Application of artificial neural networks in typhoon surge forecasting

A typhoon-surge forecasting model was developed with a back-propagation neural network (BPN) in the present paper. The typhoon's characteristics, local meteorological conditions and typhoon surges at a considered tidal station at time t−1 and t were used as input data of the model to forecast typhoon surges at the following time. For the selection of a better forecasting model, four models (Models A–D) were tested and compared under the different composition of the above-mentioned input factors. A general evaluation index that is a composition of four performance indexes was proposed to evaluate the model's overall performance. The result of typhoon-surge forecasting was classified into five grades: A (excellent), B (good), C (fair), D (poor) and E (bad), according to the value of the general evaluation index. Sixteen typhoon events and their corresponding typhoon surges and local meteorological conditions at Ken–fang Tidal Station in the coast of north-eastern Taiwan between 1993 and 2000 were collected, 12 of them were used in model's calibration while the other four were used in model's verification. The analysis of typhoon-surge forecasting results at Ken–fang tidal station show that the Model D composing 18 input factors has better performance, and that it is a suitable BPN-based model in typhoon-surge forecasting. The Model D was also applied to typhoon-surge forecasting at Cheng-kung Tidal Station in south-eastern coast of Taiwan and at Tung-shih Tidal Station in the coast of south-western Taiwan. Results show that the application of Model D in typhoon-surge forecasting at Cheng-kung Tidal Station has better performance than that at Tung-shih Tidal Station.     

基于支持向量回归的海面高度统计预报方法研究

本文提出一种基于支持向量回归的统计预报方法,通过经验正交分解对原始数据矩阵进行时空分解,提取出空间模态和时间系数。由于海面高度变化具有非线性、大惯性的特点,对时间系数进行小波分析,能有效滤除其中的高频信号,得到表征海面高度变化的低频信号。利用支持向量回归方法对小波分解后的低频信号构建预报模型。最后,进行小波重构,还原时间序列长度,实现未来7天的海面高度预报。通过黑潮附近海域的海面高度预报结果验证,该预报方法的预报效果优于整合滑动平均自回归预报方法。本文通过机器学习的算法实现了海面高度的预报,为海洋预报方法提供了新的思路。     

基于提升回归树的东、黄海鲐鱼渔场预报

为提高东、黄海鲐鱼渔场预报准确率、降低渔业生产成本,研究提出了一种基于提升回归树的渔场预报模型。研究采用2003—2010年我国大型灯光围网渔捞日志数据,以有网次记录的小渔区为渔场,以渔捞日志未记录的区域作为背景场随机选择假定非渔场数据,以海表水温等环境因子作为预测变量构建东、黄海鲐鱼渔场预报模型并以2011年的实际作业记录对预报模型进行精度验证。验证计算得到预报模型的AUC(area under receiver operating curve)值为0.897,表明模型的预报精度较高。模型的空间预测结果表明,预报渔场与实际作业位置基本吻合,其位置移动也与实际情况相符。这表明基于提升回归树的渔场预报模型可以用来进行东、黄海鲐鱼渔场的预报。     

Real time wave forecasting using neural networks

Forecasting of ocean wave heights, with warning time of a few hours or days, is necessary in planning many operation-related activities in the ocean. Such information is currently derived by numerically solving the differential equation representing wave energy balance. The solution procedure involved is extremely complex and calls for very large amounts of meteorological and oceanographic data. This paper presents a complementary and simple method to make a point forecast of waves in real time sense based on the current observation of waves at a site. It incorporates the technique of neural networks. The network involved is first trained by different algorithms and then used to forecast waves with lead times varying from 3 to 24 h. The results of different training algorithms are compared with each other. The neural output is further compared with the statistical AR models.     

Assessment of the Present State and Future Fate of River Saraswati,India: Water Quality Indices and Forecast Models as Diagnostic and Management Tools

Water quality assessment is key to the conservation and management of rivers. River Saraswati, a distributary of the river Ganga, serves as a lifeline to many villages in the district Hooghly in West Bengal, India. As the river is gradually dying due to diverse man-made pollution, ten water quality parameters in two sampling spots (PR-1 and PR-2) in the river are monitored month-wise from March 2017 to February 2020,  and these are compared with those from a reference pond. The water quality index (WQI) is determined for the two riverine spots and the reference pond based on the Canadian Council of Ministers of Environment WQI (CCMEWQI) and weighted arithmetic WQI, respectively. In addition to actual observations, three different forecasting methods, exponential smoothing, autoregressive integrated moving average, and artificial neural network, are used to predict WQI for the next two years. This study indicates that free CO₂, dissolved oxygen, and turbidity are the key parameters to evaluate this river's anthropogenic stress and health. The actual and forecasted results reflect the precipitous degradation of CCMEWQI in PR-2. Therefore, the immediate intervention of all stakeholders is required to adopt an integrated and comprehensive river management plan to save the river from utter obliteration.     

两种短期概率预测模型在2017年九寨沟7.0级地震中的应用和比较研究

本文选用"传染型余震序列"（ETAS）模型和Reasenberg-Jones（R-J）模型,分别对九寨沟M_S7.0地震序列的模型参数稳定性、余震发生率预测和余震概率预测进行了比较研究,并利用"地震信息增益"（IGPE）、N-test和T-test检验方法对预测效果进行了评价.研究结果表明,ETAS模型和R-J模型的序列参数分别在震后t₂=2.0天和t₂=1.50天后趋于稳定,此次九寨沟M_S7.0地震序列的衰减较为正常;对未来1天的余震发生率预测和余震概率连续滑动预测表明,ETAS模型给出的余震发生率和余震概率数值均低于R-J模型预测结果;IGPE结果显示,ETAS模型在95%的置信区间上预测效果明显优于R-J模型;统计检验结果表明,在序列参数较不稳定的震后早期阶段,ETAS模型预测失效而R-J模型预测效果较好,在序列参数稳定阶段,ETAS模型预测效果较好而R-J模型预测失效.根据上述分析,在与此次九寨沟M_S7.0地震类型相同的地震的余震预测策略上,如可在序列参数不稳定的震后早期阶段使用R-J模型、在此后使用ETAS模型,或可取得较好的预测效果.     

