A multiscale spatio-temporal framework to regionalize annual precipitation using k-means and self-organizing map technique

Determination of homogenous precipitation-based regions is a very important task in effective management of water resources. The present study tried to propose an effective precipitation-based regionalization methodology by conjugating both temporal pre-processing and spatial clustering approaches in a way to take advantage of multiscale properties of precipitation time series. Annual precipitation data of 51 years(1960-2010) for 31 rain gauges(RGs) were collected and used in proposed clustering approaches. Discreet wavelet transform(DWT) was used to capture the time-frequency attributes of the time series and multiscale regionalization was performed by using k-means and Self Organizing Maps(SOM) clustering techniques. Daubechies function(db) was selected as mother wavelet to decompose the precipitation time series. Also, proper boundary extensions and decomposition level were applied. Different combinations of the approximation(A) and detail(D) coefficients were used to determine the input dataset as a basis of spatial clustering. The proposed model's efficiency in spatial clustering stage was verified using three different indexes namely, Silhouette Coefficient(SC), Dunn index and Davis Bouldin index(DB). Results approved superior performance of k-means technique in comparison to SOM. It was also deduced that DWT-based regionalization methodology showed improvements in comparison to historical-based models. Cross mutual information was used to investigate the RGs of cluster 3's homogeneousness in DWT-k-means approach. Results of non-linear correlation approach verified homogeneity of cluster 3. Verifications based on mean annual precipitation values of rain gauges in each cluster also approved the capability of multiscale approach in precipitation regionalization.

Application of Self-Organizing Map for Exploration of REEs’ Deposition

Varieties of approaches and algorithms have been presented to identify the distribution of elements. Previous researches based on the type of problem, categorized their data in proper clusters or classes. This means that the process of solution could be supervised or unsupervised. In cases, where there is no idea about dependency of samples to specific groups, clustering methods (unsupervised) are applied. About geochemistry data, since various elements are involved, in addition to the complex nature of geochemical data, clustering algorithms would be useful for recognition of elements distribution. In this paper, Self-Organizing Map (SOM) algorithm, as an unsupervised method, is applied for clustering samples based on REEs contents. For this reason the Choghart Fe-REE deposit (Bafq district, central Iran), was selected as study area and dataset was a collection of 112 lithology samples that were assayed with laboratory tests such as ICP-MS and XRF analysis. In this study, input vectors include 19 features which are coordinates x, y, z and concentrations of REEs as well as the concentration of Phosphate (P2O5) since the apatite is the main source of REEs in this particular research. Four clusters were determined as an optimal number of clusters using silhouette criterion as well as k-means clustering method and SOM. Therefore, using self-organizing map, study area was subdivided in four zones. These four zones can be described as phosphate type, albitofyre type, metasomatic and phosphorus iron ore, and Iron Ore type. Phosphate type is the most prone to rare earth elements. Eventually, results were validated with laboratory analysis.

Hidden Markov Models and Self-Organizing Maps Applied to Stroke Incidence

Several studies were devoted to investigate the effects of meteorological factors on the occurrence of stroke. Regression models had been mostly used to assess the correlation between weather and stroke incidence. However, these methods could not describe the process proceeding in the back-ground of stroke incidence. The purpose of this study was to provide a new approach based on Hidden Markov Models (HMMs) and self-organizing maps (SOM), interpreting the background from the viewpoint of weather variability. Based on meteorological data, SOM was performed to classify weather patterns. Using these classes by SOM as randomly changing “states”, our Hidden Markov Models were constructed with “observation data” that were extracted from the daily data of emergency transport at Nagoya City in Japan. We showed that SOM was an effective method to get weather patterns that would serve as “states” of Hidden Markov Models. Our Hidden Markov Models provided effective models to clarify background process for stroke incidence. The effectiveness of these Hidden Markov Models was estimated by stochastic test for root mean square errors (RMSE). “HMMs with states by SOM” would serve as a description of the background process of stroke incidence and were useful to show the influence of weather on stroke onset. This finding will contribute to an improvement of our understanding for links between weather variability and stroke incidence.

APPLICATION OF FUZZY LOGIC AND SELF-ORGANIZING NETWORK TO TOOL-WEAR CLASSIFICATION

A tool-wear monitoring system for metal turning operations is presented based on the combinative application of fuzzy logic and unsupervised neural network. A group of self-organizing map （SOM） neural networks is established based on the typical cutting condition combinations, and each of networks is corresponding to a typical cutting condition. For a specifie cutting condition, the fuzzy logic method is used to select an optimum trained SOM network. The proposed monitoring system, ealled the Fuzzy-SOM-TWC, is used to classify tool states based on the in-time measurement of force, aeoustic emission（AE）, and motor eurrent signals. An approximate 98%--100% correct classification of tool-wear status is obtained by testing the system with a series data samples under freely selected cutting conditions.

Improved clustering method based on artificial immune

An improved clustering method based on artificial immune is proposed. To obtain the better initial solution, the initial antibody network is introduced by self organizing map （SOM） method. In the process of the clustering iteration, a series of optimization and evolution strategies are designed, such as clustering satisfaction, the threshold design of scale compression, the learning rate, the clustering monitoring points and the clustering evaluations indexes. These strategies can make the clustering thresholds be quantified and reduce the operator’s subjective factors. Thus, the local optimal and the global optimal clustering simultaneously are proposed by the synthesized function of these strategies. Finally, the experiment and the comparisons demonstrate the proposed method effectiveness.

