Dam water level prediction system utilizing Artificial Neural Network Back Propagation: Case study: Ciliwung watershed, Katulampa Dam

2016 ◽  
Author(s):  
Azfina Putri Anindita ◽  
Pujo Laksono ◽  
I Gusti Bagus Baskara Nugraha
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Level ◽  
Back Propagation ◽  
Prediction System ◽  
Artificial Neural ◽  
Water Level Prediction

scholarly journals MODEL PREDIKSI LEVEL AIR DI LAHAN PERKEBUNAN KELAPA SAWIT DENGAN JARINGAN SARAF TIRUAN BERDASARKAN PENGUKURAN SENSOR RAIN GAUGE DAN ULTRASONIK

2021 ◽  
Vol 10 (1) ◽  
pp. 104
Author(s):  
Hasan Al Banna ◽  
Bayu Dwi Apri Nugroho
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prediction Model ◽  
Water Level ◽  
Rain Gauge ◽  
Water Levels ◽  
Weather Station ◽  
Automatic Weather Station ◽  
Artificial Neural ◽  
Water Level Prediction

Monitoring and regulating water levels in oil palm swamps has an essential role in providing sufficient water for crops and conserving the land to not easily or quickly deteriorate. Presently, water level is still manual and has weaknesses, one of which is the accuracy of the data taken depending on the observer. Technology such as sensors integrated with artificial neural network is expected to observe and regulate water levels. This study aims to build a prediction model of water levels in oil palm plantations with artificial neural networks based on the rain gauge and ultrasonic sensors installed on Automatic Weather Station (AWS). The obtained results showed that the prediction model runs well with an R2 value of 0,994 and RMSE 1,16 cm. The water level prediction model in this research then tested for accuracy to prove the model's success rate. Testing the water level prediction model's accuracy in the dry season obtained an R2 value of 0,96 and an RMSE of 1,99 cm. Testing the water level prediction model's accuracy in the rainy season obtained an R2 value of 0,85 and an RMSE value of 4,2 cm. Keywords : artificial neural network, automatic weather station, palm oil, water level

Ground water level prediction using artificial neural network

2016 ◽  
Vol 6 (4) ◽  
pp. 371 ◽  
Author(s):  
Noor e ashmaul Husna ◽  
Sheikh Hefzul Bari ◽  
Md. Manjurul Hussain ◽  
Md. Tauhid Ur rahman ◽  
Mashrekur Rahman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Ground Water ◽  
Water Level ◽  
Ground Water Level ◽  
Artificial Neural ◽  
Water Level Prediction

scholarly journals Design of an Artificial Neural Network (BPNN) to Predict the Content of Silicon Oxide (SiO2) based on the Values of the Rock Main Oxides: Glass Factory Feed Case Study

2022 ◽  
Vol 11 (02) ◽  
pp. 41-44
Author(s):  
Hamed Nazerian ◽  
Adel Shirazy ◽  
Aref Shirazi ◽  
Ardeshir Hezarkhani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Silicon Oxide ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Neural Network Algorithm ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Artificial Neural Network Algorithm

Artificial neural network (ANN) is one of the practical methods for prediction in various sciences. In this study, which was carried out on Glass and Crystal Factory in Isfahan, the amount of silica purification used in industry has been investigated according to its analyses. In this discussion, according to the artificial neural network algorithm back propagation neural network (BPNN), the amount of silica (SiO2) was predicted according to rock main oxides in chemical analysis. These studies can be used as a criterion for estimating the purity for use in the factory due to the high accuracy obtained.

A novel respiration pattern biometric prediction system based on artificial neural network

Sensor Review ◽  
2020 ◽  
Vol 40 (1) ◽  
pp. 8-16 ◽  
Author(s):  
Rafiu King Raji ◽  
Michael Adjeisah ◽  
Xuhong Miao ◽  
Ailan Wan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Learning Rate ◽  
Prediction System ◽  
Ann Model ◽  
Content Type ◽  
Respiration Pattern ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Purpose The purpose of this paper is to introduce a novel respiration pattern-based biometric prediction system (BPS) by using artificial neural network (ANN). Design/methodology/approach Respiration patterns were obtained using a knitted piezoresistive smart chest band. The ANN model was implemented by using four hidden layers to help achieve the best complexity to produce an adequate fit for the data. Not only did this study give a detailed distribution of an ANN model construction including the scheme of parameters and network layers, ablation of the architecture and the derivation of back-propagation during the iterations but also engaged a step-based decay to systematically drop the learning rate after specific epochs during training to minimize the loss and increase the model’s accuracy as well as to limit the risk of overfitting. Findings Findings establish the feasibility of using respiratory patterns for biometric identification. Experimental results show that, with a learning rate drop factor = 0.5, the network is able to continue to learn past epoch 40 until stagnation occurs which yielded a classification accuracy of 98 per cent. Out of 51,338 test set, the model achieved 51,557 correctly classified instances and 169 misclassified instances. Practical implications The findings provide an impetus for possible studies into the application of chest breathing sensors for human machine interfaces in the area of entertainment. Originality/value This is the first time respiratory patterns have been applied in biometric prediction system design.

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