Evaluation of the landslide susceptibility and its spatial difference in the whole Qinghai-Tibetan Plateau region by five learning algorithms

AbstractLandslides are considered as major natural hazards that cause enormous property damages and fatalities in Qinghai-Tibetan Plateau (QTP). In this article, we evaluated the landslide susceptibility, and its spatial differencing in the whole Qinghai-Tibetan Plateau region using five state-of-the-art learning algorithms; deep neural network (DNN), logistic regression (LR), Naïve Bayes (NB), random forest (RF), and support vector machine (SVM), differing from previous studies only in local areas of QTP. The 671 landslide events were considered, and thirteen landslide conditioning factors (LCFs) were derived for database generation, including annual rainfall, distance to drainage $${(\mathrm{Ds}}_{\mathrm{d}})$$ ( Ds d ) , distance to faults $${(\mathrm{Ds}}_{\mathrm{f}})$$ ( Ds f ) , drainage density ($${D}_{d})$$ D d ) , elevation (Elev), fault density $$({F}_{d})$$ ( F d ) , lithology, normalized difference vegetation index (NDVI), plan curvature $${(\mathrm{Pl}}_{\mathrm{c}})$$ ( Pl c ) , profile curvature $${(\mathrm{Pr}}_{\mathrm{c}})$$ ( Pr c ) , slope $${(S}^{^\circ })$$ ( S ∘ ) , stream power index (SPI), and topographic wetness index (TWI). The multi-collinearity analysis and mean decrease Gini (MDG) were used to assess the suitability and predictability of these factors. Consequently, five landslide susceptibility prediction (LSP) maps were generated and validated using accuracy, area under the receiver operatic characteristic curve, sensitivity, and specificity. The MDG results demonstrated that the rainfall, elevation, and lithology were the most significant landslide conditioning factors ruling the occurrence of landslides in Qinghai-Tibetan Plateau. The LSP maps depicted that the north-northwestern and south-southeastern regions ( 45% of total area). Moreover, among the five models with a high goodness-of-fit, RF model was highlighted as the superior one, by which higher accuracy of landslide susceptibility assessment and better prone areas management in QTP can be achieved compared to previous results. Graphical Abstrac

Challenges in urban stormwater management in Chinese cities: A hydrologic perspective

For managing the worsening urban water disasters in China, the Government of China proposed the concept of “Sponge City” in 2013 and initiated the strategy in 30 pilot cities from 2015. Despite the promise of the concept, there have been many challenges in implementing the “Sponge City” program (SCP). In this manuscript, we discuss the hydrology-related challenges in implementing the SCP. In particular, we consider two key challenges: (1) Determination of the “Volume Capture Ratio of Annual Rainfall” (VCRAR), as controlling urban stormwater runoff is one of the core targets of the SCP; and (2) Estimation of a proper rainfall threshold, which influences the layout of green-infrastructures in the SCP to achieve the core VCRAR target. To discuss these challenges, we consider the city of Beijing, the capital of China, as a case study. Our analysis shows that the trade-offs between the investment for the SCP and its potential economic benefits should be considered by undertaking a proper determination of VCRAR. The VCRAR estimated for Beijing from the present analysis is 0.73. This value is more reasonable than the empirical value of 0.80 that is presently used, as it can guarantee the positive rate of return on the investment. We also find that the nonstationary characteristics of rainfall data and their spatiotemporal differences are important for the estimation of the rainfall threshold in SCP. For instance, even using the daily rainfall data over a period of 30 years (1983–2012) in Beijing, as required by the National Assessment Standard, the estimated rainfall threshold of 27.3 mm underestimates the reasonable rainfall threshold that should at least be larger than 30.0 mm. Thus, the former cannot ensure the VCRAR target of 0.80. Based on these results, we offer proper approaches and key suggestions towards useful guidelines for delivering better SCP in the Chinese cities

Trend variations of water balance components affected by climate changes (Case study: Atrak river basin, Iran)

