Multimodel combination techniques for analysis of hydrological simulations: Application to distributed model intercomparison project results

This paper examines several multimodel combination techniques that are used for streamflow forecasting: the simple model average (SMA), the multimodel superensemble (MMSE), modified multimodel superensemble (M3SE), and the weighted average method (WAM). These model combination techniques were evaluated using the results from the Distributed Model Intercomparison Project (DMIP), an international project sponsored by the National Weather Service (NWS) Office of Hydrologic Development (OHD). All of the multimodel combination results were obtained using uncalibrated DMIP model simulations and were compared against the best-uncalibrated as well as the best-calibrated individual model results. The purpose of this study is to understand how different combination techniques affect the accuracy levels of the multimodel simulations. This study revealed that the multimodel simulations obtained from uncalibrated single-model simulations are generally better than any single-member model simulations, even the best-calibrated single-model simulations. Furthermore, more sophisticated multimodel combination techniques that incorporated bias correction step work better than simple multimodel average simulations or multimodel simulations without bias correction. © 2006 American Meteorological Society

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

A new particle swarm optimization algorithm for neural network optimization

This paper presents a new particle swarm optimization (PSO) algorithm for tuning parameters (weights) of neural networks. The new PSO algorithm is called fuzzy logic-based particle swarm optimization with cross-mutated operation (FPSOCM), where the fuzzy inference system is applied to determine the inertia weight of PSO and the control parameter of the proposed cross-mutated operation by using human knowledge. By introducing the fuzzy system, the value of the inertia weight becomes variable. The cross-mutated operation is effectively force the solution to escape the local optimum. Tuning parameters (weights) of neural networks is presented using the FPSOCM. Numerical example of neural network is given to illustrate that the performance of the FPSOCM is good for tuning the parameters (weights) of neural networks

Simulating California reservoir operation using the classification and regression-tree algorithm combined with a shuffled cross-validation scheme

The controlled outflows from a reservoir or dam are highly dependent on the decisions made by the reservoir operators, instead of a natural hydrological process. Difference exists between the natural upstream inflows to reservoirs and the controlled outflows from reservoirs that supply the downstream users. With the decision maker's awareness of changing climate, reservoir management requires adaptable means to incorporate more information into decision making, such as water delivery requirement, environmental constraints, dry/wet conditions, etc. In this paper, a robust reservoir outflow simulation model is presented, which incorporates one of the well-developed data-mining models (Classification and Regression Tree) to predict the complicated human-controlled reservoir outflows and extract the reservoir operation patterns. A shuffled cross-validation approach is further implemented to improve CART's predictive performance. An application study of nine major reservoirs in California is carried out. Results produced by the enhanced CART, original CART, and random forest are compared with observation. The statistical measurements show that the enhanced CART and random forest overperform the CART control run in general, and the enhanced CART algorithm gives a better predictive performance over random forest in simulating the peak flows. The results also show that the proposed model is able to consistently and reasonably predict the expert release decisions. Experiments indicate that the release operation in the Oroville Lake is significantly dominated by SWP allocation amount and reservoirs with low elevation are more sensitive to inflow amount than others

Bayesian model averaging with fixed and flexible priors: theory, concepts, and calibration experiments for rainfall-runoff modeling

This paper introduces for the first time the concept of Bayesian Model Averaging (BMA) with multiple prior structures, for rainfall‐runoff modeling applications. The original BMA model proposed by Raftery et al. (2005) assumes that the prior probability density function (pdf) is adequately described by a mixture of Gamma and Gaussian distributions. Here we discuss the advantages of using BMA with fixed and flexible prior distributions. Uniform, Binomial, Binomial‐Beta, Benchmark, and Global Empirical Bayes priors along with Informative Prior Inclusion and Combined Prior Probabilities were applied to calibrate daily streamflow records of a coastal plain watershed in the South‐East USA. Various specifications for Zellner's g prior including Hyper, Fixed, and Empirical Bayes Local (EBL) g priors were also employed to account for the sensitivity of BMA and derive the conditional pdf of each constituent ensemble member. These priors were examined using the simulation results of conceptual and semi‐distributed rainfall‐runoff models. The hydrologic simulations were first coupled with a new sensitivity analysis model and a parameter uncertainty algorithm to assess the sensitivity and uncertainty associated with each model. BMA was then used to subsequently combine the simulations of the posterior pdf of each constituent hydrological model. Analysis suggests that a BMA based on combined fixed and flexible priors provides a coherent mechanism and promising results for calculating a weighted posterior probability compared to individual model calibration. Furthermore, the probability of Uniform and Informative Prior Inclusion priors received significantly lower predictive error whereas more uncertainty resulted from a fixed g prior (i.e. EBL)

