In this study a hybrid Pore Network (PN) model that simulates two-phase (water-oil) drainage and imbibition mechanisms is developed. The developed model produces Nuclear Magnetic Resonance (NMR) T2 relaxation times using correlations available in the literature. The developed PN was calibrated using experimental relative permeability data obtained for Berea Sandstone, Kuzey Marmara Limestone, Yenik&ouml / y Dolostone and Dolomitic Limestone core plugs. Pore network body and throat parameters were obtained from serial computerized tomography scans and thin section images. It was observed that pore body and throat sizes were not statistically correlated. It was also observed that the developed PN model can be used to model different displacement mechanisms. By using the synthetic data obtained from PN model, an Artificial Neural Network (ANN) model was developed and tested. It has been observed that the developed ANN tool can be used to estimate oil &ndash / water relative permeability data very well (with less than 0.05 mean square error) given a T2 signal. It was finally concluded that the developed tools can be used to obtain multiphase flow functions directly from an NMR well log such as Combinable Magnetic Resonance (CMR).

This study aims to develop business failure prediction models using the data of selected firms from ISE markets. The sample data comprise ten selected financial ratios for 27 non-going concerns (failed businesses) and paired 27 going concerns. Two non-parametric classification methods are used in the study: Artificial Neural Networks (ANN) and Decision Trees. The classification results show that there is equilibrium in the classification of the training samples by the models, but ANN model outperform the decision tree model in the classification of the testing samples. Further, the potential usefulness of ANN and Decision Tree type data mining techniques in the analysis of complex and non-linear relationships are observed.

In this dissertation, 3D surface seismic data was integrated with the well logs to be able to define the properties in every location for the reservoir under investigation. To accomplish this task, geostatistical and artificial neural networks (ANN) techniques were employed. First, missing log sets in the study area were estimated using common empirical relationships and ANN. Empirical estimations showed linear dependent results that cannot be generalized. On the other hand, ANNs predicted missing logs with an very high accuracy. Sonic logs were predicted using resistivity logs with 90% correlation coefficient. Second, acoustic impedance property was predicted in the study area. AI estimation first performed using sonic log with GRNN and 88% CC was obtained. AI estimation was repeated using sonic and resistivity logs and the result were improved to 94% CC. In the final part of the study, SGS technique was used with collocated cokriging techniques to estimate NPHI property. Results were varying due to nature of the algorithm. Then, GRNN and RNN algorithms were applied to predict NPHI property. Using optimized GRNN network parameters, NPHI was estimated with high accuracy. Results of the study were showed that ANN provides a powerful solution for reservoir parameter prediction in the study area with its flexibility to find out nonlinear relationships from the existing available data.

Chlorophyll-a is a parameter which can be used to understand the trophic state of water bodies. Therefore, monitoring of this parameter is required. Yet, distribution of chlorophyll-a in water bodies is not homogeneous and exhibits both spatial and temporal variations. Therefore, frequent sampling and high sample sizes are needed for the determination of chlorophyll-a quantities. This would in return increase the sampling costs and labor requirement, especially if the topography makes the location hard to reach. Remote sensing is a technology that can aid in handling of these difficulties and obtain a continuous distribution of chlorophyll-a concentrations in a water body. In this method, reflectance from water bodies in different wavelengths is used to quantify the chlorophyll-a concentrations. In previous studies in literature, empirical regression models that use the reflectance values in different bands in different combinations have been derived. Yet, prediction performances of these models decline especially in shallow lakes. In this study, the spatial distribution of chlorophyll-a in shallow Lake Eymir is determined using both regression models and artificial neural network models that use hybrid inputs. Unlike the models generated before, field measured parameters which can influence the reflectance values in remotely sensed images have been used in addition to the reflectance values. The parameters that are considered other than reflectance values are photosynthetically active radiation (PAR), secchi depth (SD), water column depth, turbidity, dissolved oxygen concentration (DO), pH, total suspended solids (TSS), total dissolved organic matter (TDOM), water and air temperatures, wind data and humidity. Reflectance values are obtained from QuickBird and World View 2 satellite images. Effect of using hybrid input in mapping the reflectance values to chlorophyll-a concentrations are studied. In the context of this study, three different high-resolution satellite images are analyzed for the spatial distribution of chlorophyll-a concentration in Lake Eymir. Field and laboratory studies are conducted for the measurement of parameters other than the reflectance values. Principle component analysis is applied on the collected data to decrease the number of model input parameters. Then, linear and non-linear regression and artificial neural network (ANN) models are derived to model the chlorophyll-a concentrations in Lake Eymir. Results indicate that ANN model shows better predictability compared to regression models. The predictability of ANN model increases with increasing variation in the dataset. Finally, it is seen that in determination of chlorophyll-a concentrations using remotely sensed data, models with hybrid inputs are superior compared to ones that use only remotely sensed reflectance values.

