Maximum likelihood environmental calibration and the compute program WACALIB a correction

A recently discovered error in the part of the computer program WACALIB that implements maximum likelihood (ML) calibration has been discovered and corrected. The new version of WACALIB has been re-run with all the data-sets from which results based on the earlier version of WACALIB had been published. The new results suggest that ML regression and calibration perform as well or even better than weighted averaging (WA), at least when judged by the apparent root mean squared error. Further work involving cross-validation is required to evaluate more fully the relative performance of WA and ML approaches.     

Thematic Map Analysis Using Multiple Regression

Thematic maps can be analyzed by multiple regression for (1) forward prediction where a younger geological structural map is written as a least-squares function of older maps for information on historical perspectives or (2) by backward prediction where an older map is regressed stepwise on a series of younger ones to aid in prospecting. The technique was evaluated by a series of structure maps on different geological horizons from the U.S. Midcontinent (Kansas) where the forward prediction proved more effective than backward. In forward prediction, the first map entered into a multiple regression is invariably the immediately underlying one as expected. On the other hand, the first map in a backward prediction is not necessarily related to stratigraphy which limits the utility of the technique for prospecting.     

山地降水垂直分布三参数高斯模式及其应用

针对蒋忠信山地降水高斯模式计算中的问题,提出三参数高斯模式及其非线性回归计算方法,将其应用于我国部分山地降水中,效果较好。     

本溪市区剪切波速分布的初步研究

本文利用检层法、表面波法实测的波速资料。通过对实测数据的处理和回归分析,求出了本溪市区不同岩土波速与埋深的回归关系。同时收集了市区已有的钻孔资料,经分析计算,给出了本溪市区平均剪切波速的分布。     

Estimation of the transfer function by autoregressive deconvolution techniques—An application to time series analysis

The transfer function of time-dependent models is classically inferred by the ordinary least squares (OLS) techniques. This OLS technique assumes independence of the residuals with time. However, in practical cases, this hypothesis is often not justified producing inefficient estimation of the transfer function. When the residuals constitute an autoregressive process, we propose to apply the Box-Jenkins' method to model the residuals, and to modify in a simple manner the primary convolution equation. Then, a multivariate regression technique is used to infer the transfer function of the new equation producing time-independent residuals. This three-step autoregressive deconvolution technique is particularly efficient for time series analysis. The reconstitution and the forecasting of real data are improved efficiently. Theoretically, the proposed method can be extended to the convolution equations for which the residuals follow a moving average or an autoregressive-moving average process, but the mathematical formulation is no longer direct and explicit. For this general case, we propose to approximate the moving average or the autoregressive-moving average process by an autoregressive process of sufficient order, and then the transfer function. Two case studies in hydrogeology will be used to illustrate the procedure.     

川滇强震的回归—马尔可夫链联合预测

本文应用回归—马尔可夫链联合预测地震的方法,结合川、滇强震的特点,对川、滇强震进行了计算,并作了预测验证。结果表明,该方法对川、滇强震的预测效果较好。     

集成奇异谱分析和自回归滑动平均预测日本近海海平面变化

本文从日本沿岸选取了28个验潮站及联测的GPS站,利用奇异谱分析（Singular Spectrum Analysis,SSA）和SSA+自回归滑动平均（Auto Regression Moving Average,ARMA）方法预测了2014—2018年的近海海平面变化和地壳垂直变化.并用同时段的验潮及GPS的实际测量值进行验证,结果显示,SSA+ARMA预测的相对海平面精度为0.0357~0.0607 m,地壳垂直运动的精度为0.0049~0.0077 m,绝对海平面的精度为0.0433~0.0683 m,且三者SSA+ARMA的预测结果均优于只用SSA预测的结果.在此基础上本文利用SSA+ARMA预测了日本沿岸2019—2023年的近海绝对海平面变化,结果显示,2019—2023年的平均海面高较往年（2014—2018）升高0.0353 m,2003—2023年绝对海平面的变化率为0.0039 m·a^-1,预测结果较为理想.     

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.     

Improvement of spatial interpolation accuracy of daily maximum air temperature in urban areas using a stacking ensemble technique

ABSTRACT

The reliable and robust monitoring of air temperature distribution is essential for urban thermal environmental analysis. In this study, a stacking ensemble model consisting of multi-linear regression (MLR), support vector regression (SVR), and random forest (RF) optimized by the SVR is proposed to interpolate the daily maximum air temperature (T_max) during summertime in a mega urban area. A total of 10 geographic variables, including the clear-sky averaged land surface temperature and the normalized difference vegetation index, were used as input variables. The stacking model was compared to Cokriging, three individual data-driven methods, and a simple average ensemble model, all through leave-one-station-out cross validation. The stacking model showed the best performance by improving the generalizability of the individual models and mitigating the sensitivity to the extreme daily T_max. This study demonstrates that the stacking ensemble method can improve the accuracy of spatial interpolation of environmental variables in various research fields.