Combined gamma and M-test-based ANN and ARIMA models for groundwater fluctuation forecasting in semiarid regions

The shortage of surface water in arid and semiarid regions has led to the more use of the groundwater resources. In these areas, the groundwater is essential for activities such as water supply and irrigation. One of the most important stages in sustainable yield of groundwater resources is awareness of groundwater level. In this study, we have applied artificial neural networks (ANN) and autoregressive integrated moving average (ARIMA) models for groundwater level forecasting to 4 months ahead in Shiraz basin, southwestern Iran. Time series analysis was conducted according to the Box–Jenkins method. Meanwhile, gamma and M-test were considered for determining the optimal input combination and length of training and testing data in the ANN model. The results indicated that performance of multilayer perceptron neural network (4, 14, 1) and ARIMA (2, 1, 2) is satisfactory in the groundwater level forecasting for one month ahead. The performance comparison shows that the ARIMA model performs appreciably better than the ANN.     

Flood forecasts based on multi-model ensemble precipitation forecasting using a coupled atmospheric-hydrological modeling system

The recent improvement of numerical weather prediction (NWP) models has a strong potential for extending the lead time of precipitation and subsequent flooding. However, uncertainties inherent in precipitation outputs from NWP models are propagated into hydrological forecasts and can also be magnified by the scaling process, contributing considerable uncertainties to flood forecasts. In order to address uncertainties in flood forecasting based on single-model precipitation forecasting, a coupled atmospheric-hydrological modeling system based on multi-model ensemble precipitation forecasting is implemented in a configuration for two episodes of intense precipitation affecting the Wangjiaba sub-region in Huaihe River Basin, China. The present study aimed at comparing high-resolution limited-area meteorological model Canadian regional mesoscale compressible community model (MC2) with the multiple linear regression integrated forecast (MLRF), covering short and medium range. The former is a single-model approach; while the latter one is based on NWP models [(MC2, global environmental multiscale model (GEM), T213L31 global spectral model (T213)] integrating by a multiple linear regression method. Both MC2 and MLRF are coupled with Chinese National Flood Forecasting System (NFFS), MC2-NFFS and MLRF-NFFS, to simulate the discharge of the Wangjiaba sub-basin. The evaluation of the flood forecasts is performed both from a meteorological perspective and in terms of discharge prediction. The encouraging results obtained in this study demonstrate that the coupled system based on multi-model ensemble precipitation forecasting has a promising potential of increasing discharge accuracy and modeling stability in terms of precipitation amount and timing, along with reducing uncertainties in flood forecasts and models. Moreover, the precipitation distribution of MC2 is more problematic in finer temporal and spatial scales, even for the high resolution simulation, which requests further research on storm-scale data assimilation, sub-grid-scale parameterization of clouds and other small-scale atmospheric dynamics.     

Analysis and prediction of Caspian Sea level pattern anomalies observed by satellite altimetry using autoregressive integrated moving average models

In this study, we successfully present the analysis and forecasting of Caspian Sea level pattern anomalies based on about 15 years of Topex/Poseidon and Jason-1 altimetry data covering 1993–2008, which are originally developed and optimized for open oceans but have the considerable capability to monitor inland water level changes. Since these altimetric measurements comprise of a large datasets and then are complicated to be used for our purposes, principal component analysis is adopted to reduce the complexity of large time series data analysis. Furthermore, autoregressive integrated moving average (ARIMA) model is applied for further analyzing and forecasting the time series. The ARIMA model is herein applied to the 1993–2006 time series of first principal component scores (sPC1). Subsequently, the remaining data acquired from sPC1 is used for verification of the model prediction results. According to our analysis, ARIMA (1,1,0)(0,1,1) model has been found as optimal representative model capable of predicting pattern of Caspian Sea level anomalies reasonably. The analysis of the time series derived by sPC1 reveals the evolution of Caspian Sea level pattern can be subdivided into five different phases with dissimilar rates of rise and fall for a 15-year time span.     

