Improving Monthly Rainfall Forecast in a Watershed by Combining Neural Networks and Autoregressive Models

Pérez‐Alarcón, Albenis; Cortés, Daniel A. García; Fernández‐Alvarez, José C.; Martínez-González, Yoel

doi:10.1007/s40710-022-00602-x

Cited by 39 publications

(4 citation statements)

References 113 publications

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“…The differentiation between LSTM and conventional RNNs lies in their respective internal architectures. One of the most salient features of the LSTM model is its cell state, which facilitates the propagation of information across the entire sequence through a series of linear interactions (Pérez-Alarcón et al, 2022).…”

Section: Methodsmentioning

confidence: 99%

“…Although they could fairly accurately simulate monthly rainfall, the models were unable to replicate some of the highest monthly rainfall values. The size of the training data and the small number of heavy rainfall events may have made it difficult for models to learn such features (Pérez-Alarcón et al, 2022). Furthermore, the performance of all models' predictions varies significantly, both within and between climate zones, with more accurate performance in the Sudanian climate zone having a unimodal rainfall regime and less accurate performance in the Guinean zone having a bimodal rainfall regime.…”

Section: E11-20mentioning

confidence: 99%

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Monthly rainfall prediction using artificial neural network (case study: Republic of Benin)

Aïzansi,

Ogunjobi,

Ogou

2024

Environ. Data Science

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Complex physical processes that are inherent to rainfall lead to the challenging task of its prediction. To contribute to the improvement of rainfall prediction, artificial neural network (ANN) models were developed using a multilayer perceptron (MLP) approach to predict monthly rainfall 2 months in advance for six geographically diverse weather stations across the Benin Republic. For this purpose, 12 lagged values of atmospheric data were used as predictors. The models were trained using data from 1959 to 2017 and tested for 4 years (2018–2021). The proposed method was compared to long short-term memory (LSTM) and climatology forecasts (CFs). The prediction performance was evaluated using five statistical measures: root mean square error, mean absolute error, mean absolute percentage error, coefficient of determination, and Nash–Sutcliffe efficiency (NSE) coefficient. Furthermore, Taylor diagrams, violin plots, box error, and Kruskal–Wallis test were used to assess the robustness of the model’s forecast. The results revealed that MLP gives better results than LSTM and CF. The NSE obtained with the MLP, LSTM, and CF models during the test period ranges from 0.373 to 0.885, 0.297 to 0.875, and 0.335 to 0.845, respectively, depending on the weather station. Rainfall predictability was more accurate, with 0.512 improvement in NSE using MLP at higher latitudes across the country, showing the effect of geographic regions on prediction model results. In summary, this research has revealed the potential of ANN techniques in predicting monthly rainfall 2 months ahead, supplying valuable insights for decision-makers in the Republic of Benin.

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Section: Methodsmentioning

confidence: 99%

Section: E11-20mentioning

confidence: 99%

Monthly rainfall prediction using artificial neural network (case study: Republic of Benin)

Aïzansi,

Ogunjobi,

Ogou

2024

Environ. Data Science

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“…Due to the availability of big data in drought forecasts, many researchers have benefited from ML. Because of these, ML is thought to help sustainable ecosystem (Deparday et al 2019;Pérez-Alarcón et al 2022;Pham et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Improvement of Forecasting Drought by Means of Various Machine Learning Algorithms and Wavelet Transformation

Tuğrul,

HINIS

2024

Preprint

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Defined as a decrease in average rainfall amounts, drought is one of the most insidious natural disasters. When it starts, people may not be aware of it, that's why droughts are difficult to monitor. Scientists have long been working to predict and monitor droughts. For this purpose, they have developed many methods such as drought indices one of which Standardized Precipitation Index (SPI) is. In this study, SPI to detect droughts and machine learning algorithms, support vector machines (SVM), artificial neural networks (ANN), random forest (RF), decision tree (DT), frequently used in the literature to predict droughts and 3 different statistical methods: correlation coefficient (r), Root Mean-square Error (RMSE), Nash-Sutcliffe Efficiency (NSE) Coefficient to investigate model performance values were used. Wavelet analysis was also applied to improve model performances. Konya closed basin located in the middle of Türkiye in terms of location and is among the leading regions of Turkey in terms of grain is one of the regions most affected by droughts in Türkiye. One of the most important water resources of the region is the Apa dam. It provides water to many fields which fertile land in its vicinity and is affected by droughts. Therefore, this region was determined as the study area. Meteorological data, total monthly precipitation, that could represent the region were obtained between 1955 and 2020 from general directorate of state water works and general directorate of meteorology. The results show that among the models analyzed with machine learning algorithms, the best results were obtained from M04 model whose input structure was created from SPI, different times steps, data delayed up to 5 months and total monthly precipitation data for time t-1. Among machine learning algorithms, SVM has achieved the most successful results in not only without wavelet transform (WT) but also with WT. Effective results were obtained from M04 in which SVM with WT was used (NSE = 0.9942, RMSE = 0.0764, R = 0.9971).

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“…Process-driven and data-driven methodologies can be used to broadly classify the models of precipitation prediction now in use. Unlike process-driven models, data-driven models are exempt from taking into account the physical mechanisms underlying runoff generation 2 , 3 . Rather, they only use mathematical analysis of time-series data to determine the functional relationships between the variables that are input and output, which increases tractability 4 .…”

Section: Introductionmentioning

confidence: 99%

A novel optimization rainfall coupling model based on stepwise decomposition technique

Zheng,

Zhang,

Yin

et al. 2024

Sci Rep

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Traditional decomposition integration models decompose the original sequence into subsequences, which are then proportionally divided into training and testing periods for modeling. Decomposition may cause data aliasing, then the decomposed training period may contain part of the test period data. A more effective method of sample construction is sought in order to accurately validate the model prediction accuracy. Semi-stepwise decomposition (SSD), full stepwise decomposition (FSD), single model semi-stepwise decomposition (SMSSD), and single model full stepwise decomposition (SMFSD) techniques were used to create the samples. This study integrates Variational Mode Decomposition (VMD), African Vulture Optimization Algorithm (AVOA), and Least Squares Support Vector Machine (LSSVM) to construct a coupled rainfall prediction model. The influence of different VMD parameters α is examined, and the most suitable stepwise decomposition machine learning coupled model algorithm for various stations in the North China Plain is selected. The results reveal that SMFSD is relatively the most suitable tool for monthly precipitation forecasting in the North China Plain. Among the predictions for the five stations, the best overall performance is observed at Huairou Station (RMSE of 18.37 mm, NSE of 0.86, MRE of 107.2%) and Jingxian Station (RMSE of 24.74 mm, NSE of 0.86, MRE of 51.71%), while Hekou Station exhibits the poorest performance (RMSE of 25.11 mm, NSE of 0.75, MRE of 173.75%).

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Improving Monthly Rainfall Forecast in a Watershed by Combining Neural Networks and Autoregressive Models

Cited by 39 publications

References 113 publications

Monthly rainfall prediction using artificial neural network (case study: Republic of Benin)

Monthly rainfall prediction using artificial neural network (case study: Republic of Benin)

Improvement of Forecasting Drought by Means of Various Machine Learning Algorithms and Wavelet Transformation

A novel optimization rainfall coupling model based on stepwise decomposition technique

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