Yi-Chen E. Yang

Bio: Yi-Chen E. Yang is an academic researcher from Lehigh University. The author has contributed to research in topics: Climate change & Water resources. The author has an hindex of 16, co-authored 48 publications receiving 986 citations. Previous affiliations of Yi-Chen E. Yang include University of Massachusetts Amherst & National Taiwan University.

A decentralized optimization algorithm for multiagent system-based watershed management

Abstract: [1] A watershed can be simulated as a multiagent system (MAS) composed of spatially distributed land and water users (agents) within a common defined environment. The watershed system is characterized by distributed decision processes at the agent level with a coordination mechanism organizing the interactions among individual decision processes at the system level. This paper presents a decentralized (distributed) optimization method known as constraint-based reasoning, which allows individual agents in an MAS to optimize their behaviors over various alternatives. The method incorporates the optimization of all agents' objectives through an interaction scheme, in which the ith agent optimizes its objective with a selected priority for collaboration and forwards the solution and consequences to all agents that interact with it. Agents are allowed to determine how important their own objectives are in comparison with the constraints, using a local interest factor (βi). A large βi value indicates a selfish agent who puts high priority on its own benefit and ignores collaboration requirements. This bottom-up problem-solving approach mimics real-world watershed management problems better than conventional “top-down” optimization methods in which it is assumed that individual agents will completely comply with any recommendations that the coordinator makes. The method is applied to a steady state hypothetical watershed with three off-stream human agents, one in-stream human agent (reservoir), and two ecological agents.

The Indus Basin of Pakistan: The Impacts of Climate Risks on Water and Agriculture

Abstract: This study, Indus basin of Pakistan: the impacts of climate risks on water and agriculture was undertaken at a pivotal time in the region. The weak summer monsoon in 2009 created drought conditions throughout the country. This followed an already tenuous situation for many rural households faced with high fuel and fertilizer costs and the impacts of rising global food prices. Then catastrophic monsoon flooding in 2010 affected over 20 million people, devastating their housing, infrastructure, and crops. Damages from this single flood event were estimated at US dollar 10 billion, half of which were losses in the agriculture sector. Notwithstanding the debate as to whether these observed extremes are evidence of climate change, an investigation is needed regarding the extent to which the country is resilient to these shocks. It is thus timely, if not critical, to focus on climate risks for water, agriculture, and food security in the Indus basin of Pakistan.

Identification of hydrologic indicators related to fish diversity and abundance: A data mining approach for fish community analysis

Abstract: [1] This paper develops a new approach to identify hydrologic indicators related to fish community and generate a quantitative function between an ecological target index and the identified hydrologic indicators. The approach is based on genetic programming (GP), a data mining method. Using the Shannon Index (a fish community diversity index) or the number of individuals (total abundance) of a fish community, as an ecological target, the GP identified the most ecologically relevant hydrologic indicators (ERHIs) from 32 indicators of hydrologic alteration, for the case study site, the upper Illinois River. Robustness analysis showed that different GP runs found a similar set of ERHIs; each of the identified ERHI from different GP runs had a consistent relationship with the target index. By comparing the GP results with those from principal component analysis and autecology matrix, the three approaches identified a small number (six) of common ERHIs. Particularly, the timing of low flow (Dmin) seems to be more relevant to the diversity of the fish community, while the magnitude of the low flow (Qb) is more relevant to the total fish abundance; large rising rates result in a significant improvement of fish diversity, which is counterintuitive and against previous findings. The quantitative function developed by GP was further used to construct an indicator impact matrix (IIM), which was demonstrated as a potentially useful tool for streamflow restoration design.

Assessing groundwater policy with coupled economic-groundwater hydrologic modeling

Abstract: This study explores groundwater management policies and the effect of modeling assumptions on the projected performance of those policies The study compares an optimal economic allocation for groundwater use subject to streamflow constraints, achieved by a central planner with perfect foresight, with a uniform tax on groundwater use and a uniform quota on groundwater use The policies are compared with two modeling approaches, the Optimal Control Model (OCM) and the Multi-Agent System Simulation (MASS) The economic decision models are coupled with a physically based representation of the aquifer using a calibrated MODFLOW groundwater model The results indicate that uniformly applied policies perform poorly when simulated with more realistic, heterogeneous, myopic, and self-interested agents In particular, the effects of the physical heterogeneity of the basin and the agents undercut the perceived benefits of policy instruments assessed with simple, single-cell groundwater modeling This study demonstrates the results of coupling realistic hydrogeology and human behavior models to assess groundwater management policies The Republican River Basin, which overlies a portion of the Ogallala aquifer in the High Plains of the United States, is used as a case study for this analysis

Calibration approaches for distributed hydrologic models in poorly gaged basins: implication for streamflow projections under climate change

