Stimulating Deep Learning Network on Graphical Processing Unit To Predict Water Level

Neeru Singh,Supriya P Panda

doi:10.35940/ijeat.d8452.049420

Abstract

Deep learning is widespread over different fields like health industries, voice recognition, image & video classification, real-time rendering applications, face recognition and many other domains too. Fundamentally Deep Learning is used due to the three different aspects. The first one is its ability to perform better with a huge amount of data for training, second is high computational speed, and third is the elevation of deep training at various levels of reflection and depiction. Acceleration of Deep Machine Learning requires a platform for immense performance; this needs accelerated hardware for training convoluted deep learning problems. While training large datasets on deep learning that takes hours, days, or weeks, accelerated hardware that decreased the overload of computation task can be used. The main attention of all the research studies is to optimize the results of predictions in terms of accuracy, error rate and execution time. Graphical Processing Unit (GPU) is one of the accelerated hardware that has currently prevailed to decrease the training time due to its parallel architecture. In this research paper, the multi-level or Deep Learning approach is simulated over Central Processing Unit (CPU) and GPU. Different research claims that GPUs deliver accurate results with a maximum rate of speed. MATLAB is the framework used in this work to train the Deep Learning network for predicting Ground Water Level using a dataset of three different parameters Temperature, Rainfall, and Water requirement. Thirteen year’s dataset of Faridabad District of Haryana from the year 2006 to 2018 is used to train, verify, test and analyzed the network over CPU and GPU. The training function used was the trailm for training the network over CPU and trainscg for GPU training as it does not support Jacobian training. From our results, it is concluded that for large dataset the accuracy of training increased with GPU and processing time for training is decreased when compared to CPU. Overall performance improves while training the network over GPU and suits to be a better method for predicting the Water Level. The proficiency estimation of the network shows the maximum regression value, least Mean Square Error (MSE), and highperformance value for GPU during the training.

Highlights

Deep Learning that stimulates like human brain for acquiring knowledge is the subsection of MachineIntelligence
Our hypothesis states that there is significant increase in the performance of training the data on parallel mode and Graphical Processing Unit (GPU) with that of Central Processing Unit (CPU) alone
This section deals with the results related to the training of the network using CPU, Parallel execution and GPU computing."Fig.8 (a)" shows the graphs plotted for checking the training performance graph concerning the Mean Squared Error (MSE) calculated using CPU

Summary

Introduction

Deep Learning that stimulates like human brain for acquiring knowledge is the subsection of MachineIntelligence. Deep Learning is used to automate the predictive analysis. Traditional Machine learning approach for training network are linear and that of Deep learning is hierarchical with layered architecture. In Machine learning an optimum criteria heading to accelerate the accurate categorization and need escalation to increase the performance by continuous training until the error is reduced [1]. According to recent analysis in time series research and development Deep Learning Networks exhibits tremendous work for prediction on large dataset with more accuracy and throughput [5]. The successful applications of Deep Neural Network (DNN) have substantiate in many domains and is the most appropriate approach for performance improvement over prediction problems [6]. As the requirement of water increases constantly, the level of the groundwater continually dips down to 0.50 meters per annum from the past 30 years. The basic element of reduction of water in Haryana is due to the crops, which require more water than usual [9]

Methods

Results

Conclusion