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For the construction industry, timely and reliable information on current and future rain information is vital for enabling forecasters to make accurate and timely forecasts and to allow appropriate construction operations. Because construction works are often delayed during typhoons, a useful scheme for rain forecasts during typhoon periods is highly desirable. This study developed a regional extreme precipitation and construction suspension estimation system (REPCSES) for the construction industry to use when a structure is in the construction stage. The REPCSES has two major functions: a regional extreme precipitation estimation model (comprising Modules 1 and 2) and the construction suspension estimation model (Modules 3 and 4). Module 1 is a regional 1-h-ahead rainfall estimation model, which is used for estimating the hourly rainfall near the construction location. Module 2 is used for estimating the cumulative rainfall within 24 h. Module 3 is designed to plot a hyetograph using the results from Modules 1 and 2. Then, Module 4 determines whether the construction should be suspended according to the plots from Module 3. In addition, this study developed a deep convolutional neural network model for estimating extreme rainfall during a structure under construction, and the experimental area was Nantou County, Taiwan. The collected typhoons (i.e., Soulik, Trami, Kong-Rey, Matmo, Dujuan, and Nesat) affecting the study area occurred from 2013 to 2017. The results indicated that the integrated system could provide accurate estimations of whether work could proceed as well as the number of days that construction should be suspended for. [ABSTRACT FROM AUTHOR]

In meteorology and engineering, the prediction of quantitative precipitation and streamflow during typhoon events is a vital research topic. In Southern Taiwan, typhoons often occur in the summer. The interaction between the typhoon circulation and southwesterly monsoon flow frequently transports abundant moisture into Southern Taiwan leading to the substantial pouring rains. This study proposes a rainfall-runoff prediction methodology for addressing the complicated inflow forecasts of southwest monsoon rainfall during typhoons in the upper Tsengwen River in Southern Taiwan. This paper is novel in that it incorporates various data types (reservoir inflows, watershed rainfalls, typhoon information, and ground-weather characteristics) that were applied as model inputs. The most frequently used support vector regressions were employed to construct the rainfall-runoff models on the basis of three designed data combination scenarios. Typhoons Kalmaegi (2008), Fung-wong (2008), Jangmi (2008), and Morakot (2009) were used as validation typhoons. The model cases, involving lead times of 1 h to 6 h, were evaluated. Six performance criteria were used in the three scenarios to highlight the scenario capable of identifying the optimal performance level. In addition, this study compared the error rates between accumulation observations and accumulation predictions. The results showed that Scenario 3, which considered typhoon information and ground-weather characteristics simultaneously, had superior watershed rainfall and runoff predictions to those of the other scenarios. Thus, this study demonstrated the feasibility of using the proposed methodology to increase the accuracy of rainfall-runoff predictions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper employed two classical, popular decision-tree algorithms (C5.0 and CART), and traditional Regression to deal with reservoir operations regarding decision of the releases from a reservoir system during floods. The experiment site was in Shihmen Reservoir, located in northern Taiwan. In a typical single-peak typhoon, the rules derived include two operational stages, the stage before peakflow (Stage I) and the stage after peakflow (Stage II). This study collected 50 typhoons (1987-2009). Four cases are designed, that are discretized class labels (target fields) are run by C5.0 and CART (i.e., Cases 1 and 2, respectively), while numeric class labels are run by CART and Regression (i.e., Cases 3 and 4, respectively). The criteria of root mean square error (RMSE), coefficient of efficiency (CE), and relative error of peak discharge (EQ) were used to evaluate the forecasts. Results showed that the decision trees are skillful in the prediction of reservoir releases in the studied site. Furthermore, it was found that CART regression trees with numeric targets are more appropriate and precise than C5.0 classification trees and Regression for the prediction of releases. [ABSTRACT FROM AUTHOR]

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 m/s and it's the optimal use of the ecological base flow between the water supply and the ecology. [ABSTRACT FROM AUTHOR]

The wet and dry seasons are distinctive in Taiwan as the amount of precipitation in wet seasons accounts for over three-fourth of the total rainfall. And the water-resources management relies pretty much on the rainfall brought in by typhoons as it accounts for a significant portion of the precipitation during wet seasons. Furthermore, as the storage of reservoirs is limited due to topographical factors, the management of typhoon rainfall has always been an important issue in Taiwan. The technique of decision-tree analysis is applied in this article to determine the optimal reservoir release in advance upon the issuance of a typhoon warning by the Central Weather Bureau (CWB), and the proposed methodology may provide solution to the trade-off judgment of reservoir operations between flood control and water supply according to economic efficiency. In this article, the economic loss functions of flooding damage and water-supply shortage are assumed in linear and nonlinear conditions, and the respective expected optimal releases based on the predicted precipitation as issued by CWB are derived. The proposed methodology has been applied to the Shihmen Reservoir System, and the capabilities of the model as an aid to real-time decision-making as well as the evaluation of the economic worth of forecasts is presented. [ABSTRACT FROM AUTHOR]