林野火災における大気・海洋変動指標と水文気象要因の定量的分析

Tohoku University博士（工学）thesi

A Hybrid Wavelet de-noising and Rank-Set Pair Analysis approach for forecasting hydro-meteorological time series

Accurate, fast forecasting of hydro-meteorological time series is presently a major challenge in drought and flood mitigation. This paper proposes a hybrid approach, wavelet de-noising (WD) and Rank-Set Pair Analysis (RSPA), that takes full advantage of a combination of the two approaches to improve forecasts of hydro-meteorological time series. WD allows decomposition and reconstruction of a time series by the wavelet transform, and hence separation of the noise from the original series. RSPA, a more reliable and efficient version of Set Pair Analysis, is integrated with WD to form the hybrid WD-RSPA approach. Two types of hydro-meteorological data sets with different characteristics and different levels of human influences at some representative stations are used to illustrate the WD-RSPA approach. The approach is also compared to three other generic methods: the conventional Auto Regressive Integrated Moving Average (ARIMA) method, Artificial Neural Networks (ANNs) (BP-error Back Propagation, MLP-Multilayer Perceptron and RBF-Radial Basis Function), and RSPA alone. Nine error metrics are used to evaluate the model performance. Compared to three other generic methods, the results generated by WD-REPA model presented invariably smaller error measures which means the forecasting capability of the WD-REPA model is better than other models. The results show that WD-RSPA is accurate, feasible, and effective. In particular, WD-RSPA is found to be the best among the various generic methods compared in this paper, even when the extreme events are included within a time series

Forecasting of Medium-term Rainfall Using Artificial Neural Networks: Case Studies from Eastern Australia

The advent of machine learning, of which artificial neural networks (ANN) are a component, has provided an opportunity for improved rainfall forecasts, which is of value for water infrastructure management, agriculture, mining and other industries. In this chapter, ANNs are shown to provide more skillful monthly rainfall forecasts for locations in south-eastern Queensland, Australia, for lead-times of 3–12 months. The skill of the forecasts from the ANNs is highest when the models are individually optimized for each month, and when longer-duration series are used as input. The ANN technique has application where there is temperature and rainfall data extending back at least 50 years. Such datasets exist for much of Europe and North America, though a review of the available literature indicates most research into the application of ANN has focused on China, India and Australia

Modeling and analysis of actual evapotranspiration using data driven and wavelet techniques

Large-scale mining practices have disturbed many natural watersheds in northern Alberta, Canada. To restore disturbed landscapes and ecosystems’ functions, reconstruction strategies have been adopted with the aim of establishing sustainable reclaimed lands. The success of the reconstruction process depends on the design of reconstruction strategies, which can be optimized by improving the understanding of the controlling hydrological processes in the reconstructed watersheds. Evapotranspiration is one of the important components of the hydrological cycle; its estimation and analysis are crucial for better assessment of the reconstructed landscape hydrology, and for more efficient design. The complexity of the evapotranspiration process and its variability in time and space has imposed some limitations on previously developed evapotranspiration estimation models. The vast majority of the available models estimate the rate of potential evapotranspiration, which occurs under unlimited water supply condition. However, the rate of actual evapotranspiration (AET) depends on the available soil moisture, which makes its physical modeling more complicated than the potential evapotranspiration. The main objective of this study is to estimate and analyze the AET process in a reconstructed landscape. Data driven techniques can model the process without having a complete understanding of its physics. In this study, three data driven models; genetic programming (GP), artificial neural networks (ANNs), and multilinear regression (MLR), were developed and compared for estimating the hourly eddy covariance (EC)-measured AET using meteorological variables. The AET was modeled as a function of five meteorological variables: net radiation (Rn), ground temperature (Tg), air temperature (Ta), relative humidity (RH), and wind speed (Ws) in a reconstructed landscape located in northern Alberta, Canada. Several ANN models were evaluated using two training algorithms of Levenberg-Marquardt and Bayesian regularization. The GP technique was employed to generate mathematical equations correlating AET to the five meteorological variables. Furthermore, the available data were statistically analyzed to obtain MLR models and to identify the meteorological variables that have significant effect on the evapotranspiration process. The utility of the investigated data driven models was also compared with that of HYDRUS-1D model, which is a physically based model that makes use of conventional Penman-Monteith (PM) method for the prediction of AET. HYDRUS-1D model was examined for estimating AET using meteorological variables, leaf area index, and soil moisture information. Furthermore, Wavelet analysis (WA), as a multiresolution signal processing tool, was examined to improve the understanding of the available time series temporal variations, through identifying the significant cyclic features, and to explore the possible correlation between AET and the meteorological signals. WA was used with the purpose of input determination of AET models, a priori. The results of this study indicated that all three proposed data driven models were able to approximate the AET reasonably well; however, GP and MLR models had better generalization ability than the ANN model. GP models demonstrated that the complex process of hourly AET can be efficiently modeled as simple semi-linear functions of few meteorological variables. The results of HYDRUS-1D model exhibited that a physically based model, such as HYDRUS-1D, might perform on par or even inferior to the data driven models in terms of the overall prediction accuracy. The developed equation-based models; GP and MLR, revealed the larger contribution of net radiation and ground temperature, compared to other variables, to the estimation of AET. It was also found that the interaction effects of meteorological variables are important for the AET modeling. The results of wavelet analysis demonstrated the presence of both small-scale (2 to 8 hours) and larger-scale (e.g. diurnal) cyclic features in most of the investigated time series. Larger-scale cyclic features were found to be the dominant source of temporal variations in the AET and most of the meteorological variables. The results of cross wavelet analysis indicated that the cause and effect relationship between AET and the meteorological variables might vary based on the time-scale of variation under consideration. At small time-scales, significant linear correlations were observed between AET and Rn, RH, and Ws time series, while at larger time-scales significant linear correlations were observed between AET and Rn, RH, Tg, and Ta time series

