Complementary Information for Reducing Parameter Uncertainty in Distributed Rainfall-Runoff Modeling

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Machine learning for high-resolution landslide susceptibility mapping: case study in Inje County, South Korea

Landslides are a major natural hazard that can significantly damage infrastructure and cause loss of life. In South Korea, the current landslide susceptibility mapping (LSM) approach is mainly based on statistical techniques (logistic regression (LR) analysis). According to previous studies, this method has achieved an accuracy of approximately 75.2%. In this paper, we expand upon this traditional approach by comparing the performance of six machine learning (ML) algorithms for LSM in Inje County, South Korea. The study employed a combination of geographical data gathered from 2005 to 2019 to train and evaluate six algorithms, including LR, Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest (RF), and Extreme Gradient Boosting (XGB). The effectiveness of these models was measured by various criteria, such as the percentage of correct classification (PCC) score, F1 score, and Kappa score. The results demonstrated that the PCC and F1 scores of the six models fell between [0.869–0.941] and [0.857–0.940], respectively. RF and XGB had the highest PCC and F1 scores of 0.939 and 0.941, respectively. This study indicates that ML can be a valuable technique for high-resolution LSM in South Korea instead of the current approach

Human malarial disease: a consequence of inflammatory cytokine release

Malaria causes an acute systemic human disease that bears many similarities, both clinically and mechanistically, to those caused by bacteria, rickettsia, and viruses. Over the past few decades, a literature has emerged that argues for most of the pathology seen in all of these infectious diseases being explained by activation of the inflammatory system, with the balance between the pro and anti-inflammatory cytokines being tipped towards the onset of systemic inflammation. Although not often expressed in energy terms, there is, when reduced to biochemical essentials, wide agreement that infection with falciparum malaria is often fatal because mitochondria are unable to generate enough ATP to maintain normal cellular function. Most, however, would contend that this largely occurs because sequestered parasitized red cells prevent sufficient oxygen getting to where it is needed. This review considers the evidence that an equally or more important way ATP deficency arises in malaria, as well as these other infectious diseases, is an inability of mitochondria, through the effects of inflammatory cytokines on their function, to utilise available oxygen. This activity of these cytokines, plus their capacity to control the pathways through which oxygen supply to mitochondria are restricted (particularly through directing sequestration and driving anaemia), combine to make falciparum malaria primarily an inflammatory cytokine-driven disease

コウウ リュウシュツ モデリング ニ オケル サマザマナ ヨウイン ニ ヨル ヨソク ノ フタシカサ ノ ヒョウカ

京都大学0048新制・課程博士博士(工学)甲第14152号工博第2986号新制||工||1443(附属図書館)UT51-2008-N469京都大学大学院工学研究科都市環境工学専攻(主査)教授 寶 馨, 教授 椎葉 充晴, 准教授 立川 康人学位規則第4条第1項該当Doctor of EngineeringKyoto UniversityDA

Analysis of Hydrologic Model Parameter Characteristics Using Automatic Global Optimization Method

