Monthly rainfall forecasting using temperature and climate indices through a hybrid method in Queensland, Australia

AbstractThis paper presents applications of wavelet artificial neural networks (WANN) to forecast rainfalls one, three, six, and twelve months in advance using lagged monthly rainfall, maximum, minimum temperatures, Southern Oscillation Index (SOI), Inter-decadal Pacific Oscillation (IPO), and Nino3.4 as predictors. Eight input datasets comprised of different combinations of predictive variables were used for ten candidate climate stations in Queensland, Australia. Datasets were split as 1908 to 1999 for the training of the model and 2000 to 2016 for the verification of the model. Also, the conventional Artificial Neural Network (ANN) model was developed with the same input datasets to compare with WANN results. Moreover, the skillfulness of the WANN was investigated with the current climate prediction system used by the Australian Bureau of Meteorology (BOM), Australian Community Climate Earth-System Simulator–Seasonal (ACCESS–S) as well as climatology forecasts. The comparisons showed that the WANN achieved the lowest errors for three-month lagged prediction with an average Root Mean Square Error (RMSE) of 38.6mm. In contrast, for the same lag-period, the average RMSEs from ANN, ACCESS-S, and climatology predictions were 72.2mm, 102.7mm, and 72.2mm, respectively. It is also found that the ANN underestimates the peak values with an average value of 49%, 47%, 52%, and 53% at one, three, six, and twelve months lead times, correspondingly. However, the corresponding peak values underestimation through the WANN were 0%, 1%, 22%, and 39%, respectively. This research provides promising insights into using hybrid methods for predicting rainfall a few months in advance, which is extremely beneficial for Australia’s agricultural industries.

Monthly Rainfall Anomalies Forecasting for Southwestern Colombia Using Artificial Neural Networks Approaches

Water ◽

10.3390/w12092628 ◽

2020 ◽

Vol 12 (9) ◽

pp. 2628 ◽

Cited By ~ 1

Author(s):

Teresita Canchala ◽

Wilfredo Alfonso-Morales ◽

Yesid Carvajal-Escobar ◽

Wilmar L. Cerón ◽

Eduardo Caicedo-Bravo

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Southern Oscillation ◽

Rainfall Variability ◽

Monthly Rainfall ◽

Climate Indices ◽

Rainfall Forecasting ◽

Exogenous Variables ◽

Rainfall Anomalies ◽

Artificial Neural

Improving the accuracy of rainfall forecasting is relevant for adequate water resources planning and management. This research project evaluated the performance of the combination of three Artificial Neural Networks (ANN) approaches in the forecasting of the monthly rainfall anomalies for Southwestern Colombia. For this purpose, we applied the Non-linear Principal Component Analysis (NLPCA) approach to get the main modes, a Neural Network Autoregressive Moving Average with eXogenous variables (NNARMAX) as a model, and an Inverse NLPCA approach for reconstructing the monthly rainfall anomalies forecasting in the Andean Region (AR) and the Pacific Region (PR) of Southwestern Colombia, respectively. For the model, we used monthly rainfall lagged values of the eight large-scale climate indices linked to the El Niño Southern Oscillation (ENSO) phenomenon as exogenous variables. They were cross-correlated with the main modes of the rainfall variability of AR and PR obtained using NLPCA. Subsequently, both NNARMAX models were trained from 1983 to 2014 and tested for two years (2015–2016). Finally, the reconstructed outputs from the NNARMAX models were used as inputs for the Inverse NLPCA approach. The performance of the ANN approaches was measured using three different performance metrics: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Pearson’s correlation (r). The results showed suitable forecasting performance for AR and PR, and the combination of these ANN approaches demonstrated the possibility of rainfall forecasting in these sub-regions five months in advance and provided useful information for the decision-makers in Southwestern Colombia.

Artificial Neural Network for Markov Chaining of Rainfall Over India

International Journal of Business Analytics ◽

10.4018/ijban.2020070105 ◽

2020 ◽

Vol 7 (3) ◽

pp. 71-84

Author(s):

Kavita Pabreja

Keyword(s):

Neural Network ◽

Time Series Data ◽

Mean Squared Error ◽

Multiple Linear Regression Model ◽

Monthly Rainfall ◽

Series Data ◽

Rainfall Forecasting ◽

Artificial Neural ◽

Rainfall forecasting plays a significant role in water management for agriculture in a country like India where the economy depends heavily upon agriculture. In this paper, a feed forward artificial neural network (ANN) and a multiple linear regression model has been utilized for lagged time series data of monthly rainfall. The data for 23 years from 1990 to 2012 over Indian region has been used in this study. Convincing values of root mean squared error between actual monthly rainfall and that predicted by ANN has been found. It has been found that during monsoon months, rainfall of every n+3rd month can be predicted using last three months' (n, n+1, n+2) rainfall data with an excellent correlation coefficient that is more than 0.9 between actual and predicted rainfall. The probabilities of dry seasonal month, wet seasonal month for monsoon and non-monsoon months have been found.

