In Colorado's Lower Arkansas River Basin (LARB), inefficient irrigation and canal seepage contribute to salinization and waterlogging of irrigated lands and to stream-aquifer pollution. The geographic information system (GIS)-based river basin management model River GeoDSS is applied to further explore best management practices (BMPs) earlier determined to remedy these agro-environmental impacts. Unfortunately, BMP benefits are offset by altered irrigation return flows which change historical downstream river flows, threatening compliance with water rights and the Arkansas River Compact. Compensation is possible through optimal sizing and operation of a dedicated reservoir storage account. Multi-agent optimization combines a metaheuristic mutation linear particle swarm optimization (MLPSO) with a fuzzy rule-based system to produce generalized operational policies along with optimal storage sizing, to enable BMPs while satisfying legal constraints. A storage account making up less than 5% of available reservoir capacity can be operated with rules that enable implementation of even the most aggressive BMPs.

在科罗拉多州的阿肯色州下游流域（LARB），低效的灌溉和渠道渗漏导致了灌溉土地的盐碱化和涝渍，并造成了地下水蓄水层的污染。基于地理信息系统（GIS）的流域管理模型River GeoDSS被用于进一步探索最佳管理实践（BMP），这些最佳管理实践是较早确定的，以补救这些农业环境影响。不幸的是，BMP的收益被灌溉回流的变化所抵消，灌溉回流改变了历史上下游的河流流量，威胁到对水权和阿肯色河契约的遵守。通过优化大小和专用储油库帐户的操作，可以进行补偿。多主体优化将元启发式突变线性粒子群优化（MLPSO）与基于模糊规则的系统相结合，以产生通用的操作策略以及最佳的存储大小，从而在满足法律约束的同时启用BMP。可以使用规则执行操作的存储帐户（仅占可用存储库容量的5％不到），这些规则甚至可以实施最具攻击性的BMP。