Morphological self-organizing feature map neural network with applications to automatic target recognition

The rotation invariant feature of the target is obtained using the multi-direction feature extraction property of the steerable filter. Combining the morphological operation top-hat transform with the self-organizing feature map neural network, the adaptive topological region is selected. Using the erosion operation, the topological region shrinkage is achieved. The steerable filter based morphological self-organizing feature map neural network is applied to automatic target recognition of binary standard patterns and real world infrared sequence images. Compared with Hamming network and morphological shared-weight networks respectively, the higher recognition correct rate, robust adaptability, quick training, and better generalization of the proposed method are achieved.

Hidden Markov Models to Estimate the Lagged Effects of Weather on Stroke and Ischemic Heart Disease

The links between low temperature and the incidence of disease have been studied by many researchers. What remains still unclear is the exact nature of the relation, especially the mechanism by which the change of weather effects on the onset of diseases. The existence of lag period between exposure to temperature and its effect on mortality may reflect the nature of the onset of diseases. Therefore, to assess lagged effects becomes potentially important. The most of studies on lags used the method by Lag-distributed Poisson Regression, and neglected extreme case as random noise to get correlations. In order to assess the lagged effect, we proposed a new approach, i.e., Hidden Markov Model by Self Organized Map (HMM by SOM) apart from well-known regression models. HMM by SOM includes the randomness in its nature and encompasses the extreme cases which were neglected by auto-regression models. The daily data of the number of patients transported by ambulance in Nagoya, Japan, were used. SOM was carried out to classify the meteorological elements into six classes. These classes were used as “states” of HMM. HMM was used to describe a background process which might produce the time series of the incidence of diseases. The background process was considered to change randomly weather states, classified by SOM. We estimated the lagged effects of weather change on the onset of both cerebral infarction and ischemic heart disease. This fact is potentially important in that if one could trace a path in the chain of events leading from temperature change to death, one might be able to prevent it and avert the fatal outcome.

Clustering Seismic Activities Using Linear and Nonlinear Discriminant Analysis

Identification and classification of different seismo-tectonic events with similar character- istics in a region of interest is one of the most important subjects in seismic hazard studies. In this study, linear and nonlinear discriminant analyses have been applied to classify seismic events in the vicinity of Istanbul. The vertical components of the digital velocity seismograms are used for seismic events with magnitude （Md） between 1.8 and 3.0 that occurred between 2001 and 2004. Two, time dependent pa- rameters, complexity and S/P peak amplitude ratio are selected as predictands. Linear, quadratic, diag- linear and diagquadratic discriminant functions are investigated. Accuracy of methods with an addi- tional adjusted quadratic models are 96.6%, 96.6%, 95.5%, 96.6%, and 97.6%, respectively with a vari- ous misclassified rate for each class. The performances of models are justified with cross validation and resubstitution error. Although all models remarkably well performed, adjusted quadratic function achieved the best success rate with just 4 misclassified events out of 179, even better compared to com- plex methods such as, self organizing method, k-means, Gaussion mixture models that applied to same dataset in literature.

Synoptic mode of Antarctic summer sea ice superimposed on interannual and decadal variability

In contrast to decreased Arctic sea ice extent,Antarctic sea ice extent shows a somewhat increased trend.There is a large interannual variability of Antarctic sea ice,especially in the Pacific sector of the Southern Ocean.The change and variability of Antarctic sea ice in synoptic timescales in the recent decades remain unclear.We identify synoptic modes of variability of Antarctic summer sea ice by applying the Self Organizing Map(SOM)technique to daily sea ice concentration data for the period 1979–2018.Nearly 40%of the variability is characterized by opposite changes between sea ice cover in the Bellingshausen,Amundsen and western Ross Seas and in the rest of the Antarctic seas,and another 30%by meridional asymmetry in the Weddell,Amundsen,and Ross Seas.Most of these spatial patterns may be explained by the dynamics and thermodynamic processes associated with anomalous atmospheric circulations related to the Southern Annular Mode(SAM)with a structure of strong zonal asymmetry.The interannual variability of the sea ice modes appears to have little connection to SAM,and only a weak relation to ENSO.The annual frequencies of SOM node occurrences also show a great decadal variability.Node 9 appears mainly prior to 1990;while node 1 occurs mainly after 1990.The decadal variability of nodes 1 and 9 is associated with the asymmetrical SAM,which results from two wavetrains excited over northern Australia and the southeastern Indian Ocean.These results further highlight the importance of understanding the role of southern mid-to-high latitude atmospheric intrinsic variability in predicting Antarctic summer sea ice variations from synoptic to decadal timescales.

Competitive Learning Approach to GIS Based Land Use Suitability Analysis

This paper uses the expected utility under risk hypothesis to develop a new approach to GIS modeling for land use suitability analysis with competitive learning algorithms （CLG-LUSA）. It uses Kohonen＇s Self Organ- ized Maps （SOM） and Linear Vector Quantization （LVQ） among other tools to create comprehensive ordering of high number of options. The model uses decision makers preferred locations and environmental data to construct a manifold of the decision＇s attribute space. Then, decision and uncertainty maps are derived from this manifold. An application example is provided using the selection of suitable environments for coconut development in a mu- nicipality of Cuba. CLG-LUSA model was able to provide accurate visual feedback of key aspects of the decision process, making the methodology suitable for personal or group decision making.