Abstract: . This study tests the performance and uncertainty of calibration strategies for a spatially distributed hydrologic model in order to improve model simulation accuracy and understand prediction uncertainty at interior ungaged sites of a sparsely gaged watershed. The study is conducted using a distributed version of the HYMOD hydrologic model (HYMOD_DS) applied to the Kabul River basin. Several calibration experiments are conducted to understand the benefits and costs associated with different calibration choices, including (1) whether multisite gaged data should be used simultaneously or in a stepwise manner during model fitting, (2) the effects of increasing parameter complexity, and (3) the potential to estimate interior watershed flows using only gaged data at the basin outlet. The implications of the different calibration strategies are considered in the context of hydrologic projections under climate change. To address the research questions, high-performance computing is utilized to manage the computational burden that results from high-dimensional optimization problems. Several interesting results emerge from the study. The simultaneous use of multisite data is shown to improve the calibration over a stepwise approach, and both multisite approaches far exceed a calibration based on only the basin outlet. The basin outlet calibration can lead to projections of mid-21st century streamflow that deviate substantially from projections under multisite calibration strategies, supporting the use of caution when using distributed models in data-scarce regions for climate change impact assessments. Surprisingly, increased parameter complexity does not substantially increase the uncertainty in streamflow projections, even though parameter equifinality does emerge. The results suggest that increased (excessive) parameter complexity does not always lead to increased predictive uncertainty if structural uncertainties are present. The largest uncertainty in future streamflow results from variations in projected climate between climate models, which substantially outweighs the calibration uncertainty.

Evolutionary algorithms and other metaheuristics in water resources

Abstract: The development and application of evolutionary algorithms (EAs) and other metaheuristics for the optimisation of water resources systems has been an active research field for over two decades. Research to date has emphasized algorithmic improvements and individual applications in specific areas (e.g. model calibration, water distribution systems, groundwater management, river-basin planning and management, etc.). However, there has been limited synthesis between shared problem traits, common EA challenges, and needed advances across major applications. This paper clarifies the current status and future research directions for better solving key water resources problems using EAs. Advances in understanding fitness landscape properties and their effects on algorithm performance are critical. Future EA-based applications to real-world problems require a fundamental shift of focus towards improving problem formulations, understanding general theoretic frameworks for problem decompositions, major advances in EA computational efficiency, and most importantly aiding real decision-making in complex, uncertain application contexts.

Annual Report 2008

Abstract: The following section, Management’s Discussion and Analysis of Operations, provides an overview of the consolidated financial statements of Fujitsu Limited (the “Company”) and its consolidated subsidiaries (together, the “Group”) for the year ended March 31, 2008 (fiscal 2007). Forward-looking statements in this section are based on management’s understanding and best judgment as of March 31, 2008.

Many objective robust decision making for complex environmental systems undergoing change

Abstract: This paper introduces many objective robust decision making (MORDM). MORDM combines concepts and methods from many objective evolutionary optimization and robust decision making (RDM), along with extensive use of interactive visual analytics, to facilitate the management of complex environmental systems. Many objective evolutionary search is used to generate alternatives for complex planning problems, enabling the discovery of the key tradeoffs among planning objectives. RDM then determines the robustness of planning alternatives to deeply uncertain future conditions and facilitates decision makers' selection of promising candidate solutions. MORDM tests each solution under the ensemble of future extreme states of the world (SOW). Interactive visual analytics are used to explore whether solutions of interest are robust to a wide range of plausible future conditions (i.e., assessment of their Pareto satisficing behavior in alternative SOW). Scenario discovery methods that use statistical data mining algorithms are then used to identify what assumptions and system conditions strongly influence the cost-effectiveness, efficiency, and reliability of the robust alternatives. The framework is demonstrated using a case study that examines a single city's water supply in the Lower Rio Grande Valley (LRGV) in Texas, USA. Results suggest that including robustness as a decision criterion can dramatically change the formulation of complex environmental management problems as well as the negotiated selection of candidate alternatives to implement. MORDM also allows decision makers to characterize the most important vulnerabilities for their systems, which should be the focus of ex post monitoring and identification of triggers for adaptive management.

Groundwater quality and depletion in the Indo-Gangetic Basin mapped from in situ observations

Abstract: Groundwater abstraction from the transboundary Indo-Gangetic Basin comprises 25% of global groundwater withdrawals, sustaining agricultural productivity in Pakistan, India, Nepal and Bangladesh. Recent interpretations of satellite gravity data indicate that current abstraction is unsustainable, yet these large-scale interpretations lack the spatio-temporal resolution required to govern groundwater effectively. Here we report new evidence from high-resolution in situ records of groundwater levels, abstraction and groundwater quality, which reveal that sustainable groundwater supplies are constrained more by extensive contamination than depletion. We estimate the volume of groundwater to 200 m depth to be >20 times the combined annual flow of the Indus, Brahmaputra and Ganges, and show the water table has been stable or rising across 70% of the aquifer between 2000 and 2012. Groundwater levels are falling in the remaining 30%, amounting to a net annual depletion of 8.0 ± 3.0 km3. Within 60% of the aquifer, access to potable groundwater is restricted by excessive salinity or arsenic. Recent groundwater depletion in northern India and Pakistan has occurred within a longer history of groundwater accumulation from extensive canal leakage. This basin-wide synthesis of in situ groundwater observations provides the spatial detail essential for policy development, and the historical context to help evaluate recent satellite gravity data.