• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Expert Systems with Application >Developing an intelligent expert system for streamflow prediction, integrated in a dynamic decision support system for managing multiple reservoirs: A case study
【24h】

Developing an intelligent expert system for streamflow prediction, integrated in a dynamic decision support system for managing multiple reservoirs: A case study

机译：开发用于流量预测的智能专家系统，并集成到用于管理多个水库的动态决策支持系统中：一个案例研究

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Since fresh water is limited while agricultural and human water demands are continuously increasing, optimal prediction and management of streamflows as a source of fresh water is crucially important. This study investigates and demonstrates how data preprocessing and data mining techniques would improve the accuracy of streamflow predictive models. Based on easily accessible Snow Telemetry data (SNOTEL), four streamflow prediction models - autoregressive integrated moving average (ARIMA), artificial neural networks (ANNs), a hybrid-model of ANN and ARIMA (ANN-ARIMA), and an adaptive neuro fuzzy inference system (ANFIS) - were developed and utilized in a streamflow prediction process on Elephant Butte Reservoir. Utilizing the statistical correlation analysis and the extracting importance degrees of predictors led to efficiently select the most effective predictors for daily and monthly streamflow to Elephant Butte Reservoir. For the daily prediction time step, by preprocessing the historical data and extracting and utilizing the extracted climate variability indices through data mining techniques, the ANFIS model achieved a superior streamflow prediction performance for Elephant Butte Reservoir compared to the other three evaluated prediction models. Additionally, for predicting monthly streamflow to the Elephant Butte Reservoir, ANFIS showed significantly higher accuracy than the ANNs. As an optimal application of the developed predictive expert systems, successful integrating the prediction models in integrated reservoir operations balanced the need for a reliable supply of irrigation water against losses through evaporation. The optimal operation plan significantly minimizes the total evaporation loss from both reservoirs by providing the optimal storage levels in both reservoirs. This study provides the conceptual procedures of non-seasonal (ARIMA) model, and since the model is univariate, it demonstrates a strongly-reliable inflow prediction when existing information is limited to streamflow data as a predictor. (C) 2017 Elsevier Ltd. All rights reserved.
机译：由于淡水有限，而农业和人类用水需求却不断增加，因此，作为淡水来源的水流的最佳预测和管理至关重要。这项研究调查并演示了数据预处理和数据挖掘技术将如何提高流量预测模型的准确性。基于易于访问的Snow Telemetry数据（SNOTEL），四个流预测模型-自回归综合移动平均值（ARIMA），人工神经网络（ANN），ANN和ARIMA的混合模型（ANN-ARIMA）以及自适应神经模糊推理系统（ANFIS）-在Elephant Butte水库的流量预测过程中得到开发和利用。利用统计相关分析和提取预测因子的重要性程度，可以有效地选择每日和每月流向大象比尤水库的最有效预测因子。对于每日的预测时间步骤，通过预处理历史数据并通过数据挖掘技术提取和利用提取的气候变异性指数，与其他三个评估的预测模型相比，ANFIS模型在象坡水库中实现了优异的流量预测性能。此外，为了预测大象坡水库的每月流量，ANFIS显示的准确度明显高于ANN。作为已开发的预测专家系统的最佳应用，成功地将预测模型集成到集成的水库调度中，平衡了对可靠灌溉水和蒸发损失的需求。最佳运行计划通过在两个水库中提供最佳的存储水平，大大降低了两个水库的总蒸发损失。这项研究提供了非季节（ARIMA）模型的概念程序，并且由于该模型是单变量的，因此，当现有信息仅限于流量数据作为预测因子时，它证明了流量预测非常可靠。 （C）2017 Elsevier Ltd.保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号