Toshiharu Kojiri

Advance flood forecasting for flood stricken Bangladesh with a fuzzy reasoning method

An artificial Neural Network (NN) was successfully applied, in an earlier study, as a prediction tool to forecast water level at Dhaka (Bangladesh), for up to seven lead days in advance, with a high accuracy level. In addition, this high accuracy degree was accompanied with a very short computational time. Both make NN a desirable advance warming forecasting tool. In a later study, a sensitivity analysis was also performed to retain only the most sensitive gauging stations for the Dhaka station. The resulting reduction of gauging stations insignificantly affects the prediction accuracy level. The work concerning the possibility of measurement failure in any of the gauging stations during the critical flow level at Dhaka requires prediction tools which can interpret linguistic assessment of flow levels. A fuzzy logic approach is introduced with two or three membership functions, depending on necessity, for the input stations with five membership functions for the output station. Membership functions for each station are derived from their respective water level frequency distributions, after the Kohonen neural network is used to group the data into clusters. The proposed approach in deriving membership function shows a number of advances over the approach commonly used. When prediction results are compared with measured data, the prediction accuracy level is comparable with that of the data driven neural network approach. Copyright

Operation of storage reservoir for water quality by using optimization and artificial intelligence techniques

Water quantity and quality are considered to be the main driving forces of the reservoir operation. Barra Bonita reservoir, located in the southeast region of Brazil, is chosen as the case study for the application of the proposed methodology. Herein, optimization and artificial intelligence (AI) techniques are applied in the simulation and operation of the reservoir. A fuzzy stochastic dynamic programming model (FSDP) is developed for calculating the optimal operation procedures. Optimization is applied to achieve multiple fuzzy objectives. Markov chain technique is applied to handle the stochastic characteristics of river flow. Water quality analysis is carried out using an artificial neural network model. Organic matter and nutrient loads are modeled as a function of river discharge through the application of a fuzzy regression model based on fuzzy performance functions. The obtained results show that the proposed methodology provides an effective and useful tool for reservoir operation.

A physically based distributed hydrological model of wadi system to simulate flash floods in arid regions

Many hydrological approaches have been developed for humid areas; however, we still have many challenges regarding flash floods simulation in arid regions. Thus, the main purpose of this study is to develop a physically based hydrological model for flash floods simulation, to understand the hydrological processes, and to overcome the water relating problems, as water scarcity, data deficiency. In this study, Hydrological River Basin Environmental Assessment Model incorporating wadi system (Hydro-BEAM-WaS) integrated with both Geographic Information System (GIS) and remote sensing data is proposed. To overcome the lack of observational data, Global Satellite Mapping of Precipitation (GSMaP) data has been compared with the Global Precipitation Climatology Center (GPCC) data. We found that the GSMaP has an overestimated or underestimated systematic seasonal bias. Wadi El-Khoud in Oman was chosen for model calibration, while the River Nile basin in Egypt was chosen for flash flood simulations. The simulation has been successfully carried out where it exhibits a reasonable fit between both the simulated and observed results, in spite of the deficiency of high quality observations. This approach can be used to evaluate and simulate the wadi runoff behaviors, such as discontinuous surface flow. It also can help in estimating the initial and transmission losses. Flash flood water has been assessed and evaluated as new water resources, which would be properly utilized to overcome the problem of water shortage in such regions. The proposed model has been proven to provide reliable simulations of flash floods referring to ungauged wadi systems.

Development of a Decision Support for Integrated Water Management in River Basins

The application of computers for the planning and operation of water resource systems is a rapidly advancing field of research. In recent years, decision support system (DSS) has gained much attention in civil engineering, in which the output can be displayed in high quality and easy to be understood. In this study, the decision support system for integrated water management, CTIWM, is developed with particular reference to Chikugo River basin, a multipurpose multireservoir system in Japan. It uses a module library that contains compatible modules for simulating a variety of water and physio-chemical processes. Different kinds of numerical models may be invoked through user interface menu, which facilitates communications between users and models in a friendly way. It demonstrates that the integration of DSS technique, simulation and optimization models is an efficient way for water resources management.

Flood Management in Urban River Basins

A flood management system used for urban rivers in Japan is presented in this paper. The system is composed of three subsystems: on-line data collection subsystem which is used for collecting rainfall and water level data; flood prediction subsystem based on the previous 3-hour hydrologic data; and result display subsystem. The on-line data collection subsystem is used to collect necessary rainfall and water level data for flood prediction in short periods of 10 minutes. The flood prediction subsystem is composed of effective rainfall estimation, slope flow model, and channel flow model, which integrates the effect of land use, rainfall intensity, and the drainage facilities in the river basin. The purpose of the display subsystem is to provide necessary hydrologic data for all users, which is developed with the combination of the WWW (World Wide Web) explorer and may be linked with the Internet. This system was developed in 1998 and was used for practical flood prediction later. The functionalities of every subsystem were tested and the more efficient application will be expected.

Severe rainfall prediction method using artificial intelligence

A severe rainfall prediction method using artificial intelligence, which can forecast the time series variation and spatial distribution of severe rainfall is developed for supporting flood control management. This method is unprecedented in the sense that severe rainfall is predicted mainly based on the physical processes of cloud development, qualitative reasoning, and the model based reasoning.

Knowledge-Based System for Reservoir Operation during Low Flows Utilizing Weather Forecast Information

A model for long range and real time reservoir operations is developed, considering the medium and long range weather forecast provided by the meteorological agency. The reasoning employed by the reservoir operator to make the appropriate decision on the reservoir operations, in the presence of uncertainty and inevitable errors in the forecast, is modeled through a rule-based scheme. A fuzzy inference procedure is used to evaluate the rules and produce the control output.

Severe Rainfall Prediction Method Using Artificial Intelligence Based on the Knowledge of Convective Cloud Processes

The severe rainfall prediction method using artificial intelligence which simulates the act of weather forecast experts who are well versed in the local weather is proposed in order to support the flood control.Under the condition that the system does not take the place of the numerical weather forecast, the objectives of development of the system are as follows; i) to forecast severe rainfall in fine grid scale by consideration of subgrid phenomena which are difficult to be expressed in numerical models, and ii) to make a real-time explanation of the important causes of severe rainfall to river managers.Then, the subsystems of the SRAI are developed and discussed in terms of their performance. The accuracy of the system was 80% for forecast of two hours ahead rainfall over 10mm/hr. The system shows the important initial conditions for severe rainfall effectively using backward reasoning.