水文モデルの精度を向上させ，信頼性を高めるためにはモデル同定の過程が重要となる。分布型流出モデルの場合は，試行錯誤的にパラメータを決定することが多いが，客観性を欠き，モデル同定に多くの時間を要する。これに対して，自動キャリブレーションはそれらの欠点を克服し，そうした手法の一つであるshuffled complex evolution (SCE)は広域のパラメータ同定のための最適化アルゴリズムを実現している。本研究では，構造の異なる2つの水文モデルのパラメータ同定にSCEを適用し，異なる目的関数ごとに同定されるパラメータがどのような値を取るかを分析する。また，規模の異なる洪水によって求められたパラメータ値の持つ不確実性が，流出予測シミュレーション結果に及ぼす影響を分析する。さらに流出モデルの安定性を評価する指標を示し，その指標を用いて流出モデルの性能を分析する．上椎葉流域(211km2)を対象にこれらの分析を実施した。The successful application of hydrologic models depends on how well the models are calibrated. Therefore, the calibration procedure should be performed prudently to improve model accuracy and maximize model reliability before making decision of an intended purpose using a hydrologic model. Despite frequent utilization of manual calibration especially for distributed hydrologic models, much more weakness still remains with respect to the absence of generally accepted objective measures and extreme time consuming. Automatic calibration can overcome these kinds of shortcomings. A global optimization algorithm entitled shuffled complex evolution (SCE) has been proved to be efficient and robust to find optimal parameters of hydrologic models. This study examines the applicability of global optimization scheme, SCE, for calibrating two hydrologic models which have different model structures and indicates variation of optimal parameters according to objective functions. We also analyze parameter transferability under various flood scale. At last, guideline indexes able to assess model stability are introduced to allow modelers to select a more stable and suitable hydrologic model. Above all procedures are applied to Kamishiiba catchment (211km2).水文モデルの精度を向上させ，信頼性を高めるためにはモデル同定の過程が重要となる。分布型流出モデルの場合は，試行錯誤的にパラメータを決定することが多いが，客観性を欠き，モデル同定に多くの時間を要する。これに対して，自動キャリブレーションはそれらの欠点を克服し，そうした手法の一つであるshuffled complex evolution (SCE)は広域のパラメータ同定のための最適化アルゴリズムを実現している。本研究では，構造の異なる2つの水文モデルのパラメータ同定にSCEを適用し，異なる目的関数ごとに同定されるパラメータがどのような値を取るかを分析する。また，規模の異なる洪水によって求められたパラメータ値の持つ不確実性が，流出予測シミュレーション結果に及ぼす影響を分析する。さらに流出モデルの安定性を評価する指標を示し，その指標を用いて流出モデルの性能を分析する．上椎葉流域(211km2)を対象にこれらの分析を実施した。The successful application of hydrologic models depends on how well the models are calibrated. Therefore, the calibration procedure should be performed prudently to improve model accuracy and maximize model reliability before making decision of an intended purpose using a hydrologic model. Despite frequent utilization of manual calibration especially for distributed hydrologic models, much more weakness still remains with respect to the absence of generally accepted objective measures and extreme time consuming. Automatic calibration can overcome these kinds of shortcomings. A global optimization algorithm entitled shuffled complex evolution (SCE) has been proved to be efficient and robust to find optimal parameters of hydrologic models. This study examines the applicability of global optimization scheme, SCE, for calibrating two hydrologic models which have different model structures and indicates variation of optimal parameters according to objective functions. We also analyze parameter transferability under various flood scale. At last, guideline indexes able to assess model stability are introduced to allow modelers to select a more stable and suitable hydrologic model. Above all procedures are applied to Kamishiiba catchment (211km2)

分布型降雨流出モデルにおけるパラメータの不確実性の定量化

一般的に水文モデルには直接計測することのできないモデルパラメータが含まれ，それらの値は過去の水文データに適合するように決定される。これまで数十年に渡って，モデルパラメータの自動推定に関する研究が多数なされてきたが，それらの手法はパラメータ推定に伴う不確かを考慮できないという欠点を有する。本研究では，分布型降雨流出モデルKsedgeFC2Dのモデルパラメータ推定について，パラメータ推定の不確かさとその河川流量予測への影響を，上椎葉流域(211㎢)を対象に分析する。パラメータの不確かさが予測値にどのように伝達するかを分析するために，Shuffled Complex Evolution Metropolis アルゴリズム(SCEM-UA)を採用した。SCEM-UAを用いることにより，効率的にモデルパラメータの事後分布が得られることがわかった。また，SCEM-UAによって求められたパラメータの値と3つの異なる目的関数を設定したSCE-UA法によって得た値とを比較し，SCEM-UAの適用性を検討した。In general, hydrological models have several (or a lot of) parameters that cannot be directly measured, which only are inferred by calibration procedure against a historical input-output data record. While the applications of automatic parameter estimation techniques have received considerable attention over the last decades, such classical methods have received criticism for their lack of rigor in handling with uncertainty in the parameter estimates. This work addresses the calibration of the distributed rainfall-runoff model KsEdgeFC2D, the quantification of parameter uncertainty and its effect on the prediction of streamflow for Kamishiiba catchment (211km2). In this study, to analyze the propagation of parameter uncertainty into prediction, we employ the Shuffled Complex Evolution Metropolis (SCEM-UA) global optimization algorithm. Moreover, we compare SCEM-UA derived optimal parameter values to those estimated using deterministic SCE-UA method with three different objective functions to account for the structural stability of KsEdgFC2D model and to demonstrate the capability of the SCEM-UA algorithm to efficiently evolve to parameter posterior distribution.一般的に水文モデルには直接計測することのできないモデルパラメータが含まれ，それらの値は過去の水文データに適合するように決定される。これまで数十年に渡って，モデルパラメータの自動推定に関する研究が多数なされてきたが，それらの手法はパラメータ推定に伴う不確かを考慮できないという欠点を有する。本研究では，分布型降雨流出モデルKsedgeFC2Dのモデルパラメータ推定について，パラメータ推定の不確かさとその河川流量予測への影響を，上椎葉流域(211㎢)を対象に分析する。パラメータの不確かさが予測値にどのように伝達するかを分析するために，Shuffled Complex Evolution Metropolis アルゴリズム(SCEM-UA)を採用した。SCEM-UAを用いることにより，効率的にモデルパラメータの事後分布が得られることがわかった。また，SCEM-UAによって求められたパラメータの値と3つの異なる目的関数を設定したSCE-UA法によって得た値とを比較し，SCEM-UAの適用性を検討した。In general, hydrological models have several (or a lot of) parameters that cannot be directly measured, which only are inferred by calibration procedure against a historical input-output data record. While the applications of automatic parameter estimation techniques have received considerable attention over the last decades, such classical methods have received criticism for their lack of rigor in handling with uncertainty in the parameter estimates. This work addresses the calibration of the distributed rainfall-runoff model KsEdgeFC2D, the quantification of parameter uncertainty and its effect on the prediction of streamflow for Kamishiiba catchment (211km2). In this study, to analyze the propagation of parameter uncertainty into prediction, we employ the Shuffled Complex Evolution Metropolis (SCEM-UA) global optimization algorithm. Moreover, we compare SCEM-UA derived optimal parameter values to those estimated using deterministic SCE-UA method with three different objective functions to account for the structural stability of KsEdgFC2D model and to demonstrate the capability of the SCEM-UA algorithm to efficiently evolve to parameter posterior distribution

