Nien-Sheng Hsu

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.

Real-Time Forecast of Reservoir Inflow Hydrographs Incorporating Terrain and Monsoon Effects during Typhoon Invasion by Novel Intelligent Numerical-Statistic Impulse Techniques

AbstractThis study develops an original methodology for forecasting real-time reservoir inflow hydrographs during typhoons, taking advantage of meteoro-hydrological methods such as analysis of typhoon hydrographs, numerical typhoon track forecasts, statistic typhoon central impulse-based quantitative precipitation forecasts model based on a real-time revised approach (TCI-RTQPF), real-time recurrent learning neural network (RTRLNN), and adaptive network-based fuzzy inference system (ANFIS). To derive the inflow hydrograph induced by interaction between typhoon rain bands, terrain, and monsoons, the inventive novel ensemble numerical-statistic impulse techniques are employed. The inflow during peak flow, inflection, and direct runoff ending (DRE) periods (impulse signal) are used for the deriving process. The hydrograph analysis is used to examine the mechanism between typhoon center location, wind field, precipitation, and the inflow hydrograph, and to establish the evaluation methods. Additionally, a nov...

Using Artificial Intelligence to Retrieve the Optimal Parameters and Structures of Adaptive Network-Based Fuzzy Inference System for Typhoon Precipitation Forecast Modeling

This study aims to construct a typhoon precipitation forecast model providing forecasts one to six hours in advance using optimal model parameters and structures retrieved from a combination of the adaptive network-based fuzzy inference system (ANFIS) and artificial intelligence. To enhance the accuracy of the precipitation forecast, two structures were then used to establish the precipitation forecast model for a specific lead-time: a single-model structure and a dual-model hybrid structure where the forecast models of higher and lower precipitation were integrated. In order to rapidly, automatically, and accurately retrieve the optimal parameters and structures of the ANFIS-based precipitation forecast model, a tabu search was applied to identify the adjacent radius in subtractive clustering when constructing the ANFIS structure. The coupled structure was also employed to establish a precipitation forecast model across short and long lead-times in order to improve the accuracy of long-term precipitation forecasts. The study area is the Shimen Reservoir, and the analyzed period is from 2001 to 2009. Results showed that the optimal initial ANFIS parameters selected by the tabu search, combined with the dual-model hybrid method and the coupled structure, provided the favors in computation efficiency and high-reliability predictions in typhoon precipitation forecasts regarding short to long lead-time forecasting horizons.