An artificial neural network model for rainfall forecasting in Bangkok, Thailand

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-5-183-2008 ◽

2008 ◽

Vol 5 (1) ◽

pp. 183-218 ◽

Cited By ~ 24

Author(s):

N. Q. Hung ◽

M. S. Babel ◽

S. Weesakul ◽

N. K. Tripathi

Keyword(s):

Neural Network ◽

Input Parameter ◽

Flood Management ◽

Rain Gauge ◽

Ann Model ◽

Rainfall Forecasting ◽

Rainfall Forecast ◽

Artificial Neural ◽

Abstract. The present study developed an artificial neural network (ANN) model to overcome the difficulties in training the ANN models with continuous data consisting of rainy and non-rainy days. Among the six models analyzed the ANN model which used generalized feedforward type network and a hyperbolic tangent function and a combination of meteorological parameters (relative humidity, air pressure, wet bulb temperature and cloudiness), and the rainfall at the point of forecasting and rainfall at the surrounding stations, as an input for training of the model was found most satisfactory in forecasting rainfall in Bangkok, Thailand. The developed ANN model was applied to derive rainfall forecast from 1 to 6 h ahead at 75 rain gauge stations in the study area as forecast point from the data of 3 consecutive years (1997–1999). Results were highly satisfactory for rainfall forecast 1 to 3 h ahead. Sensitivity analysis indicated that the most important input parameter beside rainfall itself is the wet bulb temperature in forecasting rainfall. Based on these results, it is recommended that the developed ANN model can be used for real-time rainfall forecasting and flood management in Bangkok, Thailand.

Seasonal Precipitation Forecast Based on Artificial Neural Networks

Computational Methods for Agricultural Research ◽

10.4018/978-1-61692-871-1.ch016 ◽

2011 ◽

pp. 326-354 ◽

Cited By ~ 1

Author(s):

Adriano Rolim da Paz ◽

Cíntia Uvo ◽

Juan Bravo ◽

Walter Collischonn ◽

Humberto Ribeiro da Rocha

Keyword(s):

Time Horizon ◽

Precipitation Forecast ◽

Climate Indices ◽

Seasonal Precipitation ◽

Ann Model ◽

Interannual Climate Variability ◽

Study Case ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Favorable Conditions

Agriculture is vulnerable to the interannual climate variability and to its unpredictability, in such a way that most agricultural decisions taken within the time horizon of several months are made in a conservative manner, supposing a near-pessimist scenario. The improvement of climate prediction may help the strategic view, mitigating unwanted impacts and taking advantage of favorable conditions. This chapter presents the development of an Artificial Neural Network (ANN) model for seasonal precipitation forecast based on climate indices, focusing on the practical aspects of selecting the best predictors, defining ANN architecture, data handling and ANN training and validation. The study case is the Pardo/Mogi-Guaçu rivers watershed in Brazil, which is characterized by intense sugarcane plantation for both ethanol and sugar industries. The results demonstrate how the methodology for seasonal precipitation forecast based on ANN can be particularly helpful, with the use of available time series of climate indices.

Application of Artificial Neural Networks to Rainfall Forecasting in the Geum River Basin, Korea

Water ◽

10.3390/w10101448 ◽

2018 ◽

Vol 10 (10) ◽

pp. 1448 ◽

Cited By ~ 11

Author(s):

Jeongwoo Lee ◽

Chul-Gyum Kim ◽

Jeong Lee ◽

Nam Kim ◽

Hyeonjun Kim

Keyword(s):

River Basin ◽

Global Climate ◽

Early Summer ◽

Climate Indices ◽

Ann Model ◽

Rainfall Forecasting ◽

Study Region ◽

Artificial Neural ◽

Input Variables ◽

Mean Square Errors

This study develops a late spring-early summer rainfall forecasting model using an artificial neural network (ANN) for the Geum River Basin in South Korea. After identifying the lagged correlation between climate indices and the rainfall amount in May and June, 11 significant input variables were selected for the preliminary ANN structure. From quantification of the relative importance of the input variables, the lagged climate indices of East Atlantic Pattern (EA), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), East Pacific/North Pacific Oscillation (EP/NP), and Tropical Northern Atlantic Index (TNA) were identified as significant predictors and were used to construct a much simpler ANN model. The final best ANN model, with five input variables, showed acceptable performance with relative root mean square errors of 25.84%, 32.72%, and 34.75% for training, validation, and testing data sets, respectively. The hit score, which is the number of hit years divided by the total number of years, was more than 60%, which indicates that the ANN model successfully predicts rainfall in the study area. The developed ANN model, incorporated with lagged global climate indices, could allow for more timely and flexible management of water resources and better preparation against potential droughts in the study region.

