Machine learning methods to facilitate optimal water allocation and management in irrigated river basins to comply with water law
Date
2019
Authors
Rohmat, Faizal Immaddudin Wira, author
Labadie, John W., advisor
Gates, Timothy K., advisor
Bailey, Ryan T., committee member
Anderson, Charles W., committee member
Journal Title
Journal ISSN
Volume Title
Abstract
The sustainability issues facing irrigated river basins are intensified by legal and institutional regulations imposed on the hydrologic system. Although solutions that would boost water savings and quality might prove to be feasible, such imposed institutional constraints could veto their implementation, rendering them legally ineffectual. The problems of basin-scale irrigation water management in a legally-constrained system are exemplified in the central alluvial valley of the Lower Arkansas River Basin (LARB) in Colorado, USA, and in the Tripa River Basin in Indonesia. In the LARB, water and land best management practices (BMPs) have been proposed to enhance the environment, conserve water, and boost productivity; however, the legal feasibility of their implementation in the basin hinder BMP adoption. In the Tripa river basin, the rapid growth of water demand pushes the proposal of new reservoir construction. However, inadequate water availability and the lack of water law enforcement requires the basin to seek water from adjacent basins, thereby raising legal and economic feasibility issues. To address these issues, an updated version of a decision support system (DSS) named River GeoDSS has been employed to model basin-scale behavior of the LARB for both historical (baseline) and BMP implementation scenarios. River GeoDSS uses GeoMODSIM as its water allocation component, which also handles water rights and uses a deep neural network (DNN) functionality to emulate calibrated regional MODFLOW-SFR2 models in modeling complex stream-aquifer interactions. The use of DNNs for emulation if critical for extrapolating the results of MODFLOW-SFR2 simulations to un-modeled portions of the basin and for compute-efficient analysis. The BMP implementations are found to introduce significant alterations to streamflows in the LARB, including shortages in flow deliveries to water right demands and in flow deficits at the Colorado-Kansas Stateline. To address this, an advanced Fuzzy-Mutation Linear Particle Swarm Optimization (Fuzzy-MLPSO) metaheuristic algorithm is applied to determine optimal operational policies for a new storage account in John Martin Reservoir for use in mitigating the side-effects of BMP implementation on water rights and the interstate compact. Prior to the implementation of Fuzzy-MLPSO, a dedicated study is conducted to develop the integration between MLPSO and GeoMODSIM, where it is applied in addressing the water allocation issue in the Tripa River Basin. The coupling of simulation (GeoMODSIM) and optimization (MLPSO) models provides optimal sizing of reservoirs and transbasin diversions along with optimal operation policies. Aside from that, this study shows that MLPSO converges faster compared to the original PSO with sufficiently smaller swarm size. The implementations of Fuzzy-MLPSO in the LARB provided optimal operational rules for a new storage account in John Martin Reservoir dedicated to abating the undesirable impacts of BMP implementation on water rights and Stateline flows. The Fuzzy-MLPSO processes inflow, storage, seasonal, and hydrologic states into divert-to-storage/release-from-storage decisions for the new storage account. Results show that concerns over shortages in meeting water rights demands and deficits to required Stateline flow due to otherwise beneficial BMP implementations can be addressed with optimized reservoir operations.
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Subject
machine learning
reservoir operation
stream-aquifer systems
particle swarm optimization
fuzzy logic
river basin management