Top PDF A New Approach of Expert System for Rainfall Prediction Based on Data Series

A New Approach of Expert System for Rainfall Prediction Based on Data Series

A New Approach of Expert System for Rainfall Prediction Based on Data Series

Fuzzy inference systems, also known as fuzzy rule-based systems or fuzzy models, areschematically shown in Fig.2.They are composed of five conventional blocks;arule- basecontaining a number of fuzzy if-then rules, adatabasewhich defines the membershipfunctions of the fuzzy sets used in the fuzzy rules, adecision- making unitwhich performs the inference operations on the rules, afuzzification interfacewhich transform thecrisp inputs into degrees of match with linguistic values, adefuzzification interfacewhichtransform the fuzzy results of the inference into a crisp output.Fig.3 shows the example of temperature membership function.
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A new splitting based displacement prediction approach for location based services

A new splitting based displacement prediction approach for location based services

The simulations are done over Pentium IV computers with 2 GB RAM and CPU speed of 3 GHz. The operating system used was Windows XP, where the LAN speed was 100 Mbps. A simulator was created using Java programming language for the SDPA, in which the algorithm based on Markov Chain models is implemented and tested. The number of cells in the simulated experiments varies between one, two, three, five, fifty and one hundred cells with a fixed radius of 250m each. The movement is recorded to train the program to learn how the mobile user moves during different trips. Different samples of data are used to test the performance of the SDPA.
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A data-driven approach using deep learning time series prediction for forecasting power system variables

A data-driven approach using deep learning time series prediction for forecasting power system variables

Figure 2. The schematic structure of GMDH neural network In respect of time series, the GMDH algorithm learns the relationship among the lags with the function f which is given in (2). The proposed stochastic approximation algorithm is based on a multilayer structure using various component subsets of the polynomial function for each layer; in the way that the output obtained from the last layer will be set as a new input variable for the next layer. The architecture is shown schematically in Fig. 2. All possible tries of two independent variables are taken out of a total n inputs to build a regression polynomial in the form of (2) in the first layer. Therefore, the activation function is the second-order polynomial but it can be gradually increased to higher orders to find an architecture with an optimal complexity. The number of solutions will be restricted by a threshold value of the external criterion to find the fittest structure. The parameters are calculated by using a least- squares estimation.
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An Expert System Approach to Medical Region Selection for a New Hospital Using Data Envelopment Analysis

An Expert System Approach to Medical Region Selection for a New Hospital Using Data Envelopment Analysis

This means that all individuals have equal access to medical care. Access not only indicates the convenience of obtaining medical care for all individuals, but also demonstrates the relationship between people and sites of medical care. For example, access involves distances and traffic problems [22]. Equality of access thus improves with decreasing distance. This study measures the equa- lity of access based on the percentage of doctors, the length of road, and the percentage of the people able to obtain medical treatment within an acceptable time, with a higher value representing higher accessibility. Action [23], Coffey [24] and Cauley [25] indicated that the time taken to get medical treatment influences consumers’ medical demand. When the distance and time to obtain medical treatment are long, then the demand for medical treatment is very low. Using a questionnaire survey, one can identify the time delay that consumers find accepta- ble for obtaining medical treatment. When a medical institute chooses a medical area with lower accessibility, it is in agreement with D’Aspremont et al. [26], who indicated that factory dealers try their best to be far away from each other, the so-called principle of maximum differentiation. In addition, by choosing a medical area with lower accessibility, consumer demand will also in- crease, and there will not be much competition for medi- cal facilities.
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A Belief Rule Based Expert System for Datacenter PUE Prediction under Uncertainty

A Belief Rule Based Expert System for Datacenter PUE Prediction under Uncertainty

