS pollutant loads from agricultural areas. However, in a watershed with multiple farms and multiple BMPs feasible for implementation, it becomes a daunting task to choose a right combination of BMPs that provide maximum pollution reduction for least implementation costs. Multi-objective algorithms capable of searching from a large number of solutions are required to meet the given watershed management objectives. Genetic algorithms have been the most popular optimization algorithms for the BMP selection and placement. However, previous BMP optimization models did not study pesticide which is very commonly used in corn areas. Also, with corn stover being projected as a viable alternative for biofuel production there might be unintended consequences of the reduced residue in the corn fields on water quality. Therefore, there is a need to study the impact of different levels of residue management in combination with other BMPs at a watershed scale. In this research the following BMPs were selected for placement in the watershed: (a) residue management, (b) filter strips, (c) parallel terraces, (d) contour farming, and (e) tillage. We present a novel method of combing different NPS pollutants into a single objective function, which, along with the net costs, were used as the two objective functions during optimization. In this study we used BMP tool, a database that contains the pollution reduction and cost information of different BMPs under consideration which provides pollutant loads during optimization. The BMP optimization was performed using a NSGA-II based search method. The model was tested for the selection and placement of BMPs in Wildcat Creek Watershed, a corn dominated watershed located in northcentral Indiana, to reduce nitrogen, phosphorus, sediment, and pesticide losses from the watershed. The Pareto optimal fronts (plotted as spider plots) generated between the optimized objective functions can be used to make management decisions to achieve desired water quality goals with minimum BMP implementation and maintenance cost for the watershed. Also these solutions were geographically mapped to show the locations where various BMPs should be implemented. The solutions with larger pollution reduction consisted of buffer filter strips that lead to larger pollution reduction with greater costs compared to other alternatives.

Comparing the selection and placement of best management practices in improving water quality using a multiobjective optimization and targeting method.

Suites of Best Management Practices (BMPs) are usually selected to be economically and environmentally efficient in reducing nonpoint source (NPS) pollutants from agricultural areas in a watershed. The objective of this research was to compare the selection and placement of BMPs in a pasture-dominated watershed using multiobjective optimization and targeting methods. Two objective functions were used in the optimization process, which minimize pollutant losses and the BMP placement areas. The optimization tool was an integration of a multi-objective genetic algorithm (GA) and a watershed model (Soil and Water Assessment Tool—SWAT). For the targeting method, an optimum BMP option was implemented in critical areas in the watershed that contribute the greatest pollutant losses. A total of 171 BMP combinations, which consist of grazing management, vegetated filter strips (VFS), and poultry litter applications were considered. The results showed that the optimization is less effective when vegetated filter strips (VFS) are not considered, and it requires much longer computation times than the targeting method to search for optimum BMPs. Although the targeting method is effective in selecting and placing an optimum BMP, larger areas are needed for BMP implementation to achieve the same pollutant reductions as the optimization method.

Remote Sensing, Public Health & Disaster Mitigation

The authors review advances in applications for geotechnologies, specifically earth-observing satellite remote sensing, geo-positioning (i.e. USA’s Global Positioning System (GPS), Russia’s Global’naya Navigatsionnaya Sputnikovaya Sistema (GLONASS), Europe’s Galileo and China’s Beidou/Compass) and selected geo-spatial modeling software for public health and disaster management applications, with an emphasis on environmental health and environmental sustainability. Specific applications addressed include the use of remote sensing for infectious disease vector habitat identification and ecologically sustainable disease vector population mitigation, as well as the integration of GPS into mobile CD4 testing devices for HIV/AIDS. Public domain software models described include the Spatio-Temporal Epidemiological Modeler (STEM) and the Hydrologic Engineering River Analysis System (HEC-RAS) for flood modeling. Examples of regional, national and global real-time data acquisition and near-real-time data product development and distribution for time-critical events are offered, specifically through the Purdue Terrestrial Observatory (PTO), the United States Geological Survey (USGS) supported AmericaView and the International Charter – Space & Major Disasters.

Development of a Multi-Objective Optimization Tool for the Selection and Placement of BMPs for Pesticide Control

Best management practices (BMPs) have been proven to effectively reduce the Nonpoint source (NPS) pollution loads from agricultural areas. Pesticides (particularly atrazine used in corn fields) are the foremost source of water contamination in many of the water bodies in Indiana, exceeding the 3 ppb threshold for drinking water on a number of days. Candidate BMPs that could effectively control the movement of atrazine include buffer strips and land management practices such as tillage operations. However, selection and placement of BMPs in watersheds to achieve an ecologically effective and economically feasible solution is a daunting task. BMP placement decisions under such complex conditions require a multi-objective optimization algorithm that would search for the best possible solutions that satisfies the given objectives. Genetic algorithms (GA) have been the most popular optimization algorithms for the BMP section and placement problem. However, most of the previous works done have considered the two objectives individually during the optimization process by introducing a constraint on the other objective, therefore decreasing the degree of freedom to find the solution. Most of the optimization models also had a dynamic linkage with the water quality model, which increased the computation time considerably thus restricting them to apply models on field scale or relatively smaller (11 or 14 digit HUC) watersheds. In the present work the optimization for atrazine reduction is performed by considering the two objectives simultaneously using a multi-objective genetic algorithm (NSGA-II). The limitation with the dynamic linkage has been overcome through the development of BMP tool, a novel technique to estimate the pollution reduction efficiciencies of BMPs a priori. The model was used for the selection and placement of BMPs in Wildcat Creek Watershed (USGS 8 digit [05120107] HUC), Indiana, for atrazine reduction. The most ecologically effective solution from the model had a reduction of 30%, in atrazine concentration, from the base scenario with a BMP implementation cost of

High Performance Computing Application to Address Non-Point Source Pollution at a Watershed Level

10 million in the watershed per annum. The pareto-optimal fronts generated between the two optimized objective functions can be used to achieve desired water quality goals with minimum BMP implementation cost for the watershed..