A risk assessment model (MAcRisk) have been developed to forecast the probability and the risk of maritime accidents on Bosphorus. Accident archives of Undersecretariat Maritime Affairs Search and Rescue Department, weather conditions data of Turkish State Meteorological Service and bathymetry and current maps of Office of Navigation, Hydrography and Oceanography have been used to prepare the model input and to forecast the accident probability. Accident data has been compiled according to stated sub-regions on Bosphorus and event type of accidents such as collision, grounding, capsizing, fire and other. All data that could be obtained are used to clarify the relationship on accident reasons. An artificial neural network model has been developed to forecast the maritime accidents in Bosphorus.

Along with advancement of human industrialization, pollution in the ocean is getting worse. Moreover, the overfishing through the years has caused catastrophic damage to the ocean eco-system. In order to avoid exhaustion of fishery resource, many concepts of planned administrative fishery has become popular, and thereamong, ocean ranch draws the most attention. Artificial reef plays a key role in an ocean ranch, which starts with incubating brood fish in the laboratory. Often, the brood fish will grow in the cage near coast till proper size, then be released to the artificial reef. If fish groups do not disperse and multiply, the artificial reef can be considered successful. The success of the artificial reef relies on the stable foundation. Consequently, the composition of seabed sediment under the planned site should be investigated thoroughly before hand. This research introduced a remote investigation method, which an active sonar, depth sounder, was used to emit and collect acoustic signals. By using the signals reflected from the seabed, the sediment composition can be analyzed. However, all acoustic signals are subjected to noise through propagation, and distorted somehow. Therefore, certain signal pre-processing should be applied to the received signal, and representative characteristics can be extracted from it. In this research, the recognition platform was built on artificial neural network (ANN) in this research. Among many network algorithm modes, this research chose the widely used backpropagation learning algorithm to be the main structure in ANN. The goal of this research was to discriminate among three seabed sediments: fine sand, medium sand, and rock. During the signal processing, characteristics were extracted by using peak value selection method. Selected major frequency peaks were fed into the network to train and learn. According to partial error relation between recognition and practical result, weights of the network were adjusted for improving successful ratio. Finally, a reliable acoustic wave signal recognition system was constructed.

In the design and plan of the coast engineering, long-term and continual tidal database represent the indispensable role. This paper collect the tidal database, their locations include the ocean around the Taiwan and the South China Sea. Use the artificial neural networks (ANN) to build model and find the relationship between neighbor tidal observation stations. There are many reasons to cause the tide phenomenon, include the tide generating force, season, coastal geography, geography of sea floor, resonance of gulf or estuary, change depth of sea, and so on, it will be determined by local environment. The tide analysis and prediction usually use the harmonic analysis method. This method need long-term and continual tidal record, and the theory depend on the tide generating force, it has limit about accuracy. The application of artificial neural networks is used in nonlinear science problems in general cases. The back propagation (BP) networks is the one model of the artificial neural networks, this paper use ANN-BP model to build the relationship from different tide observed stations, and verify the quality of model. From the result of verified models, the ANN-BP model can predict and supplement the tide record very well. The items of research include: ¡i1¡j the relationship between two neighbor tide observed stations. (one station input, one station output) ¡i2¡jthe relationship between three neighbor tide observed stations. (two station input, one station output) ¡i3¡j input several tide observed stations and output one station. ¡i4¡j the correlation of connected weight and threshold between different models. ¡i5¡j change the parameters of ANN-BP model and discus the affect of model¡¦s quality. ¡i6¡j application of truly case. From the result of this paper, in the design and plan of the coast engineering, the long-term tide observed record can be predict from the ANN-BP model and tide record of neighbor observed stations. When the tide record has miss or lost cause by machine or other reasons, the ANN-BP model can supplement the lost tide record well. This paper show the ANN-BP model can be apply to predict and supplement the tide record very well, and will be possible applied method.