Predictive modelling in data centers will be considered as the ”next frontier for condition maintenance“ [54] and according to Koomey [55], predictive modelling in the context of a data center, could ”unlock stranded capacity and identify practices for higher efficiency and reliability“. Google estimates the use of AI for predictive modelling could reduce energy use within a data center by 15% [56]. Romonet has developed a set of Prognose software suite which could be employed to for predictive modelling of energy and capacity use within data centers [57]. This model uses PUE to determine energy efficiency of data centers. The Prognose simulator includes the following tools: add infrastructure devices, change parameters of devices, load IT devices, add additional information (e.g. energy tariff, climate data, etc.), monitoring tools to provide down to minute-granularity data whenever necessary, data storage facility (in a Prognose database). The acronymn DCPM (data center predictive modelling) has been ascribed this data center predictive modelling suite by Romonet [ibid] and the demonstration of the software capability has been discussed in [58] [59]. In brief, DCPM refers to the capability to forecast the performance of a data center in terms or energy consumption, energy efficiency, etc. [57]. However, the model employed for energy use prediction based on various parameter changes (e.g. change IT equipment load, fan speed controls of CRAH, and number of water-side economizers, etc.) will have to be run in successive iterations [58] [59]. Thermal News [54] provides a very coherent picture of three sets of intertwined variables whose trade- offs contribute to the overall performance of a data center: IT availability (in terms of % of load and failure); physical capacity (i.e. amount of design capacity that is available for use and scalability) and cooling efficiency (i.e. efficiency of cooling systems). On the other hand, Google has success- fully run trials on the use of machine learning applications (using neural network) for data center optimization which takes into considerations the complex interaction of three primary systems within a data center: mechanical, electrical, and control [60]. Their results reveal that machine learning is an effective of leveraging existing sensor data to model a data center performance and improve energy efficiency. On the other hand, IBM has launched its new predictive modelling suite which primarily focuses on the business side of a data center: operational changes with growing business demands; cash flow analysis, physical threshold capacity; resiliency rationalization to support data center planning and management.
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A Decentralized Approach for Nonlinear Prediction of Time Series Data in Sensor Networks

A Decentralized Approach for Nonlinear Prediction of Time Series Data in Sensor Networks

The new approach is based on a kernel machine but con- trols the model order through a coherence-based criterion that reduces spatial redundancy. It also employs sensor-to- sensor communication, and thus is robust to single sensor failures. The paper is organized as follows. The next section briefly reviews functional learning with kernel machines and addresses its limitations within the context of wireless sensor networks. It is shown how to overcome these limi- tations through a model order reduction strategy. Section 3 describes the proposed algorithm and its application to instantaneous functional estimation and tracking. Section 4 addresses implementation issues in sensor networks. Finally, we report simulation results in Section 5 to illustrate the applicability of the proposed approach.
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A Belief Rule Based Expert System for Datacenter PUE Prediction under Uncertainty

A Belief Rule Based Expert System for Datacenter PUE Prediction under Uncertainty

Predictive modelling in data centers will be considered as the ”next frontier for condition maintenance“ [54] and according to Koomey [55], predictive modelling in the context of a data center, could ”unlock stranded capacity and identify practices for higher efficiency and reliability“. Google estimates the use of AI for predictive modelling could reduce energy use within a data center by 15% [56]. Romonet has developed a set of Prognose software suite which could be employed to for predictive modelling of energy and capacity use within data centers [57]. This model uses PUE to determine energy efficiency of data centers. The Prognose simulator includes the following tools: add infrastructure devices, change parameters of devices, load IT devices, add additional information (e.g. energy tariff, climate data, etc.), monitoring tools to provide down to minute-granularity data whenever necessary, data storage facility (in a Prognose database). The acronymn DCPM (data center predictive modelling) has been ascribed this data center predictive modelling suite by Romonet [ibid] and the demonstration of the software capability has been discussed in [58] [59]. In brief, DCPM refers to the capability to forecast the performance of a data center in terms or energy consumption, energy efficiency, etc. [57]. However, the model employed for energy use prediction based on various parameter changes (e.g. change IT equipment load, fan speed controls of CRAH, and number of water-side economizers, etc.) will have to be run in successive iterations [58] [59]. Thermal News [54] provides a very coherent picture of three sets of intertwined variables whose trade- offs contribute to the overall performance of a data center: IT availability (in terms of % of load and failure); physical capacity (i.e. amount of design capacity that is available for use and scalability) and cooling efficiency (i.e. efficiency of cooling systems). On the other hand, Google has success- fully run trials on the use of machine learning applications (using neural network) for data center optimization which takes into considerations the complex interaction of three primary systems within a data center: mechanical, electrical, and control [60]. Their results reveal that machine learning is an effective of leveraging existing sensor data to model a data center performance and improve energy efficiency. On the other hand, IBM has launched its new predictive modelling suite which primarily focuses on the business side of a data center: operational changes with growing business demands; cash flow analysis, physical threshold capacity; resiliency rationalization to support data center planning and management.
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Determining Growing Season of Potatoes Based on Rainfall Prediction Result Using System Dynamics

Determining Growing Season of Potatoes Based on Rainfall Prediction Result Using System Dynamics