Development of a multi-objective optimization tool for the selection and placement of BMPs for nonpoint source pollution control

Best management practices (BMPs) have been proven to effectively reduce the Nonpoint source (NPS) pollution loads from agricultural areas. BMP selection and placement problem needs to be addressed for the placement of BMPs in a watershed at a field scale that would achieve maximum pollution reduction subjected to minimum cost increase for the placement of BMPs in the watershed through optimization techniques. The BMP selection problem is linked with a hydrologic and water quality simulation model to estimate the pollution loads at various locations in the watershed. However, the optimization techniques get very complicated as the size of the watershed gets larger as the design variables increase proportionately. Also, the BMP optimization is linked to the watershed level hydrologic and water quality simulation mode and this makes the problem require very high amount of computation time for the simulations to get to a near optimal set of BMP placement in the watershed. In this regard, the high performance computing (HPC) helps a great deal by providing a platform for running the simulations on a multitude of processors rather than a single processor. Unix serves as the de-facto operating system for most of the HPC applications as the architecture allows to run simulations that take a long time to run in much lesser time when compared to the Windows OS machines. In the present work we have used the Soil and Water Assessment Tool (SWAT) model in combination with a genetic algorithm (GA) optimization technique NSGA-II to optimally select and place BMPs at a watershed scale. We have tested the application of the optimization algorithm to reduce sediment, nitrogen, and phosphorus from the L’Anguille River Watershed, Arkansas and pesticide from Wildcat Creek Watershed, Indiana. The SWAT model was compiled in Unix and the optimization routine for BMP selection and placement was coded as an objective function in NSGA-II program in C language. The optimization model was compiled in Unix to optimize the various BMPs that can be placed at a field scale, which is represented as a Hydrologic Response Unit ( HRU) in SWAT.

Inverse modeling of beaver reservoir's water spectral reflectance.

Nonpoint source (NPS) pollution from agricultural areas can be minimized by the implementation of best management practices (BMPs) at the source (farm), by controlling the movement of pollutants from the agricultural areas into the receiving bodies. However, selection and implementation of BMPs in every farm, to achieve cost effective NPS pollution reduction in a watershed may be a daunting task. This typically requires obtaining an optimal solution, from the many million solutions that are possible, that is ecologically effective and economically feasible for the placement of BMPs. The previous works done to solve this problem have used genetic algorithms (GA) for optimizing the two objectives of : 1) pollution reduction and 2) cost increase. But most of the works have considered the two objectives individually during the optimization process by introducing a constraint on the other objective. This approach of finding an optimal solution is not practical as the constrained objective results in a decrease in the degree of freedom in the solution space. In the present work the optimization is performed by considering the two objectives simultaneously. A multi-objective genetic algorithm (NSGA-II) was used to optimize the two objectives which gave a tradeoff between the two objectives for a range of optimal pollution reduction alternatives and their corresponding cost for implementation of BMPs. The model was used for the selection and placement of BMPs in L’Anguille River Watershed, Arkansas, USA for total phosphorus (TP) reduction. The most ecologically effective solution from the model had a TP reduction of 33% from the base scenario for a BMP implementation cost of

Development of a multiobjective optimization tool for the selection and placement of best management practices for nonpoint source pollution control

14 million. The tradeoff was obtained between the two optimized objective functions which can be used to achieve desired water quality goals with the minimum BMP implementation cost for the watershed.

Selection and placement of best management practices used to reduce water quality degradation in Lincoln Lake watershed

Estimation of inherent optical properties (IOP) needed for water quality evaluation by remote sensing models is very complex, primarily due to the large number of model simulations needed to find optimal parameter values. This study presents an approach for optimally parameterizing the IOP values of a physical hyperspectral optical - Monte Carlo (PHO-MC) model. An artificial neural network (ANN) based pseudo simulator combined with the Nondominated Sorted Genetic Algorithm II (NSGA II) was used to efficiently perform a large number of model simulations and to search the optimal parameter values for IOP determination. Concentrations of suspended matter (sm), chlorophyll-a (chl), and total dissolved organic matter (DOM) along with the reflectance data at 16 different wavelengths were measured at 48 sampling stations in the Beaver Reservoir, Arkansas, between 2003 and 2005 and were used to evaluate the IOP values. Measured concentrations and reflectance data from 24 sampling stations were used to optimize IOP parameter values for sm, chl, and DOM. The data collected from the remaining 24 sampling stations were used for the validation of PHO-MC model-predicted reflectance by using optimized IOP values. PHO-MC predicted reflectance values were significantly correlated (r = 0.90, p < 0.01) with the corresponding measured reflectance values, indicating that the pseudo simulator combined with the NSGA II accurately estimated the IOP values. An estimated 1010 years of calculation time was reduced to less than 3 min by using the pseudo simulator and NSGA II to supplement the PHO-MC model for estimating the IOP values.