Power system protection is important for service reliability and quality assurance. Various faults may occur due to natural and artificial calamity. To reduce the outage duration and promptly restore power services, fault section estimate has to be done effectively with appeared fault alarms. Dispatchers could study the changed statuses of primary/back-up relays and circuit breakers to identify the fault section and fault types. It is difficult to process too many alarms under various conditions in a large power system. Single fault, multiple faults, single and multiple faults could coexist with the failed operation of relays and circuit breakers, or with the erroneous data communication. Dispatchers need more time to process the many uncertainties before identifying the fault. This thesis presents the use of artificial intelligence for fault section detection in substation with neural networks. Probabilistic Neural Networks (PNN) are proposed for fault detection system in substation. The proposed methodology will use primary/back-up information of protective relays and circuit breakers to detect the fault sections involving single fault, multiple faults, or fault with the failure operation of the relays and circuit breakers. This paper also presents a fuzzy theory-based method to identify fault types. It is derived to improve the inadequacy of making decisions by selecting a fixed threshold value and has the capability of non-deterministic decision making with a prior knowledge of uncertainties in fault location, fault resistance and the a size of loads. The proposed approach has been tested on a typical taipower system with accurate results.

The technology of power electronics is used increasingly during recent years, and the electronic power facilities are used more and more in the power system. The non-linear electronic loads produce heavy harmonic currents and could significantly degrade the power quality. Nonlinear loads, including the un-interruptible power supply, motor control and converter, etc, are important equipment in a modern factory, however, these nonlinear loads could lead to power facility malfunction and capacitor damage. The harmonics would eventually cause severe unexpected capital loss. Identification of harmonic sources location becomes an important study for power quality. An effective tool is thus helpful for the harmonic source locating. This paper proposes a method to deal with the harmonic sources and location detection in the power system by using the artificial neural network (ANN). The non-linear loading characteristics are studied by the power flow analysis, and then the proposed methodology uses the Probabilistic Neural Networks¡]PNN¡^and wavelet-probabilistic network (WPN) for harmonic source locating. An IEEE 14-bus power system is used for study to show the effectiveness of the proposed approach.

Man-made farm ponds are unique geographic features of the Taoyuan Tableland. Besides irrigation, they provide refuges for wintering birds. The issue at hand is that these features are disappearing and bring with it the loss of this refuge function. It is ecologically significant because one fifth of all the bird species in Taiwan find a home on these ponds. This study aims at characterizing the diversity of bird species associated with these ponds whose likelihood of survival was assessed along the gradient of land development intensities. Such characterization helps establish decision criteria needed for designating certain ponds for habitat preservation and developing their protection strategies. A holistic model was developed by incorporating logistic regression with error back-propagation into the paradigm of artificial neural networks (ANN). The model considers pond shape, size, neighboring farmlands, and developed areas in calculating parameters pertaining to their respective and interactive influences on avian diversity, among them the Shannon-Wiener diversity index (H’). Results indicate that ponds with regular shape or the ones with larger size possess a strong positive correlation with H’. Farm ponds adjacent to farmland benefited waterside bird diversity. On the other hand, urban development was shown to cause the reduction of farmland and pond numbers, which in turn reduced waterside bird diversity. By running the ANN model with four neurons, the resulting H’ index shows a good-fit prediction of bird diversity against pond size, shape, neighboring farmlands, and neighboring developed areas with a correlation coefficient (r) of 0.72, in contrast to the results from a linear regression model (r < 0.28). Analysis of historical pond occurrence to the present showed that ponds with larger size and a long perimeter were less likely to disappear. Smaller (< 0.1 ha) and more curvilinear ponds had a more drastic rate of disappearance. Based on this finding, a logistic regression was constructed to predict pond-loss likelihood in the future and to help identify ponds that should be protected. Overlaying results from ANN and form logistic regression enabled the creation of pond-diversity maps for these simulated scenarios of development intensities with respective to pond-loss trends and the corresponding dynamics of bird diversity.