Artificial Neural Network for Markov Chaining of Rainfall Over India

10.4018/978-1-6684-2408-7.ch053 ◽

2022 ◽

pp. 1130-1145

Author(s):

Kavita Pabreja

Keyword(s):

Neural Network ◽

Time Series Data ◽

Mean Squared Error ◽

Multiple Linear Regression Model ◽

Monthly Rainfall ◽

Series Data ◽

Rainfall Forecasting ◽

Artificial Neural ◽

Predicting the 14-Day Hospital Readmission of Patients with Pneumonia Using Artificial Neural Networks (ANN)

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18105110 ◽

2021 ◽

Vol 18 (10) ◽

pp. 5110

Author(s):

Shu-Farn Tey ◽

Chung-Feng Liu ◽

Tsair-Wei Chien ◽

Chin-Wei Hsu ◽

Kun-Chen Chan ◽

...

Keyword(s):

Neural Network ◽

Early Stage ◽

Characteristic Curve ◽

Patient Discharge ◽

Ann Model ◽

Healthcare Settings ◽

High Area ◽

Icd 10 ◽

Artificial Neural ◽

Unplanned patient readmission (UPRA) is frequent and costly in healthcare settings. No indicators during hospitalization have been suggested to clinicians as useful for identifying patients at high risk of UPRA. This study aimed to create a prediction model for the early detection of 14-day UPRA of patients with pneumonia. We downloaded the data of patients with pneumonia as the primary disease (e.g., ICD-10:J12*-J18*) at three hospitals in Taiwan from 2016 to 2018. A total of 21,892 cases (1208 (6%) for UPRA) were collected. Two models, namely, artificial neural network (ANN) and convolutional neural network (CNN), were compared using the training (n = 15,324; ≅70%) and test (n = 6568; ≅30%) sets to verify the model accuracy. An app was developed for the prediction and classification of UPRA. We observed that (i) the 17 feature variables extracted in this study yielded a high area under the receiver operating characteristic curve of 0.75 using the ANN model and that (ii) the ANN exhibited better AUC (0.73) than the CNN (0.50), and (iii) a ready and available app for predicting UHA was developed. The app could help clinicians predict UPRA of patients with pneumonia at an early stage and enable them to formulate preparedness plans near or after patient discharge from hospitalization.

Modeling Fused Filament Fabrication using Artificial Neural Networks

Production Engineering ◽

10.1007/s11740-021-01020-y ◽

2021 ◽

Author(s):

Paul Oehlmann ◽

Paul Osswald ◽

Juan Camilo Blanco ◽

Martin Friedrich ◽

Dominik Rietzel ◽

...

Keyword(s):

Real Time ◽

Large Scale ◽

Cost Effective ◽

Print Quality ◽

Ann Model ◽

Production Environment ◽

Fused Filament Fabrication ◽

Artificial Neural ◽

Using Data ◽

AbstractWith industries pushing towards digitalized production, adaption to expectations and increasing requirements for modern applications, has brought additive manufacturing (AM) to the forefront of Industry 4.0. In fact, AM is a main accelerator for digital production with its possibilities in structural design, such as topology optimization, production flexibility, customization, product development, to name a few. Fused Filament Fabrication (FFF) is a widespread and practical tool for rapid prototyping that also demonstrates the importance of AM technologies through its accessibility to the general public by creating cost effective desktop solutions. An increasing integration of systems in an intelligent production environment also enables the generation of large-scale data to be used for process monitoring and process control. Deep learning as a form of artificial intelligence (AI) and more specifically, a method of machine learning (ML) is ideal for handling big data. This study uses a trained artificial neural network (ANN) model as a digital shadow to predict the force within the nozzle of an FFF printer using filament speed and nozzle temperatures as input data. After the ANN model was tested using data from a theoretical model it was implemented to predict the behavior using real-time printer data. For this purpose, an FFF printer was equipped with sensors that collect real time printer data during the printing process. The ANN model reflected the kinematics of melting and flow predicted by models currently available for various speeds of printing. The model allows for a deeper understanding of the influencing process parameters which ultimately results in the determination of the optimum combination of process speed and print quality.

Modeling of lime production process using artificial neural network

Chemical Product and Process Modeling ◽

10.1515/cppm-2021-0032 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Abolghasem Daeichian ◽

Rana Shahramfar ◽

Elham Heidari

Keyword(s):

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.