Multireservoir Flood-Control Optimization with Neural-Based Linear Channel Level Routing Under Tidal Effects

In order to determine the optimal hourly releases from reservoirs under the estuary tidal effects during typhoon periods, this paper develops a generalized multipurpose multireservoir optimization model for basin-scale flood control. The model objectives include: preventing the reservoir dam and the downstream river embankment from overtopping, and meeting reservoir target storage at the end of flood. The model constraints include the reservoir operations and the neural-based linear channel level routing. The proposed channel level routing developed from the feed-forward back-propagation neural network is employed to estimate the downstream water levels. The developed optimization model has been applied to the Tanshui River Basin system in Taiwan. The results obtained by the optimization model, in contrast to historical records, demonstrate successfully the practicability in solving the problem of flood control operations.     

Two-Stage Pumping Control Model for Flood Mitigation in Inundated Urban Drainage Basins

This study proposes a two-stage intelligence-based pumping control (TWOPC) model for real-time pumping operation to solve the complex problem of estimating the desired pump flow and determining the optimal combination of pumps deployed in a flood event. In Stage I of the model, the desired pump flow was forecasted using the multilayer perceptron (MLP) with hydrological information including rainfall and basin runoffs, forebay water levels, and pump flows. In Stage II, the optimal pump combination was forecasted using tree-derived rules obtained from C4.5, classification and regression tree (CART), and chi-squared automatic interaction detection (CHAID) classifiers. The East Chung-Kong pumping station in New Taipei City was used as the study area. The pumping facilities included both submersible and upright axial pumps. The optimal input–output patterns, derived from a deterministic pumping operation optimization model, were used to train and validate the proposed TWOPC model. Data for this study were collected from three storms and four typhoons that affected an urban drainage basin. A total of 1,765 records were available. The results indicated that the case with a lag time of 5 min provided the most desirable pump flows in Stage I, and the C4.5 tree-based classifier performed well in Stage II. In addition, Typhoons Sinlaku (2) (2008/9/15) and Jangmi (2008/9/29) were selected for simulating the TWOPC model. The results demonstrated that the TWOPC model provided a more favorable performance than the traditional experienced method did. Overall, the proposed two-stage prediction model successfully addressed the problems of both determining the desired pump flow and optimal pump combination.     

An Optimization Model for Crucial Key Pipes and Mechanical Reliability: A Case Study on a Water Distribution System in Taiwan

This study develops a new method to calculate the water distribution system’s mechanical reliability and locates crucial pipes by using the minimum cut-sets method, exhaustive enumeration method and an optimal allocation model. First, a full domain optimization model was established to analyze the water distribution system. Next, the minimum cut-sets method and exhaustive enumeration method were used to discover the water distribution system’s key pipes. At last, water distribution system’s mechanical reliability can be calculated. The major contributions are that we can use optimal allocation model to locate crucial pipelines in the water distribution whose failure will severely impair the source-demand connectivity. Besides, we can calculate water distribution system’s mechanical reliability. Thus we can offer these results for hydraulic construction funds consulting. The developed methodology is applied to a water distribution system in Hsin-Chu, Taiwan.     

Study on the Trade-Off between Ecological Base Flow and Optimized Water Supply

The purpose of this study is proposing a methodology to determine the ecological base flow of a river. In the past in Taiwan, the ecological base flow was calculated according to the basin area, and the influence of the water supply was not considered. This study proposes a methodology combining the water distribution optimization model with the Riverine Habitat Simulation System model to demonstrate the impact on the aquatic habitat for indicator species with different ecological base flow settings. The most suitable value of the ecological base flow between the indicators of the Shortage Index and the average value of Percentage Usable Area is determined using trade-off analysis and the incremental method. The result shows for the indicator species in the study area, Tou-Qian River Basin in Northern Taiwan, the most appropriate ecological base flow is 3.75?m³/s and it??s the optimal use of the ecological base flow between the water supply and the ecology.     

Rainfall-Runoff Prediction Using Dynamic Typhoon Information and Surface Weather Characteristic Considering Monsoon Effects

Water Resources Management - In meteorology and engineering, the prediction of quantitative precipitation and streamflow during typhoon events is a vital research topic. In Southern Taiwan,...     

Application of Neural Networks and Optimization Model in Conjunctive Use of Surface Water and Groundwater

This study develops an optimization model for the large-scale conjunctive use of surface water and groundwater resources. The aim is to maximize public and irrigation water supplies subject to groundwater-level drawdown constraints. Linear programming is used to create the optimization model, which is formulated as a linear constrained objective function. An artificial neural network is trained by a flow modeling program at specific observation wells, and the network is then incorporated into the optimization model. The proposed methodology is applied to the Chou-Shui alluvial fan system, located in central Taiwan. People living in this region rely on large quantities of pumped water for their public and irrigation demands. This considerable dependency on groundwater has resulted in severe land subsidence in many coastal regions of the alluvial fan. Consequently, an efficient means of implementing large-scale conjunctive use of surface water and groundwater is needed to prevent further overuse of groundwater. Two different optimization scenarios are considered. The results given by the proposed model show that water-usage can be balanced with a stable groundwater level. Our findings may assist officials and researchers in establishing plans to alleviate land subsidence problems.     

Optimal Design of a Settling Basin for a Small-Scale Drainage Area

This paper develops a nonlinear programming model to optimally design a settling basin for a small-scale drainage area with a minimum total cost. It is assumed that the shape of the settling basin is rectangular parallelepiped, and it is connected to an open channel at both ends. Therefore, the decision variables include the scales of the settling basin (i.e., length, width, and height) and the scales of the channel (i.e., width and height). The design trap efficiency requirement, which must be greater than or equal to the required one of the considered watershed, makes up the main constraint. Other constraints consist of the upper and lower bounds of the decision variables, the equations for computing the trap efficiency, and the average flow velocity in the settling basin. The objective function is to minimize the total annual cost, which is the sum of the land, capital, and maintenance-operation cost. The developed model is solved by using a genetic algorithm. This model is applied to a subwatershed of the Wu-She Reservoir watershed in central Taiwan. The obtained results effectively demonstrate the applicability and practicability of the model.