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Chapter 2 presents NN basics such as network architectures, learning considerations, and commonly used NN models, e. Anyone beginning to learn how to develop an NN model can go to chapter 3, which presents the NN model development as a six-stage process with the help of a case study.
- Neural Networks for Hydrological Modeling.
- Aînés et les Cadets (Les) (Sciences Humaines) (French Edition).
- Deadly Reigns I;
An important feature of the book is that it covers application of different types of neural networks in a wide variety of hydrological processes. River flow modeling using hybrid neural networks is presented in chapter 4, wherein broader issues encountered in practice are discussed with focus on training issues and uncertainties in modeling. Chapter 5 presents application of TDNNs to river level forecasting; chapter 6 discusses application of CCNNs to river flow forecasting; and chapter 7 describes the use of PRNNs for modeling of autoregressive dynamic hydrologic systems.
Chapter 9 includes a state-of-the-art on rainfall runoff modeling using neural networks and fundamental issues on rainfall runoff relationships. Chapter 10 presents a neural network application for rainfall forecasts in an urban environmental for use in effective flood warning systems. Chapter 11 covers an overview of the NN studies in the areas of water quality and ecological management in freshwater.
Chapter 12 discusses mechanisms of sediment supply and transfer in a catchment, erosion, and sediment yield assessment and presents some NN studies on the use of neural networks of sediment modeling. Chapter 13 describes the use of meteorological satellite image data in Nowcasting and Numerical Weather Prediction NWP and why neural networks have the potential to deal with the complex patterns present in such data in real-time operational forecasting applications.
Chapter 14 focuses on the potential of feed-forward neural networks as a tool for land cover mapping using supervised digital image classification of remotely sensed imagery. The final chapter argues that although neural network solutions have been found that are either superior or comparable to the process-based and other models in use by various water agencies, neural network solutions have not found favors with the administrators, water managers, and policymakers.
This chapter proposes a solution to this problem by way of a five-stage research agenda for neurohydrologists to pursue in the next decade. The stages considered include improvements of existing neural network models, comparison of neural network solutions with process-based modeling solutions, development of meaningful criteria for model evaluation, improvement of model understanding, and building dedicated hydrological neural network software packages.
The book is well organized; chapters are nicely written and easy to follow.
The book bridges the gap between research and application of NN hydrologic models. The length of the individual chapters seems to have been limited, probably because the book covers many applications in hydrology. This prevents the authors from dealing with the subject matter in greater detail. In this research, an ANN was developed and used to model the rainfall-runoff relationship, in a catchment located in a semiarid and Mediterranean climate in Algeria.
- Finding Daddy;
- Genetic Programming: Efficient Modeling Tool in Hydrology and Groundwater Management.
- X-Rays and Materials.
- "Development of a distributed artificial neural network for hydrologic " by Rebecca Logsdon.
- 1 INTRODUCTION.
- The Purpose of a Driven Life: How to be Driven and what the Purpose of Life is.
The performance of the developed neural network-based model was compared against multiple linear regression-based models using the same observed data. It was found that the neural network model consistently gives superior predictions.
Based on the results of this research, artificial neural network modeling appears to be a promising technique for the prediction of flow for catchments in semi-arid and Mediterranean regions. Accordingly, the neural network method can be applied to various hydrological systems where other models may be inappropriate.