Forward Back Propagation Neural Network Research Articles

Analyzing brain-activation responses to auditory stimuli improves the diagnosis of a disorder of consciousness by non-linear dynamic analysis of the EEG

Although auditory stimuli benefit patients with disorders of consciousness (DOC), the optimal stimulus remains unclear. We explored the most effective electroencephalography (EEG)-tracking method for eliciting brain responses to auditory stimuli and assessed its potential as a neural marker to improve DOC diagnosis. We collected 58 EEG recordings from patients with DOC to evaluate the classification model’s performance and optimal auditory stimulus. Using non-linear dynamic analysis (approximate entropy [ApEn]), we assessed EEG responses to various auditory stimuli (resting state, preferred music, subject’s own name [SON], and familiar music) in 40 patients. The diagnostic performance of the optimal stimulus-induced EEG classification for vegetative state (VS)/unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS) was compared with the Coma Recovery Scale-Revision in 18 patients using the machine learning cascade forward backpropagation neural network model. Regardless of patient status, preferred music significantly activated the cerebral cortex. Patients in MCS showed increased activity in the prefrontal pole and central, occipital, and temporal cortices, whereas those in VS/UWS showed activity in the prefrontal and anterior temporal lobes. Patients in VS/UWS exhibited the lowest preferred music-induced ApEn differences in the central, middle, and posterior temporal lobes compared with those in MCS. The resting state ApEn value of the prefrontal pole (0.77) distinguished VS/UWS from MCS with 61.11% accuracy. The cascade forward backpropagation neural network tested for ApEn values in the resting state and preferred music-induced ApEn differences achieved an average of 83.33% accuracy in distinguishing VS/UWS from MCS (based on K-fold cross-validation). EEG non-linear analysis quantifies cortical responses in patients with DOC, with preferred music inducing more intense EEG responses than SON and familiar music. Machine learning algorithms combined with auditory stimuli showed strong potential for improving DOC diagnosis. Future studies should explore the optimal multimodal sensory stimuli tailored for individual patients.Trial registration: The study is registered in the Chinese Registry of Clinical Trials (Approval no: KYLL-2023-414, Registration code: ChiCTR2300079310).

Energy Efficient Data Aggregation in Wireless Sensor Networks Using Meta Heuristic Based Feed Forward Back Propagation Neural Network Approach

Sensor nodes are low-cost, low-power, tiny devices that make up the majority of WSNs, or distributed, self-organizing systems. These sensor nodes are able to exchange, perceive, and interpret data. The sensor nodes are equipped with a wide variety of sensors, such as chemical, touch, motion, temperature, and weather sensors. Because of its adaptability, sensors are used in a variety of applications such as automation, tracking, monitoring, and surveillance. Despite the enormous number of sensor applications, WSNs continue to suffer from common challenges like as low memory, slow processing speed, and short network lifetime. The feed forward back propagation neural network mode (FFBPNN) based on meta heuristics aims to create many paths for effective data aggregation in wireless sensor networks. This model handled the process of identifying and selecting the optimum route path. The distributed sensor nodes are utilized to create the various route paths. In this research paper, data aggregation is done using meta-heuristic firefly algorithm that helped in identifying an optimal route from among the found routes. After selecting the operative ideal route choice, the data aggregation procedure practices a rank-based approach to accomplish lower latency and a better packet delivery ratio(PDR). In addition to throughput, simulation was done to improve and measure performance in terms of packet delivery ratio, energy consumption, and end-to-end latency.

A Multi-Layered Hybrid Machine Learning Algorithm (MLHA) for Type II Diabetes Classification

Machine learning applications in the medical field has proliferated in the past decade. One of the main usages of machine learning in medical field is the detection of diseases from text and image data. In this work, a new hybrid multi-layer algorithm (MLHA) based on machine learning algorithms is proposed to predict diabetes. The algorithm consists of two layers. The first layer consists of three different machine learning algorithms; SVM, random forest and K-mean, that work in parallel on the dataset. The output of this layer is fed into the second layer that consists of only one algorithm; XGBoost, that is trained from the output data of the first layer. We trained the proposed model with two different datasets and its accuracy has been compared with three different common well-known algorithms; feed forward back propagation neural network, logistic regression and random forest. Our results show that the accuracy of MLHA reached 86.5% for the first dataset and approximately 84.6% for the second dataset. This reported accuracy outperformed all the classical models.

