A proposed hybrid rainfall simulation model: bootstrap aggregated classification tree–artificial neural network (BACT-ANN) for the Langat River Basin, Malaysia

2019 ◽  
Vol 11 (4) ◽  
pp. 1218-1234 ◽  
Author(s):  
Chau Yuan Lian ◽  
Yuk Feng Huang ◽  
Jing Lin Ng ◽  
Majid Mirzaei ◽  
Chai Hoon Koo ◽  
...  
Keyword(s):  
Neural Network ◽  
Climate Change ◽  
Artificial Neural Network ◽  
River Basin ◽  
Classification Tree ◽  
Probability Of Detection ◽  
Ann Model ◽  
Statistical Measures ◽  
Langat River ◽  
Artificial Neural

Abstract Climate change is a global issue posing threats to the human population and water systems. As Malaysia experiences a tropical climate with intense rainfall occurring throughout the year, accurate rainfall simulations are particularly important to provide information for climate change assessment and hydrological modelling. An artificial intelligence-based hybrid model, the bootstrap aggregated classification tree–artificial neural network (BACT-ANN) model, was proposed for simulating rainfall occurrences and amounts over the Langat River Basin, Malaysia. The performance of this proposed BACT-ANN model was evaluated and compared with the stochastic non-homogeneous hidden Markov model (NHMM). The observed daily rainfall series for the years 1975–2012 at four rainfall stations have been selected. It was found that the BACT-ANN model performed better however, with slight underproductions of the wet spell lengths. The BACT-ANN model scored better for the probability of detection (POD), false alarm rate (FAR) and the Heidke skill score (HSS). The NHMM model tended to overpredict the rainfall occurrence while being less capable with the statistical measures such as distribution, equality, variance and statistical correlations of rainfall amount. Overall, the BACT-ANN model was considered the more effective tool for the purpose of simulating the rainfall characteristics in Langat River Basin.

scholarly journals Artificial neural network model for precipitation forecast over Western Himalaya using satellite images

MAUSAM ◽  
2022 ◽  
Vol 73 (1) ◽  
pp. 83-90
Author(s):  
PIYUSH JOSHI ◽  
M.S. SHEKHAR ◽  
ASHAVANI KUMAR ◽  
J.K. QUAMARA
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Satellite Images ◽  
India Meteorological Department ◽  
Western Himalaya ◽  
Probability Of Detection ◽  
Precipitation Forecast ◽  
Ann Model ◽  
Skill Scores ◽  
Artificial Neural

Kalpana satellite images in real time available by India meteorological department (IMD), contain relevant inputs about the cloud in infra-red (IR), water vapor (WV), and visible (VIS) bands. In the present study an attempt has been made to forecast precipitation at six stations in western Himalaya by using extracted grey scale values of IR and WV images. The extracted pixel values at a location are trained for the corresponding precipitation at that location. The precipitation state at 0300 UTC is considered to train the model for precipitation forecast with 24 hour lead time. The satellite images acquired in IR (10.5 - 12.5 µm) and WV (5.7 - 7.1 µm) bands have been used for developing Artificial Neural Network (ANN) model for qualitative as well as quantitative precipitation forecast. The model results are validated with ground observations and skill scores are computed to check the potential of the model for operational purpose. The probability of detection at the six stations varies from 0.78 for Gulmarg in Pir-Panjal range to 0.95 for Dras in Greater Himalayan range. Overall performance for qualitative forecast is in the range from 61% to 84%. Root mean square error for different locations under study is in the range 5.81 to 8.7.

scholarly journals Modeling of Rainfall-Runoff Correlations Using Artificial Neural Network-A Case Study of Dharoi Watershed of a Sabarmati River Basin, India

2017 ◽  
Vol 3 (2) ◽  
pp. 78-87 ◽  
Author(s):  
Ajaykumar Bhagubhai Patel ◽  
Geeta S. Joshi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
River Basin ◽  
Annual Rainfall ◽  
Rainfall Runoff ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Feed Forward Back Propagation ◽  
Sabarmati River ◽  
Artificial Neural

The use of an Artificial Neural Network (ANN) is becoming common due to its ability to analyse complex nonlinear events. An ANN has a flexible, convenient and easy mathematical structure to identify the nonlinear relationships between input and output data sets. This capability could efficiently be employed for the different hydrological models such as rainfall-runoff models, which are inherently nonlinear in nature. Artificial Neural Networks (ANN) can be used in cases where the available data is limited. The present work involves the development of an ANN model using Feed-Forward Back Propagation algorithm for establishing monthly and annual rainfall runoff correlations. The hydrologic variables used were monthly and annual rainfall and runoff for monthly and annual time period of monsoon season. The ANN model developed in this study is applied to Dharoi reservoir watersheds of Sabarmati river basin of India. The hydrologic data were available for twenty-nine years at Dharoi station at Dharoi dam project. The model results yielding into the least error is recommended for simulating the rainfall-runoff characteristics of the watersheds. The obtained results can help the water resource managers to operate the reservoir properly in the case of extreme events such as flooding and drought.