Potato has been and is a basic food for many countries. However, because of the uncertainty in rainfall patterns that have occurred since the existence of climate change make a significant impact on the outcome of potatoes production from year to year. Therefore, it needs the determination of new growing season period according to climate change. The determination of growing season is based on the result of rainfall prediction data using system dynamics ever done in previous studies to predictions of rainfall during the next five years starting in 2017-2021. Based on the modeling that has been done shows that early dry season ranges in mid-April to mid-May by the length of days in the growing season ranges from 162-192 days. The growing season prediction model has small error only about two dasarian. By the middle of the dry season, rainfall is expected to be very low which will make the potatoes into water deficit and will affect the harvest of potatoes plants which can be overcome with the irrigation system.
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An improved multilayer perceptron based on wavelet approach for physical time series prediction

An improved multilayer perceptron based on wavelet approach for physical time series prediction

3 (Huang et. al., 1998; Brockwell & Davis, 2009; Wang et. al., 2013). That is, the traditional 3-point or 5-point moving average method as an initial technique to smooth the data (Stafford, 2010). Empirical Mode Decomposition inclusive of Low Pass Filtering, High Pass Filtering and Band Pass Filtering (Wu & Norden, 2009). On the other hand, wavelet analysis is a popular filtering and pre-processing technique used to overcome noise, outliers and periodicities in time series data (Cheng, 2008; Marczak &Gomez, 2012).Haar (1909) was interested in finding a basis on a functional space similar to Fourier's basis in frequency space. In physics, wavelets were used in the characterisation of Brownian motion. This work led to some of the ideas used to construct wavelet bases. Wavelets were also used for analysis of coherent states of a particular quantum system. Finally, in the signal processing field, Mallat (1989) discovered that filter banks have important connections with wavelet basis functions.
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Rainfall Prediction based on Ensemble Model

Rainfall Prediction based on Ensemble Model

In this paper, an attempt is made to analyze the time series data set to forecast rain precisely than the existing models. Hence ensemble models such as AdaSVM and AdaNaive are developed using AdaBoost technique. The object of the AdaBoost technique is that it focuses on the weak learners who are hard to learn. SVM when joined with AdaBoost (AdaSVM) will make superior classification by giving weak learners with appropriate training. A like approach is used for Naive Bayes classifier in which AdaBoost based Naive Bayes (AdaNaive) is used to generate better classified data. Fig 1 depicts the proposed work classifying the mass of input rainfall data by using SVM and Naive Bayes modeling techniques. It may be seen that it is further classified in order to improve the performance with the help of AdaBoost+SVM (AdaSVM) and AdaBoost+Naive Bayes (AdaNaive). Both are subjected to comparison and evaluation.
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A methodological approach to characterise Landslide Periods based on historical series of rainfall and landslide damage

A methodological approach to characterise Landslide Periods based on historical series of rainfall and landslide damage

A case study performed for a 39-year series of data con- cerning a study area located in NE Calabria (Italy) allows us to identify four main types of LPs that affected the study area in the past. Although the trend of landslide damage seems to be substantially decreasing from 1959 to 1998 (the period for which rainfall data are available), we hypothesise that, if new landslide periods occur in the area, they will show the same features as for past cases. This can be hypothesised because the study area did not undergo strong anthropogenic modifications in recent years, so the number and location of vulnerable elements is unchanged. On the contrary, in areas characterised by strong recent anthropogenic development, it must be taken into account that urbanisation can increase the landslide susceptibility because of terrain modifications tied to the construction of roads and buildings. In addition, even if no pejorative modifications are carried out, the urban expansion can often be performed in landslide prone areas. This increases the number of vulnerable elements and, dur- ing LPs, can amplify landslide damage.
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ECG Beat Diagnosis Approach for ECG Printout Based on Expert System

ECG Beat Diagnosis Approach for ECG Printout Based on Expert System

Since the line of ECG trace of original scanned image from ECG printout has a thickness which is a redundant of data in time series domain. Then thinning process with Parallel skeletonization algorithm 1 is used to eliminate this redundant of data a binary digitized drawing can be defined as a matrix Q, where each element, q [i, j], is either 0 (dark point) or 1 (white point) and these points are pixels. The 8- neighbors of a pixel p are identified by the eight directions shown in Figure 4. The four pixels, p [0], p [2], p [4] and p[6] {i.e. north(p), east(p), south(p), and west(p)}, are called the direct neighbors. The four pixels, p[l], p [3], p [5], and p [7] {i.e., north-east (p), south-east (p), south-west (p), and north-west (p)}.
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A Comprehensive Survey of Data Mining Techniques on Time Series Data for Rainfall Prediction

