Modeling regional-scale groundwater arsenic hazard in the transboundary Ganges River Delta, India and Bangladesh: Infusing physically-based model with machine learning

2020 ◽  
Vol 748 ◽  
pp. 141107 ◽  
Author(s):  
Madhumita Chakraborty ◽  
Soumyajit Sarkar ◽  
Abhijit Mukherjee ◽  
Mohammad Shamsudduha ◽  
Kazi Matin Ahmed ◽  
...  
Keyword(s):  
Machine Learning ◽  
Regional Scale ◽  
River Delta ◽  
Ganges River ◽  
Physically Based Model ◽  
Groundwater Arsenic ◽  
Physically Based

scholarly journals Evaluation of Direct Horizontal Irradiance in China Using a Physically-Based Model and Machine Learning Methods

Energies ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 150 ◽  
Author(s):  
Feiyan Chen ◽  
Zhigao Zhou ◽  
Aiwen Lin ◽  
Jiqiang Niu ◽  
Wenmin Qin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Spatiotemporal Variation ◽  
Multivariate Adaptive Regression Splines ◽  
Accurate Estimation ◽  
The Tibetan Plateau ◽  
Learning Methods ◽  
Physically Based Model ◽  
Machine Learning Methods ◽  
Physically Based

Accurate estimation of direct horizontal irradiance (DHI) is a prerequisite for the design and location of concentrated solar power thermal systems. Previous studies have shown that DHI observation stations are too sparsely distributed to meet requirements, as a result of the high construction and maintenance costs of observation platforms. Satellite retrieval and reanalysis have been widely used for estimating DHI, but their accuracy needs to be further improved. In addition, numerous modelling techniques have been used for this purpose worldwide. In this study, we apply five machine learning methods: back propagation neural networks (BP), general regression neural networks (GRNN), genetic algorithm (Genetic), M5 model tree (M5Tree), multivariate adaptive regression splines (MARS); and a physically based model, Yang’s hybrid model (YHM). Daily meteorological variables, including air temperature (T), relative humidity (RH), surface pressure (SP), and sunshine duration (SD) were obtained from 839 China Meteorological Administration (CMA) stations in different climatic zones across China and were used as data inputs for the six models. DHI observations at 16 CMA radiation stations were used to validate their accuracy. The results indicate that the capability of M5Tree was superior to BP, GRNN, Genetic, MARS and YHM, with the lowest values of daily root mean square (RMSE) of 1.989 MJ m−2day−1, and the highest correlation coefficient (R = 0.956), respectively. Then, monthly and annual mean DHI during 1960–2016 were calculated to reveal the spatiotemporal variation of DHI across China, using daily meteorological data based on the M5tree model. The results indicated a significantly decreasing trend with a rate of −0.019 MJ m−2during 1960–2016, and the monthly and annual DHI values of the Tibetan Plateau are the highest, while whereas the lowest values occur in the southeastern part of the Yunnan−Guizhou Plateau, the Sichuan Basin and most of the southern Yangtze River Basin. The possible causes for spatiotemporal variation of DHI across China were investigated by discussing cloud and aerosol loading.

scholarly journals THE FRAMEWORK ON MULTI-SCALE LANDSLIDE HAZARD EVALUATION IN CHINA

2016 ◽  
Vol XLI-B8 ◽  
pp. 105-107 ◽  
Author(s):  
W. Y. Li ◽  
C. Liu ◽  
J. Gao
Keyword(s):  
Hot Spot ◽  
Regional Scale ◽  
Landslide Hazard ◽  
Physical Models ◽  
Slope Instability ◽  
Hazard Evaluation ◽  
Landslide Risk ◽  
Physically Based Model ◽  
Multi Scale ◽  
Physically Based

Nowadays, Landslide has been one of the most frequent and seriously widespread natural hazards all over the world. How landslides can be monitored and predicted is an urgent research topic of the international landslide research community. Particularly, there is a lack of high quality and updated landslide risk maps and guidelines that can be employed to better mitigate and prevent landslide disasters in many emerging regions, including China. This paper considers national and regional scale, and introduces the framework on combining the empirical and physical models for landslide evaluation. Firstly, landslide susceptibility in national scale is mapped based on empirical model, and indicates the hot-spot areas. Secondly, the physically based model can indicate the process of slope instability in the hot-spot areas. The result proves that the framework is a systematic method on landslide hazard monitoring and early warning.

scholarly journals A Regional Scale Investigation on Groundwater Arsenic in Different Types of Aquifers in the Pearl River Delta, China

Geofluids ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Qinxuan Hou ◽  
Jichao Sun ◽  
Jihong Jing ◽  
Chunyan Liu ◽  
Ying Zhang ◽  
...  
Keyword(s):  
Pearl River Delta ◽  
Regional Scale ◽  
River Delta ◽  
Pearl River ◽  
Karst Aquifers ◽  
The Pearl River Delta ◽  
Groundwater Arsenic ◽  
Different Types ◽  
High Concentrations ◽  
The Pearl River