scholarly journals Trophic state assessment using hybrid classification tree-artificial neural network

2020 ◽  
Vol 6 (1) ◽  
pp. 46
Author(s):  
Ronnie Sabino Concepcion II ◽  
Pocholo James Mission Loresco ◽  
Rhen Anjerome Rañola Bedruz ◽  
Elmer Pamisa Dadios ◽  
Sandy Cruz Lauguico ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Trophic State ◽  
Classification Tree ◽  
Secchi Depth ◽  
Cross Entropy ◽  
Ann Model ◽  
The Neural Network ◽  
Hybrid Classification ◽  
Artificial Neural

The trophic state is one of the significant environmental impacts that must be monitored and controlled in any aquatic environment. This phenomenon due to nutrient imbalance in water strengthened with global warming, inhibits the natural system to progress. With eutrophication, the mass of algae in the water surface increases and results to lower dissolved oxygen in the water that is essential for fishes. Numerous limnological and physical features affect the trophic state and thus require extensive analysis to asses it. This paper proposed a model of hybrid classification tree-artificial neural network (CT-ANN) to assess the trophic state based on the selected significant features. The classification tree was used as a multidimensional reduction technique for feature selection, which eliminates eight original features. The remaining predictors having high impacts are chlorophyll-a, phosphorus and Secchi depth. The two-layer ANN with 20 artificial neurons was constructed to assess the trophic state of input features. The neural network was modeled based on the key parameters of learning time, cross-entropy, and regression coefficient. The ANN model used to assess trophic state based on 11 predictors resulted in 81.3% accuracy. The modeled hybrid classification tree-ANN based on 3 predictors resulted to 88.8% accuracy with a cross-entropy performance of 0.096495. Based on the obtained result, the modeled hybrid classification tree-ANN provides higher accuracy in assessing the trophic state of the aquaponic system.

Development of Dermatological Skin Disease Detection and Classification based on Wavelet and ANN

2018 ◽  
Vol 8 (5) ◽  
pp. 10
Author(s):  
Revati Kadu ◽  
U. A. Belorkar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Skin Disease ◽  
Skin Diseases ◽  
Screening Method ◽  
Phase Detection ◽  
Statistical Measures ◽  
The World ◽  
Artificial Neural ◽  
Wavelet Techniques

One of the most common and augmenting health problems in the world are related to skin. The most  unpredictable and one of the most difficult entities to automatically detect and evaluate is the human skin disease because of complexities of texture, tone, presence of hair and other distinctive features. Many cases of skin diseases in the world have triggered a need to develop an effective automated screening method for detection and diagnosis of the area of disease. Therefore the objective of this work is to develop a new technique for automated detection and analysis of the skin disease images based on color and texture information for skin disease screening. In this paper, system is proposed which detects the skin diseases using Wavelet Techniques and Artificial Neural Network. This paper presents a wavelet-based texture analysis method for classification of five types of skin diseases. The method applies tree-structured wavelet transform on different color channels of red, green and blue dermoscopy images, and employs various statistical measures and ratios on wavelet coefficients. In all 99 unique features are extracted from the image. By using Artificial Neural Network, the system successfully detects different types of dermatological skin diseases. It consists of mainly three phases image processing, training phase, detection  and classification phase.

Temperature Rise Prediction of Oil-Air Lubricated Angular Contact Ball Bearings Using Artificial Neural Network

2019 ◽  
Vol 12 (3) ◽  
pp. 248-261
Author(s):  
Baomin Wang ◽  
Xiao Chang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Temperature Rise ◽  
High Speed ◽  
Ball Bearing ◽  
Influence Factors ◽  
Ball Bearings ◽  
Ann Model ◽  
Angular Contact Ball Bearing ◽  
Artificial Neural

Background: Angular contact ball bearing is an important component of many high-speed rotating mechanical systems. Oil-air lubrication makes it possible for angular contact ball bearing to operate at high speed. So the lubrication state of angular contact ball bearing directly affects the performance of the mechanical systems. However, as bearing rotation speed increases, the temperature rise is still the dominant limiting factor for improving the performance and service life of angular contact ball bearings. Therefore, it is very necessary to predict the temperature rise of angular contact ball bearings lubricated with oil-air. Objective: The purpose of this study is to provide an overview of temperature calculation of bearing from many studies and patents, and propose a new prediction method for temperature rise of angular contact ball bearing. Methods: Based on the artificial neural network and genetic algorithm, a new prediction methodology for bearings temperature rise was proposed which capitalizes on the notion that the temperature rise of oil-air lubricated angular contact ball bearing is generally coupling. The influence factors of temperature rise in high-speed angular contact ball bearings were analyzed through grey relational analysis, and the key influence factors are determined. Combined with Genetic Algorithm (GA), the Artificial Neural Network (ANN) model based on these key influence factors was built up, two groups of experimental data were used to train and validate the ANN model. Results: Compared with the ANN model, the ANN-GA model has shorter training time, higher accuracy and better stability, the output of ANN-GA model shows a good agreement with the experimental data, above 92% of bearing temperature rise under varying conditions can be predicted using the ANNGA model. Conclusion: A new method was proposed to predict the temperature rise of oil-air lubricated angular contact ball bearings based on the artificial neural network and genetic algorithm. The results show that the prediction model has good accuracy, stability and robustness.