An Optimal Game Theoretical Framework for Mobility Aware Routing in Mobile Ad hoc Networks

Selfish behaviors are common in self-organized Mobile Ad hoc Networks (MANETs) where nodes belong to different authorities. Since cooperation of nodes is essential for routing protocols, various methods have been proposed to stimulate cooperation among selfish nodes. In order to provide sufficient incentives, most of these methods pay nodes a premium over their actual costs of participation. However, they lead to considerably large overpayments. Moreover, existing methods ignore mobility of nodes, for simplicity. However, owing to the mobile nature of MANETs, this assumption seems unrealistic. In this paper, we propose an optimal game theoretical framework to ensure the proper cooperation in mobility aware routing for MANETs. The proposed method is based on the multi-dimensional optimal auctions which allows us to consider path durations, in addition to the route costs. Path duration is a metric that best reflects changes in topology caused by mobility of nodes and, it is widely used in mobility aware routing protocols. Furthermore, the proposed mechanism is optimal in that it minimizes the total expected payments. We provide theoretical analysis to support our claims. In addition, simulation results show significant improvements in terms of payments compared to the most popular existing methods

Machine Learning for High-entropy Alloys: Progress, Challenges and Opportunities

High-entropy alloys (HEAs) have attracted extensive interest due to their exceptional mechanical properties and the vast compositional space for new HEAs. However, understanding their novel physical mechanisms and then using these mechanisms to design new HEAs are confronted with their high-dimensional chemical complexity, which presents unique challenges to (i) the theoretical modeling that needs accurate atomic interactions for atomistic simulations and (ii) constructing reliable macro-scale models for high-throughput screening of vast amounts of candidate alloys. Machine learning (ML) sheds light on these problems with its capability to represent extremely complex relations. This review highlights the success and promising future of utilizing ML to overcome these challenges. We first introduce the basics of ML algorithms and application scenarios. We then summarize the state-of-the-art ML models describing atomic interactions and atomistic simulations of thermodynamic and mechanical properties. Special attention is paid to phase predictions, planar-defect calculations, and plastic deformation simulations. Next, we review ML models for macro-scale properties, such as lattice structures, phase formations, and mechanical properties. Examples of machine-learned phase-formation rules and order parameters are used to illustrate the workflow. Finally, we discuss the remaining challenges and present an outlook of research directions, including uncertainty quantification and ML-guided inverse materials design.Comment: This review paper has been accepted by Progress in Materials Scienc

Spatiotemporal rainfall forecasting models for agricultural management

The main aim of the current PhD thesis is to develop forecast systems for Australia over medium time scales such as weekly, monthly, seasonal and annual for Agricultural planning. Common data driven algorithms in hydrology and climate studies including statistical methods, Artificial Intelligent (AI), machine learning and data mining techniques are sought to improve the rainfall prediction using historical data from land and oceans. First, spatiotemporal monthly rainfall forecasting is developed for south-eastern and eastern Australia using climatic and non-climatic variables. To improve model performance, climate regionalization and regionalization of the climate drivers are considered as initial steps for Neural Network model. The outcome of this study indicates that climate regionalization can improve performance of space-time prediction model for monthly rainfall in eastern and south-eastern Australia. The second part of the study investigates the stability and reliability of the lagged relationship between climate drivers and leading modes of seasonal rainfall in south-eastern Australia. Strength and polarity of correlation between climatic indices and leading mode of seasonal rainfall vary in different seasons and over time. This suggests using suitable lagged climatic indices rather than fixed climatic indices for each season leads to better rainfall predictions. Finally, annual rainfall, using Gene Expression Programming (GEP) method, significant predictors that were identified are Geographic Information System (GIS) variables, long-term mean and median annual rainfall, seasonal rainfall, previous annual rainfall and lagged climatic indices. The results indicate that the best predictors for modelling Australian annual rainfall in space-time are climatology (median and mean of rainfall) in comparison with GIS variables

Current themes and recent advances in modelling species occurrences

Recent years have seen a huge expansion in the range of methods and approaches that are being used to predict species occurrences. This expansion has been accompanied by many improvements in statistical methods, including more accurate ways of comparing models, better null models, methods to cope with autocorrelation, and greater awareness of the importance of scale and prevalence. However, the field still suffers from problems with incorporating temporal variation, overfitted models and poor out-of-sample prediction, confusion between explanation and prediction, simplistic assumptions, and a focus on pattern over process. The greatest advances in recent years have come from integrative studies that have linked species occurrence models with other themes and topics in ecology, such as island biogeography, climate change, disease geography, and invasive species