Artificial neural networks and cluster analysis in landslide susceptibility zonation

A landslide susceptibility analysis is performed by means of Artificial Neural Network (ANN) and Cluster Analysis (CA). This kind of analysis is aimed at using ANNs to model the complex non linear relationships between mass movements and conditioning factors for susceptibility zonation, in order to identify unstable areas. The proposed method adopts CA to improve the selection of training, validation, and test records from data, managed within a Geographic Information System (GIS). In particular, we introduce a domain-specific distance measure in cluster formation. Clustering is used in data pre-processing to select non landslide records and is performed on the whole dataset, excluding the test set landslides. Susceptibility analysis is carried out by means of ANNs on the so-generated data and compared with the common strategy to select random non-landslide samples from pixels without landslides. The proposed method has been applied in the Brembilla Municipality, a landslide-prone area in the Southern Alps, Italy. The results show significant differences between the two sampling methods: the classification of the test set, previously separated and excluded from the training data, is always better when the non-landslide patterns are obtained using the proposed cluster sampling. The case study validates that, by means of a domain-specific distance measure in cluster formation, it is possible to introduce expert knowledge into the black-box modelling method, implemented by ANNs, to improve the predictive capability and the robustness of the models obtained.     

Amelioration of Carbon Removal Prediction for an Activated Sludge Process using an Artificial Neural Network (ANN)

A dynamic simulation model of the Ankara central wastewater treatment plant (ACWTP) was evaluated for the prediction of effluent COD concentrations. Firstly, a mechanistic model of the municipal wastewater treatment process was developed based on Activated Sludge Model No. 1 (ASM1) by using a GPS‐X computer program. Then, the mechanistic model was combined with a feed‐forward back‐propagation neural network in parallel configuration. The appropriate architecture of the neural network models was determined through several iterative steps of training and testing of the models. Both models were run with the data obtained from the plant operation and laboratory analysis to predict the dynamic behavior of the process. Using these two models, effluent COD concentrations were predicted and the results were compared for the purpose of evaluation of treatment performance. It was observed that the ASM1 ANN model approach gave better results and better described the operational conditions of the plant than ASM1.     

河南省数字地震台网自动量算的震级与人工量算的震级的偏差分析

对2007年12月-2008年9月河南省数字地震台网资料自动量算的震级与人工量算的震级进行了比较和分析。结果表明：由MSDP软件自动量算的震级与人工量算的震级偏差较小,且自动量算的震级可以满足地震定位精度的要求,在地震速报时可采用自动量算的震级。     

平台地形对地震地面运动特征周期值的影响

本文人工合成了36条代表不同频谱特性的地震动,构造了简单的平台地形,并利用人工合成地震动作为平台地形计算输入地震波,获得了地表观测点的时程和反应谱.在此基础上,分析了具有不同高度、侧向坡降和介质阻尼等的平台地形对地震动特征周期值的影响.研究的结果表明:当平台高度与入射地震波优势波长相比较小时,平台的高度、侧向坡降、阻尼比等对地震动特征周期值的影响不大,此时单个平台地形地表地震动的特征周期主要依赖于入射地震动的特征周期,而且一般比入射地震动的特征周期略有增大;当平台高度与入射地震波优势波长相比较大时,平台高度对地表地震动特征周期影响较大.     

Evaluation of total load sediment transport formulas using ANN

The calculated results from various sediment transport formulas often differ from each other and from measured data. Some parameters in the sediment transport formulas are more effective than others to estimate total sediment load. In this study, an Artificial Neural Network （ANN） model is trained using four dominant parameters of sediment transport formulas. ANN models are able to reveal hidden laws of natural phenomena such as sediment transport process. The results of ANN and some total bed material load sediment transport formulas have been compared to indicate the importance of variables which can be used in developing sediment transport formulas. To train ANN, average flow velocity, water surface slopes, average flow depth, and median particle diameter are used as dominant parameters to estimate total bed material load. Two hundreds and fifty samples are used to train the ANN model. Twenty-four sets of field data not used in the training nor calibration of ANN are used to compare or verify the accuracy of ANN and some well-known total bed material load formulas. The test results show that the ANN model developed in this study using minimum number of dominant factors is a reliable and uncomplicated method to predict total sediment transport rate or total bed material load transport rate. Results show that the accuracy of formulas in descending order are those by Yang （1973）, Laursen （1958）, Engelund and Hansen （1972）, Ackers and White （1973）, and Toffaleti （1969）. These results are similar to those made by ASCE （1982） based on laboratory and field data not used in this paper. Study results also show that the formulas based on physical laws of sediment transport, like those formulas that were developed based on power concept, are more accurate than other formulas for estimating total bed material sediment load in rivers.     