Global warming will lead to changes in spatiotemporal distributions of regional water resources and the global hydrological cycles. Thus, an important task in climate change detection is represented by the analysis of changes in meteorological variables. The current paper aimed to detect the trends of hydro-meteorological variables such as precipitation and river discharge by focusing on the impacts of climate change. In order to achieve the stated purpose, linear regression and non-parametric test of Mann–Kendal were used on water balance components such as runoff, actual evapotranspiration and precipitation using Palmer equation in eight north eastern basins of Khorasan province. Thirty years of data results of Mann–Kendall test indicated that, the trends in average precipitation were inhomogeneous across the watersheds. The analysis of time series of discharge in most of the watersheds showed an obvious falling trend at 95 and 99 percentage levels of confidence monthly, seasonally and annually. Also, an ascending trend in evapotranspiration rate and temperature was found throughout the years. Generally, the global climate change has no significant effect on the hydrological inputs in the studied area. However, evapotranspiration component, as a part of output factors, has been increased due to the observed warming trend. Thus, a drastic strategic management is highly recommended to reduce the amount of loss

Wavelet-Based Hydrological Time Series Forecasting

These days wavelet analysis is becoming popular for hydrological time series simulation and forecasting. There are, however, a set of key issues influencing the wavelet-aided data preprocessing and modeling practice that need further discussion. This article discusses four key issues related to wavelet analysis: discrepant use of continuous and discrete wavelet methods, choice of mother wavelet, choice of temporal scale, and uncertainty evaluation in wavelet-aided forecasting. The article concludes with a personal reflection on solving the four issues for improving and supplementing relevant wavelet studies, especially wavelet-based artificial intelligence modeling

Microdisk Resonator With Negative Thermal Optical Coefficient Polymer for Refractive Index Sensing With Thermal Stability

In this paper, we propose a microdisk resonator with negative thermal optical coefficient (TOC) polymer for refractive index (RI) sensing with thermal stability. The transmission characteristics and sensing performances by using quasi-TE01 and quasi-TM01 modes are simulated by a three-dimensional finite element method. The influences of the TOC, RI, and thickness of the polymer on the sensing performances are also investigated. The simulation results show that the RI sensitivity Sn and temperature sensitivity ST with different polymers are in the ranges of 25.1-26 nm/RIU and 67.3-75.2 pm/K for the quasi-TE01 mode, and 94.5-110.6 nm/RIU and 1.2-51.3 pm/K for the quasi-TM01 mode, respectively. Moreover, figure-of-merit of the temperature sensing for the quasi-TM01 mode is in the range of 2 × 10 -4 -8 × 10 -3 , which can find important application in the implementation of the adiabatic devices

Microdisk Resonator With Negative Thermal Optical Coefficient Polymer for Refractive Index Sensing With Thermal Stability

In this paper, we propose a microdisk resonator with negative thermal optical coefficient (TOC) polymer for refractive index (RI) sensing with thermal stability. The transmission characteristics and sensing performances by using quasi-TE01 and quasi-TM01 modes are simulated by a three-dimensional finite element method. The influences of the TOC, RI, and thickness of the polymer on the sensing performances are also investigated. The simulation results show that the RI sensitivity Sn and temperature sensitivity ST with different polymers are in the ranges of 25.1-26 nm/RIU and 67.3-75.2 pm/K for the quasi-TE01 mode, and 94.5-110.6 nm/RIU and 1.2-51.3 pm/K for the quasi-TM01 mode, respectively. Moreover, figure-of-merit of the temperature sensing for the quasi-TM01 mode is in the range of 2 × 10-4-8 × 10-3, which can find important application in the implementation of the adiabatic devices

Correlation-aided method for identification and gradation of periodicities in hydrologic time series

Identification of periodicities in hydrological time series and evaluation of their statistical significance are not only important for water-related studies, but also challenging issues due to the complex variability of hydrological processes. In this article, we develop a “Moving Correlation Coefficient Analysis” (MCCA) method for identifying periodicities of a time series. In the method, the correlation between the original time series and the periodic fluctuation is used as a criterion, aiming to seek out the periodic fluctuation that fits the original time series best, and to evaluate its statistical significance. Consequently, we take periodic components consisting of simple sinusoidal variation as an example, and do statistical experiments to verify the applicability and reliability of the developed method by considering various parameters changing. Three other methods commonly used, harmonic analysis method (HAM), power spectrum method (PSM) and maximum entropy method (MEM) are also applied for comparison. The results indicate that the efficiency of each method is positively connected to the length and amplitude of samples, but negatively correlated with the mean value, variation coefficient and length of periodicity, without relationship with the initial phase of periodicity. For those time series with higher noise component, the developed MCCA method performs best among the four methods. Results from the hydrological case studies in the Yangtze River basin further verify the better performances of the MCCA method compared to other three methods for the identification of periodicities in hydrologic time series