A Comprehensive Survey of Data Mining Techniques on Time Series Data for Rainfall Prediction

Nanda, et al. [29] worked on various artificial neural network models, including Multi Layer Perceptron (MLP), Functional Link Artificial Neural Network (FLANN) and Legendre Polynomial Equation (LPE). They observed that MLP, FLANN and LPE performed better for time series data prediction. In their work, the authors proposed an ARIMA-based approach with ANN. A simulation study was carried out using MATLAB and was validated using data collected from India Meteorological Department covering June to September 2012. The authors claim that the FLANN predictions were better and closer in comparison to ARIMA with less Absolute Average Percentage Error (AAPE). Sethi, et al. [30] introduced a multiple linear regression (MLR) technique for rainfall prediction. They followed an empirical statistical technique and used 30 years of climate data from Udaipur City, Rajasthan India. The climate data included average temperature, rainfall precipitation, cloud cover over the city, and vapor pressure. The authors performed an experiment to evaluate the rainfall prediction accuracy. To identify the quality of the MLR they compared the prediction with actual data. With the help of graphs the authors showed that their method generates values that are close to the actual results. Prasad and Neeraj [31] conducted a study on weather prediction using data covering 9 years for Basra City. They used data mining techniques such as association rule mining, aggregation, classification and outlier analysis for weather prediction. Apart from the abovementioned literature, some other work has been done by various researchers [24-27, 32-35]. Many authors, like Wang and Sheng [36], Htike and Khalifa [39], Phusakulkajorn [42], Charaniya, et al. [45] developed neural network based techniques for rainfall prediction. The CART and C4.5 technique proposed by Ji, et al. [43] was developed for hourly rainfall prediction, while Phusakulkajorn’s method [42] forecasts daily rainfall based on previous rainfall data. Techniques proposed by Jesada, et al. [38], Htike and Khalifa [39], Charaniya, et al. [45], Jin, et al. [49], and Suhartono, et al. [50] predict monthly rainfall. Only Wang and Sheng [36], Kannan, et al. [37], Awan,
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Rainfall Prediction using Neural Net based Frequency Analysis Approach

Rainfall Prediction using Neural Net based Frequency Analysis Approach

Rainfall prediction is very complex hydrologic process and is important as it holds the key to any countries’ economy. Proposed model presents a new approach for yearly rainfall prediction of 30 Indian subdivisions. Yearly rainfall data of the Indian subdivision is available from IITM, Pune. The combination of Fast Fourier Transform (FFT) and Feed Forward Neural Network (FFNN) is applied for next one year rainfall prediction. Fast Fourier transform with filtering is performed on interpolated rainfall data to separate periodic components. These periodic components and delayed periodic components are given as input and desired output respectively to an FFNN for training. While testing the output of FFNN, inverse FFT gives the predicted rainfall value by amount of training input-output delay. This model is tested with 140 year’s Indian subdivisions rainfall data. The experimental results of 30 subdivisions show that next one year rainfall prediction accuracy is above 92%.
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A New Approach for Rainfall Prediction using  Artificial Neural Network

A New Approach for Rainfall Prediction using Artificial Neural Network

In the earth’s environment, Rainfall is one of the key parameter. The essential requirement of rainfall forecast data is to endorsement water resources management specifically which is concerned to be change the global climate in different tropical regions [1]. In the areas of climate and weather forecasting, Rainfall Prediction has a broader domain. On the attention of the scientific community, industries, governments, and risk management entities, were describe the rainfall prediction, which affects many human activities like construction power generation, forestry and tourism, agricultural production, among others. Different types of forecasting the rainfall techniques are provided in India, because India is an agricultural country and the rainfall and humidity is the main factor of agriculture [2]. In India, Rainfall Prediction are depends on the mainly two perspectives: Dynamical method and Empirical method. Using the physical models, Dynamical methods are used to rainfall prediction which is based on systems of equations that predict the evaluation of the Global Climate System (GCS), were response to initial atmospheric condition. The empirical approaches used for rainfall prediction are Artificial Neural Network (ANN), Linear Regression, Decision Tree Algorithm, Fuzzy Logic, and group method of data handling. Empirical method is works on analysis of historical data and it is related to a variety of atmospheric parameter [3].
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Fuzzy Knowledge Based Expert System for Prediction of Color Strength of Cotton Knitted Fabrics