Nearly 400 groundwater samples were collected from different types of aquifers in the Pearl River Delta (PRD), and the concentrations of groundwater arsenic (As) and other 22 hydrochemical parameters in different types of aquifers were then investigated. Results showed that groundwater As concentration was up to hundreds μg/L in granular aquifers, while those in fissured aquifers and karst aquifers were only up to dozens and several μg/L, respectively. Correspondingly, about 9.4% and 2.3% samples with high concentrations (>0.01 mg/L) of As were in granular and fissured aquifers, respectively, but no samples with high concentration of As were in karst aquifers. The source and mobilization of groundwater As in granular aquifers are likely controlled by the following mechanism: organic matter in marine strata was mineralized and provided electrons for electron acceptors, resulting in the release of NH4+ and I- and the reduction of Fe/Mn and NO3-, and was accompanied with the mobilization of As from sediments into groundwater. By contrast, both natural processes including the competitive adsorption between As anions and F-/PO43-/HCO3- and anthropogenic processes including industrialization were responsible for high concentrations of groundwater As in fissured aquifers.

scholarly journals THE FRAMEWORK ON MULTI-SCALE LANDSLIDE HAZARD EVALUATION IN CHINA

2016 ◽  
Vol XLI-B8 ◽  
pp. 105-107 ◽  
Author(s):  
W. Y. Li ◽  
C. Liu ◽  
J. Gao
Keyword(s):  
Hot Spot ◽  
Regional Scale ◽  
Landslide Hazard ◽  
Physical Models ◽  
Slope Instability ◽  
Hazard Evaluation ◽  
Landslide Risk ◽  
Physically Based Model ◽  
Multi Scale ◽  
Physically Based

Nowadays, Landslide has been one of the most frequent and seriously widespread natural hazards all over the world. How landslides can be monitored and predicted is an urgent research topic of the international landslide research community. Particularly, there is a lack of high quality and updated landslide risk maps and guidelines that can be employed to better mitigate and prevent landslide disasters in many emerging regions, including China. This paper considers national and regional scale, and introduces the framework on combining the empirical and physical models for landslide evaluation. Firstly, landslide susceptibility in national scale is mapped based on empirical model, and indicates the hot-spot areas. Secondly, the physically based model can indicate the process of slope instability in the hot-spot areas. The result proves that the framework is a systematic method on landslide hazard monitoring and early warning.

Fast physically-based model for rainfall-induced landslide susceptibility assessment at regional scale

CATENA ◽  
2021 ◽  
Vol 201 ◽  
pp. 105213
Author(s):  
Vicente Medina ◽  
Marcel Hürlimann ◽  
Zizheng Guo ◽  
Antonio Lloret ◽  
Jean Vaunat
Keyword(s):  
Landslide Susceptibility ◽  
Regional Scale ◽  
Susceptibility Assessment ◽  
Physically Based Model ◽  
Landslide Susceptibility Assessment ◽  
Physically Based

scholarly journals From slope- to regional-scale shallow landslides susceptibility assessment using TRIGRS

2014 ◽  
Vol 2 (12) ◽  
pp. 7409-7464 ◽  
Author(s):  
M. Bordoni ◽  
C. Meisina ◽  
R. Valentino ◽  
M. Bittelli ◽  
S. Chersich
Keyword(s):  
Regional Scale ◽  
Field Measurements ◽  
Shallow Landslides ◽  
Susceptibility Assessment ◽  
Physically Based Model ◽  
Distributed Models ◽  
Landslides Susceptibility ◽  
Physically Based ◽  
Monitoring Activity

Abstract. Rainfall-induced shallow landslides are common phenomena in many parts of the world, affecting cultivations and infrastructures and causing sometimes human losses. Assessing the shallow landslides susceptibility is fundamental for land planning at different scales. This work defines a reliable methodology to extend the slope stability analysis from the local to the regional scale by using a well established physically-based model (TRIGRS-Unsaturated). The model is applied at first for a sample slope and then to the surrounding area of 13.4 km2 in Oltrepo Pavese (Northern Italy). In order to obtain more reliable input data for the model, a long-term hydro-meteorological monitoring has been carried out at the sample slope, that has been assumed as representative of the study area. Field measurements allowed for identifying the triggering mechanism of shallow failures and were used to calibrate the model. After obtaining modelled pore water pressures at the slope scale consistent with those measured during the monitoring activity, more reliable trends have been modelled also for past landslide events, as the April 2009 event that has been assumed as benchmark. The shallow landslides susceptibility assessment obtained using TRIGRS-Unsaturated for the benchmark event appears good for both the monitored slope and the whole study area, with better results if a pedological instead of geological zoning is considered at regional scale. The scheme followed in this work allows for obtaining better results of shallow landslides susceptibility assessment in terms of reduction of overestimation of unstable areas with respect to other distributed models applied in the past.