MADESANT: malware detection and severity analysis in industrial environments

Malware remains a persistent threat to industrial operations, causing disruptions and financial losses. Traditional malware detection approaches struggle with the increasing complexity of false positives and negatives. However, existing Intrusion Detection Systems (IDSs) often lack the capability to assess the severity of detected malware, crucial for effective threat mitigation. This paper presents a novel model, MAlware DEtection and Severity Analysis for eNcrypted Traffic (MADESANT), designed to detect and analyze malware severity in encrypted traffic data. MADESANT combines Deep Learning (DL)-based intrusion detection with Machine Learning (ML)-based severity analysis, specifically customized for the minutiae of IoT systems and assets. Notably, MADESANT introduces a cascading model integrating a Cascading Forward Back Propagation Neural Network (CFBPNN) with the J48 tree to systematically assess risk factors in network traffic. Our assessment, conducted on diverse encrypted datasets including UNSW-NB15, IoT23, and XIIoTID, highlights the remarkable efficacy of MADESANT. Impressively, it achieves a flawless 0% false positive rate in detecting binary attack instances, surpassing benchmarks set by conventional models. Additionally, MADESANT excels in accurately estimate malware severity, providing invaluable insights into the factors contributing to the risk. To further validate its efficiency, we compared MADESANT against prevalent Neural Network models like FeedForward and Recurrent Neural Networks, with MADESANT emerging as the superior choice. The experimentation encompasses both the entire dataset and subsets generated through meticulous risk factor analysis. These results underscore MADESANT’s prowess in not only identifying malware but also in evaluating its potential impact, signifying a significant leap forward in industrial cybersecurity.

WHITE BLOOD CELL CANCER CLASSIFICATION USING MODIFIED HONEY BEE OPTIMIZATION ALGORITHM (MHBO) BASED FEATURE SELECTION AND NEURAL NETWORK CLASSIFICATION MODEL

A blood malignancy known as leukemia is defined by the bone marrow's abnormal and uncontrollably high production of leukocytes, or white blood cells (WBCs). It is possible to identify and diagnose illnesses early by examining photographs of minute blood cells. lately, hematopathologists have been analyzing, detecting, and identifying leukemia kinds in patients utilizing image-processing techniques. Since no specialized equipment is required for lab testing, image detection is a quick and affordable way of detection. Several image processing tools are created for obtaining significant data from medical photos to enhance patient diagnosis. In this research, a feed forward-back propagation neural network framework is presented, and the kind of cancer found in the cells is ultimately predicted. First, the provided photos are subjected to the filtering procedure. The second method is the Fuzzy Inference System (FIS) technique, which is reliable in the face of changing illumination levels and considers the stability levels of color elements to identify edges among color bone marrow microscopic pictures. Third Active contour detection provides an accurate curve of the boundary edge of the cells. And then the background separation is done by Pinched Flow Fractionation (PFF). Then the feature extraction is carried out by the Modified Honey Bee optimization algorithm (MHBO). Finally, the Feed Forward Back Propagation Neural Network (FFBNN) framework-based classifier is proposed for predicting the white blood cells. Based on the findings, this suggested approach created an automated system that allows medical practitioners to accurately diagnose all forms and subtypes of the disease. Keywords: Bone marrow microscopic images, Fuzzy Inference System (FIS), Pinched Flow Fractionation (PFF), Modified honey bee optimization algorithm (MHBO), Feed Forward Back Propagation Neural Network framework

A NEURAL NETWORK PREDICTION MODEL FOR SURFACEFINISH IN TURNING PROCESS

This paper presents a neural network based surface finish Prediction model in turning operation .Orthogonal cutting tests were performed on mild steel using H.S.S cutting tool with differentcutting parameters cutting speed ,feed and nose radius of the cutting tool . The collected data wasused to train feed forward back propagation neural network. The developed model has been testedto preclict surface finish for various cutting conditions. The model was found to be powerful &capable of accurate surface finish prediction for the range it had been trained but the accuracydeteriorated as the cutting conditions were changed significantly'

FFBP Neural Network Optimized with Woodpecker Mating Algorithm for Dynamic Cluster-based Secure Routing in WSN