River temperature forecasting: case study for Little Southwest Miramichi River (New Brunswick,Canada)

River temperature models play an increasingly important role in the management of fisheries and aquatic resources. Among river temperature models, forecasting models remain relatively unused compared to water temperature simulation models. However, water temperature forecasting is extremely important for in-season management of fisheries, especially when short-term forecasts (a few days) are required. In this study, forecast and simulation models were applied to the Little Southwest Miramichi River (New Brunswick, Canada), where water temperatures can regularly exceed 25–29°C during summer, necessitating associated fisheries closures. Second- and third-order autoregressive models (AR2, AR3) were calibrated and validated using air temperature as the exogenous variable to predict minimum, mean and maximum daily water temperatures. These models were then used to predict river temperatures in forecast mode (1-, 2- and 3-day forecasts using real-time data) and in simulation mode (using only air temperature as input). The results showed that the models performed better when used to forecast rather than simulate water temperatures. The AR3 model slightly outperformed the AR2 in the forecasting mode, with root mean square errors (RMSE) generally between 0.87°C and 1.58°C. However, in the simulation mode, the AR2 slightly outperformed the AR3 model (1.25°C < RMSE < 1.90°C). One-day forecast models performed the best (RMSE ~ 1°C) and model performance decreased as time lag increased (RMSE close to 1.5°C after 3 days). The study showed that marked improvement in the modelling can be accomplished using forecasting models compared to water temperature simulations, especially for short-term forecasts.

EDITOR M.C. Acreman ASSOCIATE EDITOR S. Huang     

Prophet时序预测模型在电离层TEC异常探测中的应用

提出一种基于Facebook 开源的Prophet预测模型进行电离层TEC异常识别的新方法。 首先, 对比分析了该方法与传统时间序列预测方法(ARIMA模型等)预测电离层TEC建模背景值的精度, 以及与经典电离层TEC异常识别方法(滑动四分位法)提取前面对应一致的电离层TEC背景值的精度。 结果表明, Prophet预测模型预测建模背景值的精度要明显优于其他方法, 且预测的建模精度比ARIMA模型等方法高2.55倍左右, 比滑动四分位法高10.74倍左右。 同时, 在最佳预测建模区间时, 其精度值大小比较依次为RMSE_IQR=10.5841>RMSE_ARIMA=3.2780>RMSE_Prophet=0.8469, 说明传统探测法预测建模背景值时具有较大的不足。 随后, 以2017年8月8日九寨沟7.0级地震为例, 利用该方法分析了电离层TEC异常扰动情况, 并对比验证了该方法的有效性和准确性。 实验结果表明: 在震前第10 d和第2 d电离层TEC发生较为明显的负异常, 第7 d电离层TEC发生较为明显的正异常。 对比实验表明, Prophet预测模型的有效性和准确性明显优于滑动四分位法。     

基于微分响应的流域产流分单元修正方法

为考虑洪水预报误差的空间变化,提出一种基于微分响应的流域产流分单元修正方法.该方法建立了各单元流域产流与流域出口流量之间的微分响应关系,采用正则化最小二乘法结合逐步迫近进行反演求解,将产流误差估计量分配给相应单元流域实现流域产流分单元修正.将构建的方法应用于大坡岭流域和七里街流域进行新安江模型产流修正,比较分析了流域产流分单元修正、流域面平均产流修正和自回归修正的效果.结果表明：流域产流分单元修正效果优于流域面平均产流修正;随着预见期的增大,产流微分响应修正效果优于自回归修正.该方法通过汇流系统将流域出口断面流量信息进行分解用于修正各单元流域产流,有利于提高实时洪水预报精度.     

不同气候变化情景下亚洲北山羊（Capra sibirica）的响应（英文）

目前很少见到关于气候变化影响亚洲北山羊物种栖息地的研究。通过调查气候变化对塔吉克斯坦东部亚洲北山羊(Capra sibirica)分布的影响,并采用生态位建模比较了亚洲北山羊的适宜栖息地的当前与未来分布情况。预计到2070年,现有适宜栖息地的18%(2689 km^2)将变得不适宜亚洲北山羊的生存,损失的区域主要位于研究区域的东南部和西北部地区。新的适宜栖息地可能会扩展到当前亚洲北山羊范围之外:到2070年将扩展30%(4595 km^2)的范围,这些区域与亚洲北山羊现有的分布有很强的相关性。东南部的损失与该地区当前大多数的亚洲北山羊栖息地重叠,主要出现在比研究区域海拔低得多的区域(3500–4000 m)。当同时考虑损失和收益时,亚洲北山羊可能会净扩展到新的适宜栖息地。到2070年,亚洲北山羊的平均栖息地增加量约为30%(1379 km^2),表明适宜栖息地已向北部低温栖息地转移。研究结果有助于规划气候变化情景下塔吉克斯坦东部山区对生物多样性保护的潜在影响。应该特别注意东南地区的高地山羊种群,那里的栖息地可能由于气候对山区生态系统的影响而变得不适合该物种继续生存。