三种地下水位动态预测模型在吉林西部的应用与对比

准确而可靠地预测地下水埋深对生态环境保护和水资源规划管理具有重要意义。针对吉林西部浅层地下水位动态变化的复杂性和非线性,提出了基于小波分析与人工神经网络相结合的预测方法小波神经网络(WA-ANN)模型。将研究区2002年1月2009年12月当月降水量、蒸发量、人工开采量和前月平均地下水埋深4个参数作为输入,当月平均地下水埋深作为输出,建立浅层地下水埋深预测模型,并与BP神经网络(BP-ANN)模型和自回归移动平均(ARIMA)模型进行比较,对比分析了三者的建模过程及其模拟精度。结果显示:相比两种ANN模型,ARIMA模型建模过程更为简单,计算效率更高;但WA-ANN模型的拟合精度高于BP-ANN和ARIMA模型,预测效果更好。总体来看,WA-ANN模型在浅层地下水埋深预测中具有一定的应用推广价值。     

西太平洋副高形态指数的分解重构与集成预测

用小波分解和自适应神经模糊推理系统(ANFIS)相结合的方法,建立了西太平洋副热带高压形态指数月、季时间尺度的集成预报模型。由于小波分解可在信号的频域—时域内自由伸缩,准确地分解和重构带通、低通信号,因而能将复杂的副高指数时间序列分解为相对简单的周期分量信号,既简化了系统结构,又突出了信号特征。随后基于ANFIS模糊系统的非线性、容错性、自适应性和联想学习功能,建立各分量信号的独立预报模型,最后对分量预报结果进行集成。试验结果表明,该方法在保留预报对象主要特征的前提下,有效降低了预报难度,预报准确率和预报时效均较传统方法有明显的改进和提高。     

Surface runoff prediction regarding LULC and climate dynamics using coupled LTM,optimized ARIMA,and GIS-based SCS-CN models in tropical region

The effects of climate and land use/land cover (LULC) dynamics have directly affected the surface runoff and flooding events. Hence, current study proposes a full-packaged model to monitor the changes in surface runoff in addition to forecast of the future surface runoff based on LULC and precipitation variations. On one hand, six different LULC classes were extracted from Spot-5 satellite image. Conjointly, land transformation model (LTM) was used to detect the LULC pixel changes from 2000 to 2010 as well as predict the 2020 ones. On the other hand, the time series-autoregressive integrated moving average (ARIMA) model was applied to forecast the amount of rainfall in 2020. The ARIMA parameters were calibrated and fitted by latest Taguchi method. To simulate the maximum probable surface runoff, distributed soil conservation service-curve number (SCS-CN) model was applied. The comparison results showed that firstly, deforestation and urbanization have been occurred upon the given time, and they are anticipated to increase as well. Secondly, the amount of rainfall has non-stationary declined since 2000 till 2015 and this trend is estimated to continue by 2020. Thirdly, due to damaging changes in LULC, the surface runoff has been also increased till 2010 and it is forecasted to gradually exceed by 2020. Generally, model calibrations and accuracy assessments have been indicated, using distributed-GIS-based SCS-CN model in combination with the LTM and ARIMA models are an efficient and reliable approach for detecting, monitoring, and forecasting surface runoff.     

基于EMD组合模型的径流多尺度预测

受全球气候变化与人类活动影响,径流序列愈发呈现出非稳态与非线性特征,为降低由此而引发的预报误差,充分发挥不同模型对提高径流预测精度的优势,针对传统径流预报模型的单一性,以干旱区典型内陆河玛纳斯河为例,采用经验模态分解(EMD)提取径流序列中具有物理含义的信号,得到不同时间尺度的多个固有模态函数(IMF)及1个趋势项,利...     

Seasonal Forecasting for Climate Hazards: Prospects and Responses

One of the most promising developments for early warning of climate hazards is seasonal climate forecasting. Already forecasts are operational in many parts of the tropics and sub-tropics, particularly for droughts and floods associated with ENSO events. Prospects for further development of seasonal forecasting for a range of climatichazards are reviewed, illustrated with case studies in Africa, Australia, the U.S.A. and Europe. A critical evaluation of the utility of seasonal forecasts centres on vulnerability, communicationchannels, and effective responses. In contrast to short-term prediction, seasonal forecasts raise new issues of preparedness and the use of information.     