scholarly journals Artificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhonghui Thong ◽  
Jolena Ying Ying Tan ◽  
Eileen Shuzhen Loo ◽  
Yu Wei Phua ◽  
Xavier Liang Shun Chan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Model ◽  
Sensitivity Threshold ◽  
Ann Model ◽  
Blood Samples ◽  
Ann Models ◽  
Artificial Neural ◽  
The Impact ◽  
Age Prediction

AbstractRegression models are often used to predict age of an individual based on methylation patterns. Artificial neural network (ANN) however was recently shown to be more accurate for age prediction. Additionally, the impact of ethnicity and sex on our previous regression model have not been studied. Furthermore, there is currently no age prediction study investigating the lower limit of input DNA at the bisulfite treatment stage prior to pyrosequencing. Herein, we evaluated both regression and ANN models, and the impact of ethnicity and sex on age prediction for 333 local blood samples using three loci on the pyrosequencing platform. Subsequently, we trained a one locus-based ANN model to reduce the amount of DNA used. We demonstrated that the ANN model has a higher accuracy of age prediction than the regression model. Additionally, we showed that ethnicity did not affect age prediction among local Chinese, Malays and Indians. Although the predicted age of males were marginally overestimated, sex did not impact the accuracy of age prediction. Lastly, we present a one locus, dual CpG model using 25 ng of input DNA that is sufficient for forensic age prediction. In conclusion, the two ANN models validated would be useful for age prediction to provide forensic intelligence leads.

scholarly journals Estimation of the Hot Swap Circulation Current of a Multiple Parallel Lithium Battery System with an Artificial Neural Network Model

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1448
Author(s):  
Nam-Gyu Lim ◽  
Jae-Yeol Kim ◽  
Seongjun Lee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Lithium Battery ◽  
Battery Pack ◽  
Battery Management ◽  
Ann Model ◽  
Battery System ◽  
Load Current ◽  
Circulating Current ◽  
Artificial Neural

Battery applications, such as electric vehicles, electric propulsion ships, and energy storage systems, are developing rapidly, and battery management issues are gaining attention. In this application field, a battery system with a high capacity and high power in which numerous battery cells are connected in series and parallel is used. Therefore, research on a battery management system (BMS) to which various algorithms are applied for efficient use and safe operation of batteries is being conducted. In general, maintenance/replacement of multi-series/multiple parallel battery systems is only possible when there is no load current, or the entire system is shut down. However, if the circulating current generated by the voltage difference between the newly added battery and the existing battery pack is less than the allowable current of the system, the new battery can be connected while the system is running, which is called hot swapping. The circulating current generated during the hot-swap operation is determined by the battery’s state of charge (SOC), the parallel configuration of the battery system, temperature, aging, operating point, and differences in the load current. Therefore, since there is a limit to formulating a circulating current that changes in size according to these various conditions, this paper presents a circulating current estimation method, using an artificial neural network (ANN). The ANN model for estimating the hot-swap circulating current is designed for a 1S4P lithium battery pack system, consisting of one series and four parallel cells. The circulating current of the ANN model proposed in this paper is experimentally verified to be able to estimate the actual value within a 6% error range.

Modeling of lime production process using artificial neural network

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Abolghasem Daeichian ◽  
Rana Shahramfar ◽  
Elham Heidari
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Production Process ◽  
High Energy ◽  
Ann Model ◽  
Artificial Neural ◽  
Rotary Kilns ◽  
Input Variables ◽  
Artificial Neural Network Ann ◽  
Lime Production

Abstract Lime is a significant material in many industrial processes, including steelmaking by blast furnace. Lime production through rotary kilns is a standard method in industries, yet it has depreciation, high energy consumption, and environmental pollution. A model of the lime production process can help to not only increase our knowledge and awareness but also can help reduce its disadvantages. This paper presents a black-box model by Artificial Neural Network (ANN) for the lime production process considering pre-heater, rotary kiln, and cooler parameters. To this end, actual data are collected from Zobahan Isfahan Steel Company, Iran, which consists of 746 data obtained in a duration of one year. The proposed model considers 23 input variables, predicting the amount of produced lime as an output variable. The ANN parameters such as number of hidden layers, number of neurons in each layer, activation functions, and training algorithm are optimized. Then, the sensitivity of the optimum model to the input variables is investigated. Top-three input variables are selected on the basis of one-group sensitivity analysis and their interactions are studied. Finally, an ANN model is developed considering the top-three most effective input variables. The mean square error of the proposed models with 23 and 3 inputs are equal to 0.000693 and 0.004061, respectively, which shows a high prediction capability of the two proposed models.

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