降雨流出モデリングにおける空間スケール依存性のもとでの水文予測の不確実性の評価

本研究では，降雨流出モデリングにおけるスケール依存性のもとでのモデルパラメータと入力に起因する水文予測の不確実性を分析することを目的とする。さらに，水文モデリングにおけるガイドラインを提供するために，水文モデリングの過程で含まれる不確かさの要素を考慮した新しい降雨流出モデルの枠組みを示す。This paper aims at investigating prediction uncertainty due to parameter and input under scale-dependant condition of rainfall-runoff modeling. Moreover, a new rainfall-runoff modeling framework considering uncertainty components involved in modeling processes is proposed to provide guideline for future modeling direction.本研究では，降雨流出モデリングにおけるスケール依存性のもとでのモデルパラメータと入力に起因する水文予測の不確実性を分析することを目的とする。さらに，水文モデリングにおけるガイドラインを提供するために，水文モデリングの過程で含まれる不確かさの要素を考慮した新しい降雨流出モデルの枠組みを示す。This paper aims at investigating prediction uncertainty due to parameter and input under scale-dependant condition of rainfall-runoff modeling. Moreover, a new rainfall-runoff modeling framework considering uncertainty components involved in modeling processes is proposed to provide guideline for future modeling direction

A Novel Framework for Correcting Satellite-Based Precipitation Products for Watersheds with Discontinuous Observed Data, Case Study in Mekong River Basin

Satellite-based precipitation (SP) data are gaining scientific interest due to their advantage in producing high-resolution products with quasi-global coverage. However, since the major reliance of precipitation data is on the distinctive geographical features of each location, they remain at a considerable distance from station-based data. This paper examines the effectiveness of a convolutional autoencoder (CAE) architecture in pixel-by-pixel bias correction of SP products for the Mekong River Basin (MRB). Two satellite-based products (TRMM and PERSIANN-CDR) and a gauge-based product (APHRODITE) are gridded rainfall products mined in this experiment. According to the estimated statistical criteria, the CAE model was effective in reducing the gap between SP products and benchmark data both in terms of spatial and temporal correlations. The two corrected SP products (CAE_TRMM and CAE_CDR) performed competitively, with CAE TRMM appearing to have a slight advantage over CAE CDR, however, the difference was minor. This study’s findings proved the effectiveness of deep learning-based models (here CAE) for bias correction of SP products. We believe that this technique will be a feasible alternative for delivering an up-to-current and reliable dataset for MRB studies, given that the sole available gauge-based dataset for this area has been out of date for a long time

Application of Long Short-Term Memory (LSTM) Neural Network for Flood Forecasting

Flood forecasting is an essential requirement in integrated water resource management. This paper suggests a Long Short-Term Memory (LSTM) neural network model for flood forecasting, where the daily discharge and rainfall were used as input data. Moreover, characteristics of the data sets which may influence the model performance were also of interest. As a result, the Da River basin in Vietnam was chosen and two different combinations of input data sets from before 1985 (when the Hoa Binh dam was built) were used for one-day, two-day, and three-day flowrate forecasting ahead at Hoa Binh Station. The predictive ability of the model is quite impressive: The Nash–Sutcliffe efficiency (NSE) reached 99%, 95%, and 87% corresponding to three forecasting cases, respectively. The findings of this study suggest a viable option for flood forecasting on the Da River in Vietnam, where the river basin stretches between many countries and downstream flows (Vietnam) may fluctuate suddenly due to flood discharge from upstream hydroelectric reservoirs