混沌序列WA-VOLTERRA耦合模型在月降水量预测中的应用

由于月降水量时间序列含有大量噪声, 并表现出明显的混沌特性, 现有预测模型均存在一定程度的不足.基于混沌理论的小波分析-VOLTERRA级数自适应(WA-VOLTERRA)耦合预测模型, 在对月降水量时间序列进行混沌特性识别的基础上, 先用小波分析对月降水序列进行时频分解, 再分别对各频率分量进行相空间重构并用3阶VOLTERRA级数自适应模型建模预测, 最后综合得到原始序列的预测值.以相近区域杭州市和南通市的月降水序列为例, 并通过与小波分析-支持向量机(WA-SVM)模型进行比较, 发现该模型具有较强的适用性和更高的预测精度.      

A note on the performance of climatic forecasts for Asia based on the periodic portion of the data: 1986–1987

Between 1973 and 1986 a group at the University of Wisconsin worked on the use of the periodic portion of climatic time series with the aim of exploring the potential for year-or-more in advance forecasting. This paper reports on the real time verification of the last sets of forecasts made by the group. From spectra of temperature and cube-rooted precipitation the dominant frequencies were chosen. These were usually related to tidal frequencies. A Fourier series of these dominant terms was then fitted to the dependent data set and future values calculated. These were analyzed for forecast skill, and the skillful Fourier series retained. Real time forecasts were then made. Verification shows a low probability that the forecast skills were obtained by chance. It is suggested that the periodic term might be a useful addition to more standard approaches to long range forecasting.     

Groundwater depth predictions by GSM,RBF, and ANFIS models: a comparative assessment

The potential of grey self-memory model (GSM), radial basis function network (RBF), and adaptive neuro fuzzy inference system (ANFIS) models in forecasting groundwater depths over an unconfined aquifer was compared. GSM, RBF, and ANFIS modeling was carried out at five sites in Jilin City, northeastern China, considering the influential lags of monthly groundwater depth as the inputs. The performance of the models was evaluated using criteria standards (R, RMSE, MARE, NS) and graphical indicators. Results indicate that the performance of all models was satisfactory in the region which lack of hydro-meteorological data. Comparison of the goodness-of-fit statistics in the research indicated that ANFIS was the better technique than the other two at all the sites except for J₂₁, and GSM(1,1) was the worst model at all the sites. However, considering the practical advantages of GSM(1,1) technique, it was recommended as an alternative and cost-effective groundwater modeling tool. Meanwhile, it was found that the modeling prediction for the well with the stable and evenly distributed data series has more accurate fitting results, generally.     

Drought forecasting through statistical models using standardised precipitation index: a systematic review and meta-regression analysis

Quality and reliable drought prediction is essential for mitigation strategies and planning in disaster-stricken regions globally. Prediction models such as empirical or data-driven models play a fundamental role in forecasting drought. However, selecting a suitable prediction model remains a challenge because of the lack of succinct information available on model performance. Therefore, this review evaluated the best model for drought forecasting and determined which differences if any were present in model performance using standardised precipitation index (SPI). In addition, the most effective combination of the SPI with its respective timescale and lead time was investigated. The effectiveness of data-driven models was analysed using meta-regression analysis by applying a linear mixed model to the coefficient of determination and the root mean square error of the validated model results. Wavelet-transformed neural networks had superior performance with the highest correlation and minimum error. Preprocessing data to eliminate non-stationarity performed substantially better than did the regular artificial neural network (ANN) model. Additionally, the best timescale to calculate the SPI was 24 and 12 months and a lead time of 1–3 months provided the most accurate forecasts. Studies from China and Sicily had the most variation based on geographical location as a random effect; while studies from India rendered consistent results overall. Variation in the result can be attributed to geographical differences, seasonal influence, incorporation of climate indices and author bias. Conclusively, this review recommends use of the wavelet-based ANN (WANN) model to forecast drought indices.

