Monthly rainfall forecasting modelling based on advanced machine learning methods: tropical region as case study

Allawi, Mohammed Falah; Abdulhameed, Uday Hatem; Adham, Ammar; Sayl, Khamis Naba; Sulaiman, Sadeq Oleiwi; Ramal, Majeed Mattar; Sherif, Mohsen; El-Shafie, Ahmed

doi:10.1080/19942060.2023.2243090

Cited by 11 publications

(2 citation statements)

References 54 publications

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“…Artificial neural networks (ANNs) are advantageous in water quality index applications for various reasons. They can capture complex relationships between water quality parameters, enhancing prediction accuracy [41,42]. ANNs are adaptable to evolving conditions, integrate data from multiple sources for comprehensive evaluations, and handle missing data effectively.…”

Section: Introductionmentioning

confidence: 99%

Applying Remote Sensing and Artificial Neural Networks for Water Quality Index Modeling in the Euphrates River

Jassam,

Mawlood,

Sayl

2024

IJDNE

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The Water Quality Index (WQI) is an effective water test that assesses water quality, identifies contaminants, and aids in decision-making. However, it is inefficient to analyze water samples in laboratories due to high costs, time-consuming processes, and limited ability to record temporal or geographical oscillations. Recently, the use of modern technologies such as Remote Sensing (RS) data, Geographic Information Systems (GIS), and Artificial Neural Networks (ANN), in combination with survey data, has confirmed an efficient tool to generate the WQI map of the Euphrates River in Ramadi, Iraq. In the present study, the RS data, such as Landsat 8 and Landsat 9 images, and laboratory tests of samples were used to develop a database for WQI based on spectral reflectance using the radial basis neural network model. The result of this model was then manipulated within ArcGIS 10.8 using the spatial analyst model to generate a digital map of WQI. This model was evaluated using seven criteria, which are correlation coefficient (r), mean absolute error, normalized mean absolute error, lowest absolute error, maximum absolute error, and root square equation of the coefficients (RMSE). The correlation value of the WQI was 0.93, which shows remarkable prediction accuracy. Therefore, this calculation method is effective in calculating the WQI and producing precise digital maps of water quality.

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Section: Introductionmentioning

confidence: 99%

Applying Remote Sensing and Artificial Neural Networks for Water Quality Index Modeling in the Euphrates River

Jassam,

Mawlood,

Sayl

2024

IJDNE

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“…Although meteorologists and forecasters have committed to improving the accuracy and timeliness of rainfall forecasts, these aspects of forecasts are still difficult to meet the needs of operations and society due to factors such as limitations in the means of detection and insufficient knowledge of the microphysical processes of rainfall [5,6]. Furthermore, rainfall forecasting for typhoon landfall events, especially estimating the affected areas, is a great challenge [7,8].…”

Section: Introductionmentioning

confidence: 99%

Contribution from the Western Pacific Subtropical High Index to a Deep Learning Typhoon Rainfall Forecast Model

Fang,

Cheung,

Yang

2024

Remote Sensing

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In this study, a tropical cyclone or typhoon rainfall forecast model based on Random Forest is developed to forecast the daily rainfall at 133 weather stations in China. The input factors to the model training process include rainfall observations during 1960–2018, typhoon information (position and intensity), station information (position and altitude), and properties of the western Pacific subtropical high. Model evaluation shows that besides the distance between a station and cyclone, the subtropical high properties are ranked very high in the model’s feature importance, especially the subtropical ridgeline, and intensity. These aspects of the subtropical high influence the location and timing of typhoon landfall. The forecast model has a correlation coefficient of about 0.73, an Index of Agreement of nearly 0.8, and a mean bias of 1.28 mm based on the training dataset. Biases are consistently low, with both positive and negative signs, for target stations in the outer rainband (up to 1000 km, beyond which the model does not forecast) of typhoons. The range of biases is much larger for target stations in the inner-core (0–200 km) region. In this region, the model mostly overestimates (underestimates) the small (large) rain rates. Cases study of Typhoon Doksuri and Talim in 2023, as independent cases, shows the high performance of the model in forecasting the peak rain rates and timing of their occurrence of the two impactful typhoons.

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A Multi-Functional Genetic Algorithm-Neural Network Model for Predicting Suspended Sediment Loads

Afan,

Wan Mohtar,

Aksoy

et al. 2024

Water Resour Manage

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Monthly rainfall forecasting modelling based on advanced machine learning methods: tropical region as case study

Cited by 11 publications

References 54 publications

Applying Remote Sensing and Artificial Neural Networks for Water Quality Index Modeling in the Euphrates River

Applying Remote Sensing and Artificial Neural Networks for Water Quality Index Modeling in the Euphrates River

Contribution from the Western Pacific Subtropical High Index to a Deep Learning Typhoon Rainfall Forecast Model

A Multi-Functional Genetic Algorithm-Neural Network Model for Predicting Suspended Sediment Loads

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