Feed Forward Back Propagation Neural Network (FFBPNN) Optimized with Woodpecker Mating Algorithm is proposed in this manuscript for Dynamic Cluster-Based Secure Routing in Wireless Sensor Networks (FFBPNN-WMA-ECHC-WSN). The proposed method contains three different stages: first stage is dynamically clustered sensor network formation based on FFBPNN, second stage is Key generation for data encryption and decryption using Elliptic Curve-based Hill Cipher (ECHC), third stage is Homomorphic encryption scheme, which is used to deliver the aggregated data in-time. Woodpecker Mating Algorithm (WMA) is proposed to optimize the parameter values of FFBPNN. The proposed FFBPNN-WMA ECHC-WSN method was implemented in NS2 tool and its efficiency is evaluated under some metrics, like alive nodes, packet drop, network lifetime, delay, throughput, and energy consumption, Packet Delivery Ratio (PDR). The performance of FFBPNN-WMA-ECHC-WSN method provides lower delay 99.01%, 98.34%, 95.23% and 97.45%, and higher throughput 97.25%, 90.12%, 89.39% and 95.47% compared with existing models, such as EHCERA-SDT-WSN, DSA-ECC-PSO-SDT-WSN, IPECC-PDF-ABC-SDT-WSN and IBFA-LDCSN-BSHHO-SDT-WSN, respectively.

Enhancing infrastructure sustainability: reliability and sensitivity analysis of localized integrated renewable energy systems using feed forward backpropagation neural network

Enhancing infrastructure sustainability: reliability and sensitivity analysis of localized integrated renewable energy systems using feed forward backpropagation neural network

Prediction and optimization of Nd: YAG laser transmission micro-channelling on PMMA employing an artificial neural network model

In the present research work, performance enhancement which plays pivotal role in the modern machining techniques during machining of advanced engineering materials has been attributed. An investigation is conducted on the laser transmission micro-channeling of thick transparent PMMA using Nd: YAG laser. The objective is to examine the impact of three key factors, namely pulse frequency, lamp current and cutting speed, on several quality aspects including depth of cut, kerf width, and heat-affected zone (HAZ) width. General full factorial design is used to design the experiments and empirical models (non-linear) are developed to establish the relationship between the control factors and responses. A feed forward back-propagation neural network (FF-BPNN) is used to model the process and subsequently to predict the responses. The successful capability of FF-BPNN in predicting and enhancing the characteristics of micro-channels fabricated on PMMA has been observed. Furthermore, it has been shown that feedforward backpropagation neural networks (FF-BPNN) can serve as a highly effective tool for acquiring a comprehensive model and determining the ideal configuration of process parameters in laser transmission micro-channeling operations. Additionally, FF-BPNN results and model predicted values are compared with non-linear modelling technique. Finally, mean absolute error is calculated to find out the accuracy of both the techniques.

An improved random drift particle swarm optimization-feed forward backpropagation neural network for high-precision state-of-charge estimation of lithium-ion batteries

A predictive model with high accuracy and stability of the state of charge (SOC) estimation for lithium-ion batteries plays a significant role in electric vehicles. An improved random drift particle swarm optimization-feed forward backpropagation neural network (IRDPSO-FFBPNN) is established in this paper. Basically, a three-layer FFBPNN is established, and its learning process is analyzed in detail. Then, to avoid the particle out-of-control, inducting weight parameter σ to achieve dynamic control weight convergence. What's more, the cross-reorganization of data is proposed to enhance the utilization. Finally, a further performance comparison with other networks is made under different working conditions to prove the effectiveness of the IRDPSO-FFBPNN. The experimental results showed that the maximum SOC error of the IRDPSO-FFBPNN is 0.1021 % in 45 s, 0.1237 % in 116 s under BBDST and DST with different temperatures, respectively, which performed better both in terms of time-consumption and accuracy.

A Novel Method of Forecasting Chaotic and Random Wind Speed Regimes Based on Machine Learning with the Evolution and Prediction of Volterra Kernels

This study aims to focus on using the Volterra series and machine learning for forecasting random and chaotic wind speed regimes, since calm weather is mostly noticed at the local site, making dataset selection difficult. A novel method is proposed to predict Volterra kernels up to the third order, using a forward–back propagation neural network with 12-month measurements at Fujairah site (United Arab Emirates). Both daily and monthly wind speed datasets are investigated for forecasting. The three dominant hourly and daily kernels are extracted for each day and each month. Predicted future Volterra kernels are estimated from past values using both statistical analysis and individual neuro networks for each of the Volterra kernel coefficients. Using the evolved Volterra kernels, the hourly and daily wind speeds are forecasted with similar patterns of the measured values. Due to the random nature of wind speed at the local site, a two-layer with four neurons per layer neuro network is used to locate the most variable and intense speed during 8 h in the day. Forecasted wind speed is determined with errors arising from different sources, such as the utilization of only third-order Volterra kernels and the difficulty of machine training of the employed shallow network. Nevertheless, this work depicts a useful algorithm to forecast chaotic and random wind speed regimes. Computational time is a trade of the complexity of Volterra mathematical analysis.