     

Short-run forecasting of United States coal production

Summary This paper focuses on short-range modelling and forecasting of aggregate US monthly coal production. The 1976–83 time-series data suggest a multiplicative autoregressive integrated moving average (ARIMA) model to replicate national level monthly coal production. The identified model required 12-month seasonal differencing and has an autoregressive component of lag 1 and a moving average component of lag 12. Model predictions for 1984 were very reasonable when compared with actual production: cyclical patterns were correctly replicated and the deterministic increasing trend was properly identified. The estimated model was enhanced by updating it with data for 1984. Intervention analysis was used to determine the impact of labour negotiations in coal production. Information relative to the identified ARIMA model was then used to model the intervening event of labour negotiations. Intervention modelling produced forecasts for 1984 superior to those identified by the ARIMA model. The mean predicted 1984 US monthly coal production of 1976–84 ARIMA and intervention models were 96.05 and 99.65% of the observed value of 74 178 thousand short tons per month, respectively. Simplicity of the ARIMA and intervention models, the realiability of their predictions, and the ease of updating make them very attractive when compared with large scale econometric models for use in short-term coal production forecasting.     

ARIMA模型在新疆喀什地区中长期降水量预测中的应用研究

为研究新疆喀什地区降水量中长期预测问题,结合ARIMA模型对新疆喀什地区的降水量进行预测,利用喀什地区两个子流域1950-2015年实测年降水数据分析其模型的适用性和预测精度。结果表明:ARIMA模型可较好的模拟喀什地区的年降水量,具有较好的预测精度,模拟降水量和实测降水量之间的误差相对值低于20%,绝对误差也可控制在15 mm以内;从空间角度分析,降水总体分布从东向西逐步递减,预测结果和实际降水空间分布状况较为吻合,可达到乙级精度标准。研究成果对于喀什地区中降水量长期预测具有较好的参考价值。     

Application of neural network and ANFIS model for earthquake occurrence in Iran

This study examined the spatial-temporal variations in seismicity parameters for the September 10^th, 2008 Qeshm earthquake in south Iran. To this aim, artificial neural networks and Adaptive Neural Fuzzy Inference System (ANFIS) were applied. The supervised Radial Basis Function (RBF) network and ANFIS model were implemented because they have shown the efficiency in classification and prediction problems. The eight seismicity parameters were calculated to analyze spatial and temporal seismicity pattern. The data preprocessing that included normalization and Principal Component Analysis (PCA) techniques was led before the data was fed into the RBF network and ANFIS model. Although the accuracy of RBF network and ANFIS model could be evaluated rather similar, the RBF exhibited a higher performance than the ANFIS for prediction of the epicenter area and time of occurrence of the 2008 Qeshm main shock. A proper training on the basis of RBF network and ANFIS model might adopt the physical understanding between seismic data and generate more effective results than conventional prediction approaches. The results of the present study indicated that the RBF neural networks and the ANFIS models could be suitable tools for accurate prediction of epicenteral area as well as time of occurrence of forthcoming strong earthquakes in active seismogenic areas.     

A preliminary discussion on the opportunities and challenges of linking climate change adaptation with disaster risk reduction

Managing the risks of extreme events such as natural disasters to advance climate change adaptation (CCA) has been a global focus. However, a critical challenge in supporting CCA is to improve its linkage with disaster risk reduction (DRR). Based on discussions on similarities and differences between CCA and DRR concerning their spatial–temporal scales, main focuses, preferred research approaches and methodologies, etc., this paper tentatively put forward an analytical framework of “6W” for linking DRR with CCA. This framework presented preliminary answers to a series of fundamental questions, such as “What is adaptation with respect to disaster risk?” “Why adaptation is needed?” “Who adapt to what?” “How to adapt?” “What are the possible principles to assess the adaptation effect?” To bridge the research gaps between CCA and DRR, it is imperative to associate the adaptation actions with both near-term disaster risk and long-term climate change and formulate adaptation strategies at various spatial–temporal scales by embracing uncertainty in a changing climate.     