Climate change and water abstraction impacts on the long-term variability of water levels in Lake Bracciano (Central Italy): A Random Forest approach

Abstract Study Region Lake Bracciano has been historically used as a strategic water reservoir for the city of Rome (Italy) since ancient times. However, following the severe water crisis of 2017, water abstraction has been completely stopped. Study Focus The relative impact of the various drivers of change (climatological and management) on fluctuations in lake water level is not yet clear. To quantify this impact, we applied the Random Forest (RF) machine learning approach, taking advantage of a century of observations. New Hydrological Insights for the Region Since the late 1990s the monthly variation in lake water levels has doubled, as has variation in monthly abstraction. Increased variation in annual cumulated precipitation and a rise in mean air temperature have also been observed. The RF machine learning approach made it possible to confirm the marginal role of temperature, the increasing role of abstraction during the last two decades (from 24 % to 39 %), and the key role played by the increased precipitation variability. These results highlight the notable prediction and inference capabilities of RF in a complex and partially unknown hydrological context. We conclude by discussing the limits of this approach, which are mainly associated with its capacity to generates scenarios compared to physical based models

Selected data exploration methods in hydroclimatology

The volumes of climatological data are rapidly growing due to development of new acquisition platforms and advances in data storage technologies. Such advances provide new challenging problems for data analysis methods. As a result, there is an increasing interest in application and development of new machine learning and data mining methods in climatological data analysis. This dissertation contributes to the field of data analysis by evaluation of selected methods and developing new techniques with application to hydro-climatological datasets. It is shown that data pre-processing with the decimated wavelet discrete transform can cause false predictive accuracy in regression machine learning algorithms. A general result is obtained that a decimated wavelet discrete transform based on a pyramidal algorithm requires utilizing some future values of the time series concerned. When the discrete wavelet transform is utilized as a pre-processing step for forecasting the time series, the necessary independence of calibration and validation data is compromised. This in turn translates into over-optimistic forecasting accuracy or even giving the illusion of forecasting skill when there in none. The obtained result is general and has wide implications in any discipline where the discrete wavelet transform is utilised in forecasting frameworks. In addition, a general framework for creating simple predictive models is presented, based on LASSO regularised regression. The method is illustrated for time series modelling with external event forcing but the approach has general applicability. As a contribution to association discovery, two tests of bivariate association are developed. The first method is designed for detecting threshold-like associations in a scatter plot with particular reference to testing the significance of the extent of a data-sparse region within a scatter plot. The second method is a more general test of non-random associations. Both methods utilise significance testing based on randomizations. Finally, LASSO regularized regression is investigated as a tool for discovering informative large scale climatogical predictors of local hydrological processes. A cross-validation scheme is proposed, which is related to practical forecasts of the next time interval to come, while at the same time maximising use of available information. The proposed methodology was applied to a case study predicting next-season river discharges in the Upper Waitaki River in New Zealand. The proposed forecasting methodology and cross-validation frameworks are applicable for similar hydroclimatological forecasting situations. The physical aspect of this part of the study included discovering the influence of the Interdecadal Pacific Oscillation on winter discharges in the upper Waitaki catchment