PIGNUS: A Deep Learning model for IDS in industrial internet-of-things

The heterogeneous nature of the Industrial Internet of Thing (IIoT) has a considerable impact on the development of an effective Intrusion Detection System (IDS). The proliferation of linked devices results in multiple inputs from industrial sensors. IDS faces challenges in analyzing the features of the traffic and identifying anonymous behavior. Due to the unavailability of a comprehensive feature mapping method, the present IDS solutions are non-usable to identify zero-day vulnerabilities.In this paper, we introduce the first comprehensive IDS framework that combines an efficient feature-mapping technique and cascading model to solve the above-mentioned problems. We call our proposed solution deeP learnIG model intrusioN detection in indUStrial internet-of things (PIGNUS). PIGNUS integrates Auto Encoders (AE) to select optimal features and Cascade Forward Back Propagation Neural Network (CFBPNN) for classification and attack detection. The cascading model uses interconnected links from the initial layer to the output layer and determines the normal and abnormal behavior patterns and produces a perfect classification. We execute a set of experiments on five popular IIoT datasets: gas pipeline, water storage tank, NSLKDD+, UNSW-NB15, and X-IIoTID. We compare PIGNUS to the state-of-the-art models in terms of accuracy, False Positive Ratio (FPR), precision, and recall. The results show that PIGNUS provides more than 95% accuracy, which is 25% better on average than the existing models. In the other parameters, PIGNUS shows 20% improved FPR, 10% better recall, and 10% better in precision. Overall, PIGNUS proves its efficiency as an IDS solution for IIoTs. Thus, PIGNUS is an efficient solution for IIoTs.

Application of grey feed forward back propagation-neural network model based on wavelet denoising to predict the residual settlement of goafs.

To study the residual settlement of goaf's law and prediction model, we investigated the Mentougou mining area in Beijing as an example. Using MATLAB software, the wavelet threshold denoising method was used to optimize measured data, and the grey model (GM) and feed forward back propagation neural network model (FFBPNN) were combined. A grey feed forward back propagation neural network (GM-FFBPNN) model based on wavelet denoising was proposed, the prediction accuracy of different models was calculated, and the prediction results were compared with original data. The results showed that the prediction accuracy of the GM-FFBPNN was higher than that of the individual GM and FFBPNN models. The mean absolute percentage error (MAPE) of the combined model was 7.39%, the root mean square error (RMSE) was 49.01 mm, the scatter index (SI) was 0.06%, and the BIAS was 2.42%. The original monitoring data were applied to the combination model after wavelet denoising, and MAPE and RMSE were only 1.78% and 16.05 mm, respectively. Compared with the combined model before denoising, the prediction error was reduced by 5.61% and 32.96 mm. Thus, the combination model optimized by wavelet analysis had a high prediction accuracy, strong stability, and accorded with the law of change of measured data. The results of this study will contribute to the construction of future surface engineering in goafs and provide a new theoretical basis for similar settlement prediction engineering, which has strong popularization and application value.

Ku-Band SIW Filter with High Fractional Bandwidth Optimized Using Feed Forward Back Propagation ANN

An Artificial Neural Network (ANN) based Substrate Integrated Waveguide Band Pass Filter (SIW BPF) is proposed in this paper. A Feed Forward Back Propagation Neural Network (FF-BP NN) is utilized to optimize the filter dimensions. The Gradient Descent with Momentum and Adaptive Learning Rate algorithm is used to train the network at a learning rate of 0.01. The high pass characteristics of the SIW is converted into band pass by incorporating U slots on its top plane. Defected Ground Structure (DGS) is utilized in the bottom plane to improve the impedance matching. To validate, the prototype is fabricated using RT/Duroid 5880 and tested. The proposed filter has a center frequency at 14.68 GHz with a wide pass band from 12.92 to 16.43 GHz with a 3-dB Fractional Bandwidth (FBW) of 24%, return loss more than 20 dB and insertion loss of about 1.9 dB within the pass band. The filter has a small dimension of about 0.63 λ g 2 , where λ g is the guided wavelength at the center frequency. This filter offers wide passband, smaller size, low insertion loss, good return loss and it is useful in Ku-band satellite communication applications.

