scholarly journals Rainfall Prediction using Artificial Neural Network in Semi-Arid mountainous region, Saudi Arabia

2021 ◽  
Vol 32 (4) ◽  
pp. 1-11
Author(s):  
Roohul Abad Khan ◽  
Rachida El Morabet ◽  
Javed Mallick ◽  
Mohammed Azam ◽  
Viola Vambol ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Arid Region ◽  
Prediction Models ◽  
Moving Average ◽  
Mountainous Region ◽  
Rainfall Prediction ◽  
Artificial Neural ◽  
Semi Arid Region ◽  
Semi Arid

Rainfall prediction using Artificial Intelligence technique is gaining attention nowadays. Semi-arid region receives rainfall below potential evapotranspiration but more than arid region. However, in mountainous semi-arid region high rainfall intensity makes it highly variable. This renders rainfall prediction difficult by applying normal techniques and calls for data pre-processing. This study presents rainfall prediction in semi-arid mountainous region of Abha, KSA. The study adopted Moving Average (Method) for data pre-processing based on 2 years, 3 years, 4 years, 5 years and 10 years. The Artificial Neural Network (ANN) was trained for a period of 1978-2016 rainfall data. The neural network was validated against the existing data of period 1997-2006. The trained neural network was used to predict for period of 2017-2025. The performance of the model was evaluated against AAE, MAE, RMSE, MASE and PP. The mean absolute error was observed least in 2 years moving average model. However, the most accurate prediction models were obtained from 2 years moving average and 5 year moving average. The study concludes that ANN coupled with MA have potential of predicting rainfall in Semi-Arid mountainous region.

Predicting catchment flow in a semi-arid region via an artificial neural network technique

2004 ◽  
Vol 18 (13) ◽  
pp. 2387-2393 ◽  
Author(s):  
S. Riad ◽  
J. Mania ◽  
L. Bouchaou ◽  
Y. Najjar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Arid Region ◽  
Artificial Neural ◽  
Semi Arid Region ◽  
Semi Arid

scholarly journals Rainfall Forecasting Using Various Artificial Neural Network Techniques - A Review

2021 ◽  
pp. 506-526
Author(s):  
Nisha Thakur ◽  
Sanjeev Karmakar ◽  
Sunita Soni
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Moving Average ◽  
Weather Prediction ◽  
Optimization Technique ◽  
Back Propagation ◽  
Rainfall Forecasting ◽  
Rainfall Prediction ◽  
Artificial Neural ◽  
Review Reports

The present review reports the work done by the various authors towards rainfall forecasting using the different techniques within Artificial Neural Network concepts. Back-Propagation, Auto-Regressive Moving Average (ARIMA), ANN , K- Nearest Neighbourhood (K-NN), Hybrid model (Wavelet-ANN), Hybrid Wavelet-NARX model, Rainfall-runoff models, (Two-stage optimization technique), Adaptive Basis Function Neural Network (ABFNN), Multilayer perceptron, etc., algorithms/technologies were reviewed. A tabular representation was used to compare the above-mentioned technologies for rainfall predictions. In most of the articles, training and testing, accuracy was found more than 95%. The rainfall prediction done using the ANN techniques was found much superior to the other techniques like Numerical Weather Prediction (NWP) and Statistical Method because of the non-linear and complex physical conditions affecting the occurrence of rainfall.

scholarly journals Improved Rainfall Prediction Using Combined Pre-Processing Methods and Feed-Forward Neural Networks

J ◽  
2019 ◽  
Vol 2 (1) ◽  
pp. 65-83 ◽  
Author(s):  
Duong Tran Anh ◽  
Thanh Duc Dang ◽  
Song Pham Van
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wavelet Transform ◽  
Moving Average ◽  
Monthly Rainfall ◽  
Feed Forward ◽  
Rainfall Prediction ◽  
Feed Forward Neural Networks ◽  
Artificial Neural ◽  
Seasonal Decomposition

Rainfall prediction is a fundamental process in providing inputs for climate impact studies and hydrological process assessments. Rainfall events are, however, a complicated phenomenon and continues to be a challenge in forecasting. This paper introduces novel hybrid models for monthly rainfall prediction in which we combined two pre-processing methods (Seasonal Decomposition and Discrete Wavelet Transform) and two feed-forward neural networks (Artificial Neural Network and Seasonal Artificial Neural Network). In detail, observed monthly rainfall time series at the Ca Mau hydrological station in Vietnam were decomposed by using the two pre-processing data methods applied to five sub-signals at four levels by wavelet analysis, and three sub-sets by seasonal decomposition. After that, the processed data were used to feed the feed-forward Neural Network (ANN) and Seasonal Artificial Neural Network (SANN) rainfall prediction models. For model evaluations, the anticipated models were compared with the traditional Genetic Algorithm and Simulated Annealing algorithm (GA-SA) supported by Autoregressive Moving Average (ARMA) and Autoregressive Integrated Moving Average (ARIMA). Results showed both the wavelet transform and seasonal decomposition methods combined with the SANN model could satisfactorily simulate non-stationary and non-linear time series-related problems such as rainfall prediction, but wavelet transform along with SANN provided the most accurately predicted monthly rainfall.

scholarly journals Corrosion Behavior of LENS Deposited CoCrMo Alloy Using Bayesian Regularization-Based Artificial Neural Network (BRANN)

2021 ◽  
Vol 7 (3) ◽  
Author(s):  
Nagoor Basha Shaik ◽  
Kedar Mallik Mantrala ◽  
Balaji Bakthavatchalam ◽  
Qandeel Fatima Gillani ◽  
M. Faisal Rehman ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Corrosion Rate ◽  
Prediction Models ◽  
Test Time ◽  
Bayesian Regularization ◽  
Cobalt Chromium ◽  
Corrosion Properties ◽  
Cocrmo Alloy ◽  
Artificial Neural

AbstractThe well-known fact of metallurgy is that the lifetime of a metal structure depends on the material's corrosion rate. Therefore, applying an appropriate prediction of corrosion process for the manufactured metals or alloys trigger an extended life of the product. At present, the current prediction models for additive manufactured alloys are either complicated or built on a restricted basis towards corrosion depletion. This paper presents a novel approach to estimate the corrosion rate and corrosion potential prediction by considering significant major parameters such as solution time, aging time, aging temperature, and corrosion test time. The Laser Engineered Net Shaping (LENS), which is an additive manufacturing process used in the manufacturing of health care equipment, was investigated in the present research. All the accumulated information used to manufacture the LENS-based Cobalt-Chromium-Molybdenum (CoCrMo) alloy was considered from previous literature. They enabled to create a robust Bayesian Regularization (BR)-based Artificial Neural Network (ANN) in order to predict with accuracy the material best corrosion properties. The achieved data were validated by investigating its experimental behavior. It was found a very good agreement between the predicted values generated with the BRANN model and experimental values. The robustness of the proposed approach allows to implement the manufactured materials successfully in the biomedical implants.

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