Fuzzy Knowledge Based Expert System for Prediction of Color Strength of Cotton Knitted Fabrics

mathematical modeling can be overcome by fuzzy logic which can effectively interpret the knowledge of a dyer/dyeing engineer into a set of expert system rules. Unlike statistical regression models, fuzzy system needs no information or prior assessment of any mathematical models in advance. Moreover, fuzzy system does not require huge amounts input- output data for model parameter optimization unlike ANN and ANFIS models. A fuzzy logic model is comparatively easier to apply than others and gives better explanation of the nature of non-linearity among the input and output variables. Besides, fuzzy system is used to resolve the problems in which descriptions of behavior and observations are imprecise, vague and uncertain. The term fuzzy refers to the circumstances where there are well-defined boundaries or explanation for the set of activities. For instance, in textile dyeing industries, a dyer/dyeing engineer often uses terms such as high or low, strong or weak, for assessing the knit dyed fabrics qualities such as color strength, color fastness, color levelness etc. [8].
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Student Counseling System: A Rule-Based
Expert System based on Certainty Factor and
Backward Chaining Approach

Student Counseling System: A Rule-Based Expert System based on Certainty Factor and Backward Chaining Approach

After completion of 12th class, those who are science students and opted mathematics, and wanted to take admission in engineering, then SCS expert system are designed for those students who can get guideline to take which branch in the engineering. The SCS expert system asks certain questions of particular branch, which student opted and depending on answers given by the student, the expert system gives percentage eligibility to take admission in that particular branch.

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A new dynamic approach for non singleton fuzzification in noisy time series prediction

A new dynamic approach for non singleton fuzzification in noisy time series prediction

Two experiments set for applying the new method to a couple of chaotic and noisy time series prediction problems Mackey-Glass and Lorenz. The performances of NSFLSs in predicting the noisy time-series are examined for the standard fuzzification and the new methods, by measuring the produced error (MSE). The results in all settings are significantly im- proved. In general, the both experiments collectively provide evidences that applying the new method can potentially opti- mize the NSFLSs’ design. However, not all of the parameters in both experiments are carefully selected or optimized. Thus more analysis is necessary to find the exact effect of those parameters on the final results. Moreover, it will be useful to know which statistical aggregation (not limited to WMA) may fit to which SNR or other input parameters/patterns.
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Expert System for Lassa Fever Diagnosis using Rule Based Approach

Expert System for Lassa Fever Diagnosis using Rule Based Approach

ABSTRACT: Lassa fever is an acute viral haemorrhagic fever caused by the Lassa virus and first discovered in 1969 in Lassa, a town in Borno State, Nigeria though it has been in existence since 1950s. Lassa virus belong to a member of the Arenaviridae family, a single stranded Ribonucleic Acid virus (RNA), has characteristic similar to Ebola virus. Lassa fever has killed thousands of people in West Africa, most especially in Nigeria, many of whose lives could have been saved if rapid diagnostic test was available; people who could have received treatment early and also who could have been isolated early enough to reduce spread of Lassa fever. Recently many people have died in the rural areas due to late detection or delay access to proper medical attention. This is due to the fact that most medical centres equipped with handling Lassa fever cases are situated far away from the rural communities. Hence, it would be of great necessity to provide a computerized system that will provide a complementary medical service where the experts are not sufficient. Therefore, in this paper, a rule- based medical expert system is developed and tested to address the various challenges of the traditional method of diagnosing Lassa fever. This research work tried to replace the manual method of diagnosing the Lassa fever by Medical Expert, with an Expert System (ES) which is capable of correcting all the limitations associated with the manual method.
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An Analytics Prediction Model of Monthly Rainfall Time Series: Case of Thailand

An Analytics Prediction Model of Monthly Rainfall Time Series: Case of Thailand

This research presented an analytics prediction model applied to monthly rainfall time series in Thailand. The study is done with five data sets and compared the overall accuracy between ANN and SVR model. In terms of accuracy, SVR learning model takes a great advantage in comparison to ANN, especially in the north and central of Thailand. Based on the data drawn from the south of Thailand, ANN learning model produced higher accuracy over SVR. In relation to the goal of the research, SVR plays an efficient model, which represents a monthly rainfall prediction model in Thailand in terms of less computational time than ANN. This proposed model provides significant benefits to the Thai agriculturist and the water management responsibilities.
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