中国西北大旱年发生概率的统计推断

根据中国西北近500a旱涝等级资料，用Bernoulli试验等理论模式，对大旱年发生的概率特征进行了研究，得出大旱年发生的统计规律。在全球气候变暖的大背景下，给出了21世纪中国西北大旱年发生概率的统计推断结果。     

塔里木盆地库车油气系统中、新生界的流体压力结构和油气成藏机制

库车油气系统中、新生界流体压力纵向结构可分为台阶式、中凸式、均斜式 3种类型。平面分布可分两个带 3个区 :北带主要是台阶式压力结构分布区 ,南带的大部分是中凸式压力结构分布区 ,南带的南缘为均斜式压力结构分布区 ,构成不同类型的封存箱。这种压力结构分异主要是喜玛拉雅晚期 ( 5Ma)天山向南强烈推挤造成的 ;喜玛拉雅早期 ,整个油气系统压力结构大体是一致的 ,深浅层基本为正常压力 ,也即均为均斜式压力结构分布区。喜玛拉雅早、晚期的流体压力封存箱规模不等 ,油气运聚的环境不同 ,因而不同时期、不同地带具有各不相同的油气成藏机制。初步可概括为 3种机制和 3种成藏模式 :早期封存箱内成藏机制———牙哈模式 ;晚期封存箱内成藏机制———克拉苏模式 ;封存箱外成藏机制———大宛齐模式     

Cuttability assessment of hard coal seams

Summary A number of field and laboratory tests have been carried out on more than 15 coal seams of compressive strengths ranging from 19 MPa to 44 MPa to evolve methods which would help in the selection of suitable coaling machines for hard coal seams. The effect of physico-mechanical properties on cuttability were studied in the laboratory for all these coal seams to identify the relevant parameters affecting the specific energy of coal cuttability. These data were subjected to regression analysis to find the best fit for estimation of laboratory specific energy of coal samples on the basis of simple laboratory and field tests for the strength parameters. Field studies were also conducted over a large number of active mechanized coal faces to study in situ cuttability along with the geo-mining conditions of the site. The field and the laboratory data so generated were correlated and an attempt is made to establish a relationship for estimating the field specific energy for a particular capacity of coaling machines by considering the geo-mining domain of the field in totality.     

机房干扰源的分析

从理论上分析了机房中干扰产生的原因,提出了压低干扰的措施。     

Monthly suspended sediment load prediction using artificial intelligence: testing of a new random subspace method

ABSTRACT

The predictive capability of a new artificial intelligence method, random subspace (RS), for the prediction of suspended sediment load in rivers was compared with commonly used methods: random forest (RF) and two support vector machine (SVM) models using a radial basis function kernel (SVM-RBF) and a normalized polynomial kernel (SVM-NPK). Using river discharge, rainfall and river stage data from the Haraz River, Iran, the results revealed: (a) the RS model provided a superior predictive accuracy (NSE = 0.83) to SVM-RBF (NSE = 0.80), SVM-NPK (NSE = 0.78) and RF (NSE = 0.68), corresponding to very good, good, satisfactory and unsatisfactory accuracies in load prediction; (b) the RBF kernel outperformed the NPK kernel; (c) the predictive capability was most sensitive to gamma and epsilon in SVM models, maximum depth of a tree and the number of features in RF models, classifier type, number of trees and subspace size in RS models; and (d) suspended sediment loads were most closely correlated with river discharge (PCC = 0.76). Overall, the results show that RS models have great potential in data poor watersheds, such as that studied here, to produce strong predictions of suspended load based on monthly records of river discharge, rainfall depth and river stage alone.     

Adaptive neuro-fuzzy inference system coupled with shuffled frog leaping algorithm for predicting river streamflow time series

ABSTRACT

Accurate runoff forecasting plays a key role in catchment water management and water resources system planning. To improve the prediction accuracy, one needs to strive to develop a reliable and accurate forecasting model for streamflow. In this study, the novel combination of the adaptive neuro-fuzzy inference system (ANFIS) model with the shuffled frog-leaping algorithm (SFLA) is proposed. Historical streamflow data of two different rivers were collected to examine the performance of the proposed model. To evaluate the performance of the proposed ANFIS-SFLA model, six different scenarios for the model input–output architecture were investigated. The results show that the proposed ANFIS-SFLA model (R² = 0.88; NS = 0.88; RMSE = 142.30 (m³/s); MAE = 88.94 (m³/s); MAPE = 35.19%) significantly improved the forecasting accuracy and outperformed the classic ANFIS model (R² = 0.83; NS = 0.83; RMSE = 167.81; MAE = 115.83 (m³/s); MAPE = 45.97%). The proposed model could be generalized and applied in different rivers worldwide.     

A minimalistic approach for evapotranspiration estimation using the Prophet model

ABSTRACT

This study aimed to evaluate the potential of the recently introduced Prophet model for estimating reference evapotranspiration (ETo). A comparative study was conducted for benchmarking the model results with support vector regression (SVR) and temperature-based empirical models (Thornthwaite and Hargreaves) in southern Japan. The performance of the Prophet, SVR and temperature-based empirical models was evaluated by Nash–Sutcliffe efficiency (NSE) and coefficient of determination (R²). The results indicate that temperature-based Prophet and SVR models have greater accuracy than the empirical models. The Prophet model with sole input of relative humidity, sunshine hours or windspeed showed acceptable accuracy (NSE > 0.80; R² > 0.80), while SVR models with similar inputs showed greater errors. Accuracy improved with increasing number of input parameters, giving excellent performance (NSE > 0.95; R² > 0.95) with all input parameters. Hence, the Prophet model is a new promising approach for modelling ETo with limited input variables.     

星载合成孔径雷达海洋遥感与大数据

星载合成孔径雷达以其全天候、全天时、不受云雨影响的工作特性在空间对海观测中起到了重要作用，又以其高空间分辨率、多极化、多成像模式的特点展示了其在海洋动力要素反演和海洋多尺度动力过程研究中独特的魅力.起步于20世纪70年代末的星载合成孔径雷达技术，迎来了发展的"黄金时期"，大数据和机器学习又赋予了星载合成孔径雷达海洋遥感更强大的生命力.本文首先阐述了星载合成孔径雷达大数据的5"V"特性，进而以高分辨率海面风场反演、海洋内波中尺度动力过程观测两类典型案例，阐述了大数据、机器学习等现代信息科学技术与卫星海洋遥感结合，实现海洋环境参数高精度反演和海洋动力过程科学深层次认知的研究.最后，展望了星载合成孔径雷达海洋遥感与大数据的发展前景.     

Knowledge-Driven and Data-Driven Fuzzy Models for Predictive Mineral Potential Mapping

In this paper, we describe new fuzzy models for predictive mineral potential mapping: (1) a knowledge-driven fuzzy model that uses a logistic membership function for deriving fuzzy membership values of input evidential maps and (2) a data-driven model, which uses a piecewise linear function based on quantified spatial associations between a set of evidential evidence features and a set of known mineral deposits for deriving fuzzy membership values of input evidential maps. We also describe a graphical defuzzification procedure for the interpretation of output fuzzy favorability maps. The models are demonstrated for mapping base metal deposit potential in an area in the south-central part of the Aravalli metallogenic province in the state of Rajasthan, western India. The data-driven and knowledge-driven models described in this paper predict potentially mineralized zones, which occupy less than 10% of the study area and contain at least 83% of the model and validation base metal deposits. A cross-validation of the favorability map derived from using one of the models with the favorability map derived from using the other model indicates a remarkable similarity in their results. Both models therefore are useful for predicting favorable zones to guide further exploration work.