Rainfall-Runoff Modeling using Clustering and Regression Analysis for the River Brahmaputra Basin

In this research, k-means, agglomerative hierarchical clustering and regression analysis have been applied in hydrological real time series in the form of patterns and models, which gives the fruitful results of data analysis, pattern discovery and forecasting of hydrological runoff of the catchment. The present study compares with the actual field data, predicted value and validation of statistical yields obtained from cluster analysis, regression analysis with ARIMA model. The seasonal autoregressive integrated moving average (SARIMA) and autoregressive integrated moving average (ARIMA) models is investigated for monthly runoff forecasting. The different parameters have been analyzed for the validation of results with casual effects. The comparison of model results obtained by K-means & AHC have very close similarities. Result of models is compared with casual effects in the same scenario and it is found that the developed model is more suitable for the runoff forecasting. The average value of R² determined is 0.92 for eight ARIMA models. This shows more accuracy of developed ARIMA model under these processes. The developed rainfall runoff models are highly useful for water resources planning and development.     

频谱分析法在挠力河流域年降水量预报中的应用

本文介绍了用频谱分析法进行降水量分析和预报的基本方法。首先对实测序列进行趋势项分析；其次用自相关函数进行谐波模式检验；然后根据函数的傅立叶级数展开理论，分析谱参数之间的函数关系，计算傅氏系数；最后采用F分布检验法对周期进行显著性检验。确定主要周期，从而建立预报模型。并应用该方法对挠力河流域菜咀子站的年降水量进行了预报，结果表明该区年降水量存在两个主要周期（3年和9年左右），反映了该地区的气候变化规律。实例证明，频谱分析法预测效果很好．预报结果可为挠力河流域的水资源开发和管理提供依据。     

考虑监测离群值朱家店滑坡位移预测研究

滑坡位移预测预报是滑坡防灾减灾的重要组成部分,提高滑坡位移预测的准确性与精确度是该项研究的重点与难点。本文在滑坡位移预测中考虑了监测样本的离群值,通过忽略、指定与修正离群值3种方式,研究滑坡位移预测样本离群值的最优处理方式。以三峡库区朱家店滑坡为例,基于ARIMA(p,d,q)模型,分别对累积位移与位移速率时间序列开展了预测研究。研究结果表明:修正离群值的预测结果介于忽略和指定离群值两者之间,更适用于存在监测离群值的滑坡位移预测;对于ARIMA模型,更适合采用位移速率进行预测预报;使用位移速率时间序列ARIMA(1,0,1)并修正离群值的预测结果为:2016年和2017年6月份滑坡前缘GP3"阶跃"位移分别为79. 0 mm和70. 2 mm,截止2017年8月,GP3累积位移将达1647. 7 mm。     

One step back for a leap forward: toward operational measurements of elements at risk

This study presents the evaluation of 1 year of operational lightning forecasts provided for Europe, using the Weather Research and Forecasting model coupled with a cloud-top height-based lightning parameterization scheme. Three different convective parameterization schemes were employed for parameterizing sub-grid cloud-top heights and consequently driving the lightning scheme. Triggering of the lightning scheme was controlled by means of a model-resolved microphysics-based masking filter, while the formulation for deriving lightning flash rates was also modified, assuming a single “marine” equation instead of the original equations discriminating between continental and marine lightning. Gridded lightning observations were used for evaluating model performance on a dichotomous decision basis. Analysis showed that the lightning scheme is sensitive to the parameterization of convection. In particular, the Kain–Fritsch convective scheme was found to outperform the Grell–Devenyi and Grell–Freitas schemes, showing a statistically significant better performance with respect to lightning prediction. This was most evident during the warm season, while smaller differences among the schemes were recorded during the cold season. Further, for all examined convective schemes, it was found that the application of the masking filter is desirable for improving model performance in terms of lightning forecasting. Last, the reported results revealed that the refinement of the formulation of the lightning parameterization scheme, adhering to a “global” marine equation instead of distinguishing between land and sea lightning, may be necessary in order to obtain reliable lightning forecasts.