Estimation of river flow by artificial neural networks and identification of input vectors susceptible to producing unreliable flow estimates

Reliable river flow estimates are crucial for appropriate water resources planning and management. River flow forecasting can be conducted by conceptual or physical models, or data-driven black box models. Development of physically-based models requires an understanding of all the physical processes which impact a natural process and the interactions among them. Since identification of the relationships among these physical processes is very difficult, data-driven approaches have recently been utilized in hydrological modeling. Artificial neural networks are one of the widely used data-driven approaches for modeling hydrological processes. In this study, estimation of future monthly river flows for Guvenc River, Ankara is conducted using various artificial neural network models. Success of artificial neural network models relies on the availability of adequate data sets. A direct mapping from inputs to outputs without consideration of the complex relationships among the dependent and independent variables of the hydrological process is identified. In this study, past precipitation, river flow data, and the associated month are used to predict future river flows for Guvenc River. Impacts of various input patterns, number of training cycles, and initial values assigned to the weights of the connections are investigated. One of the major weaknesses of artificial neural networks is that they may fail to generate good estimates for extreme events, i.e. events that do not occur at all or often enough in the training data set. It is very important to be able to identify such unlikely events. A fuzzy c-means algorithm is used in this study to cluster the training and validation input vectors into regular and extreme events so that the user will have an idea about the risk of the artificial neural network model to generate unreliable results.     

Benthic ecosystem development in an artificial tidal flat constructed from dredged spoil

Dredged spoil (DS) was used as a silt and clay additive in the construction of artificial tidal flats from mountain sand (MS). As the ratio of DS in the sediment media increased, the number of emerging macrobenthos increased. The composition of the macrobenthic community was also affected by the addition of DS, and the changes might be dependent on the ratio of DS to MS. In addition, the macrobenthos in the artificial tidal flats was more abundant than that in the control tidal flat, which was constructed with natural tidal flat sediment. With a silt and clay content of 25%, polychaetes Ceratonereis erythraeensis and Capitella sp. and the gastropod Batillaria cumingii were dominant, whereas no bivalves were present. With less silt and clay (5% and 10%), the bivalves Ruditapes philippinarum and Musculista senhousia were observed in the artificial flats, while their numbers in the control tidal flat were lower.     

温度对东北草原区亚洲飞蝗孵化的影响

为了阐明东北草原亚洲飞蝗孵化进程与热量条件的关系,利用人工气候箱进行了温度和积温对亚洲飞蝗越冬卵孵化影响的试验研究。结果表明：出蝻数、出蝻率、累积出蝻数、累积出蝻率均随日平均气温升高而增大。日最低温度连续3 d稳定通过25 ℃时,蝗虫开始萌动、孵化;在26.0 ℃左右时孵化最快。低于25.0 ℃时,蝗虫出蝻速率缓慢。25.0 ℃以上有效积温达到11.6 ℃•d,活动积温达到211.6 ℃•d时,蝗卵开始孵化出土。当有效积温增加到20 ℃•d,活动积温320 ℃•d后,孵化速度回落,紧接着达到第二个孵化小高峰,然后孵化过程结束。     

Performance assessment of genetic programming (GP) and minimax probability machine regression (MPMR) for prediction of seismic ultrasonic attenuation

The determination of seismic attenuation (s) (dB/cm) is a challenging task in earthquake science. This article employs genetic programming (GP) and minimax probability machine regression (MPMR) for prediction of s. GP is developed based on genetic algorithm. MPMR maximizes the minimum probability of future predictions being within some bound of the true regression function. Porosity (n) (%), permeability (k) (millidarcy), grain size (d) (μm), and clay content (c) (%) have been considered as inputs of GP and MPMR. The output of GP and MPMR is s. The developed GP gives an equation for prediction of s. The results of GP and MPMR have been compared with the artificial neural network. This article gives robust models based on GP and MPMR for prediction of s.     

用人工源和天然源面波联合探测浅层速度结构.

本文在简要介绍天然源与人工源瑞雷面波勘探基本原理、数据采集和资料处理方法的基础上,结合3个不同场地的探测实例,阐述了天然源和人工源瑞雷面波方法在浅部速度结构探测中的应用效果.结果表明,根据不同的场地条件和探测目的要求,分别采用天然源、人工源瑞雷面波方法提取瑞雷波频散曲线,再用遗传算法反演得到工程场地浅部地层横波速度结构的技术方法是有效和可行的.该方法对于类似工程的浅部横波速度结构探测具有经济适用、简便快捷的优点.