scholarly journals Application of a physically based model to forecast shallow landslides at a regional scale

2018 ◽  
Vol 18 (7) ◽  
pp. 1919-1935 ◽  
Author(s):  
Teresa Salvatici ◽  
Veronica Tofani ◽  
Guglielmo Rossi ◽  
Michele D'Ambrosio ◽  
Carlo Tacconi Stefanelli ◽  
...  
Keyword(s):  
Slope Stability ◽  
Regional Scale ◽  
Shallow Landslide ◽  
Shallow Landslides ◽  
Richards Equation ◽  
Mean Annual Precipitation ◽  
Good Prediction ◽  
Physically Based Model ◽  
Good Prediction Accuracy ◽  
Physically Based

Abstract. In this work, we apply a physically based model, namely the HIRESSS (HIgh REsolution Slope Stability Simulator) model, to forecast the occurrence of shallow landslides at the regional scale. HIRESSS is a physically based distributed slope stability simulator for analyzing shallow landslide triggering conditions during a rainfall event. The modeling software is made up of two parts: hydrological and geotechnical. The hydrological model is based on an analytical solution from an approximated form of the Richards equation, while the geotechnical stability model is based on an infinite slope model that takes the unsaturated soil condition into account. The test area is a portion of the Aosta Valley region, located in the northwest of the Alpine mountain chain. The geomorphology of the region is characterized by steep slopes with elevations ranging from 400 m a.s.l. on the Dora Baltea River's floodplain to 4810 m a.s.l. at Mont Blanc. In the study area, the mean annual precipitation is about 800–900 mm. These features make the territory very prone to landslides, mainly shallow rapid landslides and rockfalls. In order to apply the model and to increase its reliability, an in-depth study of the geotechnical and hydrological properties of hillslopes controlling shallow landslide formation was conducted. In particular, two campaigns of on site measurements and laboratory experiments were performed using 12 survey points. The data collected contributed to the generation of an input map of parameters for the HIRESSS model. In order to consider the effect of vegetation on slope stability, the soil reinforcement due to the presence of roots was also taken into account; this was done based on vegetation maps and literature values of root cohesion. The model was applied using back analysis for two past events that affected the Aosta Valley region between 2008 and 2009, triggering several fast shallow landslides. The validation of the results, carried out using a database of past landslides, provided good results and a good prediction accuracy for the HIRESSS model from both a temporal and spatial point of view.

scholarly journals A new approach to mapping landslide hazards: a probabilistic integration of empirical and physically based models in the North Cascades of Washington, USA

2019 ◽  
Vol 19 (11) ◽  
pp. 2477-2495
Author(s):  
Ronda Strauch ◽  
Erkan Istanbulluoglu ◽  
Jon Riedel
Keyword(s):  
Statistical Approach ◽  
Joint Probability ◽  
North Cascades ◽  
Physically Based Model ◽  
New Approach ◽  
The North ◽  
Debris Avalanches ◽  
Landslide Hazards ◽  
Infinite Slope Stability Model ◽  
Physically Based

Abstract. We developed a new approach for mapping landslide hazards by combining probabilities of landslide impacts derived from a data-driven statistical approach and a physically based model of shallow landsliding. Our statistical approach integrates the influence of seven site attributes (SAs) on observed landslides using a frequency ratio (FR) method. Influential attributes and resulting susceptibility maps depend on the observations of landslides considered: all types of landslides, debris avalanches only, or source areas of debris avalanches. These observational datasets reflect the detection of different landslide processes or components, which relate to different landslide-inducing factors. For each landslide dataset, a stability index (SI) is calculated as a multiplicative result of the frequency ratios for all attributes and is mapped across our study domain in the North Cascades National Park Complex (NOCA), Washington, USA. A continuous function is developed to relate local SI values to landslide probability based on a ratio of landslide and non-landslide grid cells. The empirical model probability derived from the debris avalanche source area dataset is combined probabilistically with a previously developed physically based probabilistic model. A two-dimensional binning method employs empirical and physically based probabilities as indices and calculates a joint probability of landsliding at the intersections of probability bins. A ratio of the joint probability and the physically based model bin probability is used as a weight to adjust the original physically based probability at each grid cell given empirical evidence. The resulting integrated probability of landslide initiation hazard includes mechanisms not captured by the infinite-slope stability model alone. Improvements in distinguishing potentially unstable areas with the proposed integrated model are statistically quantified. We provide multiple landslide hazard maps that land managers can use for planning and decision-making, as well as for educating the public about hazards from landslides in this remote high-relief terrain.

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