Predicting the thermal performance of heat pipes applying various machine learning methods and a proposed correlation

The key purpose of this study is to predict the thermal performance of heat pipes by means of artificial intelligence methods. In this regard, several different prediction models have been employed. These models are proposed with totally nine different parameters as inputs such as dimensions of heat pipes, number of turns, working fluids, inclination angle, filling ratio, and heat input. The output of these models is the thermal performance of the heat pipe. For training the prediction models, a dataset of totally 1196 experimental data was collected from previous studies which includes several different working fluids. The employed prediction methods contain ten machine learning regression methods, seven ANN models with different structures and a correlation model. At first, the machine learning regression methods have been employed and based on the results, the best performance belongs to random forest regressor method with the R2 score and MSE value of 0.90 and 0.003, respectively. Then inspiring physics of the problem, several feed forward back-propagation neural network models have been designed for simulating and estimating the thermal performance of heat pipes. Results verified that the proposed models could be used on a wide range of working fluids with an acceptable accuracy. The values of R2 and MSE of the optimal ANN model are 0.95 and 0.03, respectively which are pretty acceptable for a predicting model working on such a wide range of working fluids. Moreover, a new correlation is proposed to estimate the thermal performance of heat pipes on the basis of the same inputs. This correlation can estimate the thermal performance of the whole dataset with R2 score of 0.60 and MSE value of 0.27, while the minimum and maximum of R2 scores are 0.47 and 0.88 for water and ethanol, respectively. Finally, the results obtained from the mentioned machine learning methods, the ANN models and the proposed correlation were compared. It was found that due to complexity of the problem and wide variety of inputs, the results of using methods based on artificial intelligence are far more accurate than those of the proposed correlation. In fact, choosing more accurate and complicated methods (e.g., the optimal ANN model with structure of 9–32–40-50-64-1), the R2 score of prediction significantly increased from 0.60 to 0.95. Since there is a trade-off between the accuracy and simplicity of the methods, selecting the best method should be considered as a choice problem.

Character level vehicle license detection using multi layered feed forward back propagation neural network

Real-world traffic situations, including smart traffic monitoring, automated parking systems, and car services are increasingly using vehicle license detection systems (VLDS). Vehicle license plate identification is still a challenge with current approaches, particularly in more complicated settings. The use of machine learning and deep learning algorithms, which display improved classification accuracy and resilience, has been a significant recent breakthrough. Deep learning-based license plate identification using neural networks is proposed in this article. The number plate is detected using a multi layered feed forward back propagation neural network (MLFFBPNN). In this method, there are 3 layers namely input, hidden, and output layers has been utilized. Each layer has been related with interconnection weights. In feed forward of information, initially a set of randomly chosen weights are feed to the input data and an output has been determined. Back propagation training algorithm is utilized to train the network. Then character level identification is performed. The suggested proposed method is compared to the region-based convolutional neural network (RCNN) method in terms of accuracy and computational efficiency. The proposed method produced the character level recognition accuracy of 89%. It is improved by 4% when compared with the RCNN recognition method.

Global Solar Radiation Forecasting Based on Hybrid Model with Combinations of Meteorological Parameters: Morocco Case Study

The adequate modeling and estimation of solar radiation plays a vital role in designing solar energy applications. In fact, unnecessary environmental changes result in several problems with the components of solar photovoltaic and affects the energy generation network. Various computational algorithms have been developed over the past decades to improve the efficiency of predicting solar radiation with various input characteristics. This research provides five approaches for forecasting daily global solar radiation (GSR) in two Moroccan cities, Tetouan and Tangier. In this regard, autoregressive integrated moving average (ARIMA), autoregressive moving average (ARMA), feed forward back propagation neural networks (FFBP), hybrid ARIMA-FFBP, and hybrid ARMA-FFBP were selected to compare and forecast the daily global solar radiation with different combinations of meteorological parameters. In addition, the performance in three approaches has been calculated in terms of the statistical metric correlation coefficient (R2), root means square error (RMSE), stand deviation (σ), the slope of best fit (SBF), legate’s coefficient of efficiency (LCE), and Wilmott’s index of agreement (WIA). The best model is selected by using the computed statistical metric, which is present, and the optimal value. The R2 of the forecasted ARIMA, ARMA, FFBP, hybrid ARIMA-FFBP, and ARMA-FFBP models is varying between 0.9472% and 0.9931%. The range value of SPE is varying between 0.8435 and 0.9296. The range value of LCE is 0.8954 and 0.9696 and the range value of WIA is 0.9491 and 0.9945. The outcomes show that the hybrid ARIMA–FFBP and hybrid ARMA–FFBP techniques are more effective than other approaches due to the improved correlation coefficient (R2).

Utilizing Artificial Intelligence Approaches to Determine the Shear Strength of Steel Beams with Flat Webs

Steel beams’ shear strength is one of the most important factors that influence how quickly webs buckle. Despite extensive studies having been performed over the previous three decades, the existing procedures did not achieve the necessary reliability to predict the ultimate shear resistance of plate girders. New techniques called Learner Techniques have started to be used over the last few years; these techniques were applied to calculate the steel beam shear strength. In this study, a Regression Learner Techniques model was built using data from 100 test results from previously published research. Based on the geometric and material properties of the web and flanges available in the published tests, a model was built using Artificial Neural Networks. Based on sensitivity analysis, a Cascade Forward Backpropagation Neural Networks (CFBNN) approach was utilized to anticipate the shear strength of steel beams. The proposed models outperformed current hybrid artificial intelligence models developed using the same collected datasets and demonstrated to accurately predict the ultimate shear strength. The performance of the models was evaluated using a range of statistical assessment methods, which led to a valuable conclusion. The CFBNN model achieved the highest root mean square (R2 = 0.95). The results corresponding to each test were verified by specimen shear strength values calculated by a theoretical approach. The resultant maximum shear force obtained by the proposed modified equation was compared with the experimental results and the shear force was estimated using two different approaches proposed by the European code. Finally, two approaches were used to verify the proposed model. The first approach was the data reported from an experimental shear test program conducted by the authors, and the second was the results of the shear values acquired experimentally by other researchers. Based on the test results of the previous studies and the current work, the suggested model gives an adequate degree of accuracy for estimating the shear strength of steel beams.

Elucidating the performance of integrated anoxic/oxic moving bed biofilm reactor: Assessment of organics and nutrients removal and optimization using feed forward back propagation neural network

A lab-scale integrated anoxic and oxic (A/O) moving bed biofilm reactor (MBBR) was investigated for the removal of organics and nutrients by varying chemical oxygen demand (COD) to NH4-N ratio (C/N ratio: 3.5, 6.75, and 10), hydraulic retention time (HRT: 6 h, 15 h, and 24 h), and recirculation ratio (R: 1, 2, and 3). The use of activated carbon coated carriers prepared from waste polyethylene material and polyurethane sponges attached to a cylindrical frame in the integrated A/O MBBR increased the attached growth biomass significantly. >95 % of COD removal was observed under the C/N ratio of 10 at an HRT of 24 h. While the low C/N ratio favored the removal of NH4-N (∼98 %) and PO43--P (∼90 %) with an optimal R of 1.75. Using the experimental dataset, to predict and forecast the performance of integrated A/O MBBR, a feed-forward-backpropagation-neural-network model was developed.

PREDICTION OF FLOOD IN KARKHEH BASIN USING DATA-DRIVEN METHODS

Abstract. Flood causes several threats with outcomes which include peril to human and animal life, damage to property, and adversity to agricultural fields. Hence, flood prediction is highly significant for the mitigating municipal and environmental damage. The aim of this study was assessing the performance of different machine learning methods in predicting flood in Karkheh basin. To aim this, we used Support Vector Machine (SVM), Least Square Support Vector Machine (LSSVM), Feed Forward Back Propagation Neural Network (FFBPNN), and Radial Basis Function Neural Network (RBFNN) to simulate monthly streamflow in the study area. Furthermore, the performance of models was compared in predicting flood. All four models indicated good performance in simulating streamflow. However, LSSVM model had the highest accuracy compared with other models with R2 and RMSE of 85.89% and 30.02 m3/s during testing periods, respectively. Similarly, LSSVM model performed better in predicting annual maximum streamflow in comparison with other machine learning models.