Forecasting monthly precipitation using sequential modelling

Kumar, Deepak; Singh, Anshuman; Samui, Pijush; Jha, Rishi Kumar

doi:10.1080/02626667.2019.1595624

Cited by 112 publications

(45 citation statements)

References 32 publications

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“…In addition, when the model has been examined using different inputs with different time series it achieved outstanding performance, proving that the model can act well with different and new input data. P 0 is the value that has been calculated based on Equation (16) while Pe is the (assumed or supposed) probability of chance agreement, then finally we can calculate the Kappa coefficient, to represent the uncertainty pattern of the model output. In fact, the higher the value of the Kappa coefficient, the better the overall agreement level between the model output and the observed data.…”

Section: Hydrological Analysis Of Resultsmentioning

confidence: 99%

“…The results showed that the neuro fuzzy-ant colony optimization algorithm not only decreases the relative error and mean absolute error (MAE) but also has a higher convergence rate than the other models. Kumar et al [16] used recurrent neural network to forecast precipitation in India. Monthly precipitation data were considered from the years 1871 to 2016 to forecast monthly precipitation.…”

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confidence: 99%

“…where Y obs i is observed data, Y sim i is simulated data, Y mean is the mean data, MAE is the mean absolute error of data, RSR is the root mean square error-observations standard deviation ratio, and NSE is the Nash-Sutcliffe efficiency coefficient. The above indexes are widely used to evaluate the performances of the soft computing models [15][16][17][18][19][20][21]30].…”

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confidence: 99%

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Precipitation Forecasting Using Multilayer Neural Network and Support Vector Machine Optimization Based on Flow Regime Algorithm Taking into Account Uncertainties of Soft Computing Models

et al. 2019

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Drought, climate change, and demand make precipitation forecast a very important issue in water resource management. The present study aims to develop a forecasting model for monthly precipitation in the basin of the province of East Azarbaijan in Iran over a ten-year period using the multilayer perceptron neural network (MLP) and support vector regression (SVR) models. In this study, the flow regime optimization algorithm (FRA) was applied to optimize the multilayer neural network and support vector machine. The flow regime optimization algorithm not only identifies the parameters of the SVR and MLP models but also replaces the training algorithms. The decision tree model (M5T) was also used to forecast precipitation and compare it with the results of hybrid models. Principal component analysis (PCA) was used to identify effective indicators for precipitation forecast. In the first scenario, the input data include temperature data with a delay of one to twelve months, the second scenario includes precipitation data with a delay of one to twelve months, and the third scenario includes precipitation and temperature data with a delay of one to three months. The mean absolute error (MAE) and Nash-Sutcliffe error (NSE) indices were used to evaluate the performance of the models. The results showed that the proposed MLP-FRA outperformed all the other examined models. Regarding the uncertainties of the models, it was also shown that the MLP-FRA model had a lower uncertainty band width than other models, and a higher percentage of the data will fall within the range of the confidence band. As the selected scenario, Scenario 3 had a better performance. Finally, monthly precipitation maps were generated based on the MLP-FRA model and Scenario 3 using the weighted interpolation method, which showed significant precipitation in spring and winter and a low level of precipitation in summer. The results of the present study showed that MLP-FRA has high capability to predict hydrological variables and can be used in future research.Consecutive droughts and increasing water demands mean that prediction and planning for the usage of precipitation are necessary for decision makers [1,2]. In addition, precipitation forecasting is necessary to prevent floods and construct flood-controlling structures. What is more, precipitation forecasting is one of the main issues of the water resources planning that can reduce the effects of drought [3]. Researchers have used various statistical and hydrological models to predict precipitation; however, it should be noted that precipitation patterns are dependent on various parameters and have a nonlinear behavior. Therefore, these complexities result in uncertainties in precipitation forecasting models. In recent years, researchers have used soft computing models to forecast hydrological variables such as precipitation [4]. Models such as artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM), or improved regression models and optimization al...

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Section: Hydrological Analysis Of Resultsmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Precipitation Forecasting Using Multilayer Neural Network and Support Vector Machine Optimization Based on Flow Regime Algorithm Taking into Account Uncertainties of Soft Computing Models

et al. 2019

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“…The state of art DL capabilities have not yet been tested in hydrological modelling and there are only a few DL applications so far (Shen et al, 2018). Successful DL applications in hydrology include rainfall-runoff modelling (Hu et al, 2018;Fan et al, 2020;Xiang et al, 2020), soil moisture modelling (Xiaodong et al, 2016), precipitation forecasting (Kumar et al, 2019), groundwater estimation (Afzaal et al, 2019) and uncertainty estimation (Gude et al, 2020).…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Hydrologically Informed Machine Learning for Rainfall-Runoff Modelling: Towards Distributed Modelling

Herath¹,

Chadalawada²,

Babovic³

2020

Preprint

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Abstract. Despite showing a great success of applications in many commercial fields, machine learning and data science models in general, show a limited use in scientific fields including hydrology. The approach is often criticized for lack of interpretability and physical consistency. This has led to the emergence of new paradigms, such as Theory Guided Data Science (TGDS) and physics informed machine learning. The motivation behind such approaches is to improve the physical meaningfulness of machine learning models by blending existing scientific knowledge with learning algorithms. Following the same principles, in our prior work (Chadalawada et al., 2020), a new model induction framework was founded on Genetic Programming (GP) namely Machine Learning Rainfall-Runoff Model Induction Toolkit (ML-RR-MI). ML-RR-MI is cable of developing fully-fledged lumped conceptual rainfall-runoff models for a watershed of interest using the building blocks of two flexible rainfall-runoff modelling frameworks (FUSE and SUPERFLEX). In this study, we extend ML-RR-MI towards inducing semi-distributed rainfall-runoff models. This effort is motivated by the desire to address the decreasing meaningfulness of lumped models which tend to particularly deteriorate within large catchments where the spatial heterogeneity of forcing variables and watershed properties are significant. Henceforth, our machine learning approach for rainfall-runoff modelling titled Machine Induction Knowledge-Augmented System Hydrologique Asiatique (MIKA-SHA) captures spatial variabilities and automatically induces rainfall-runoff models for the catchment of interest without any subjectivity in model selection. Currently, MIKA-SHA learns models utilizing the model building components of FUSE and SUPERFLEX. However, the proposed framework can be coupled with any internally coherent collection of building blocks. MIKA-SHA’s model induction capabilities have been tested on the Red Creek catchment near Vestry, Mississippi, United States. The resulted model architectures through MIKA-SHA are compatible with previously reported research findings and fieldwork insights of the watershed and are readily interpretable by hydrologists.

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“…Misra et al [ 67 ] used the LSTM model to capture the spatiotemporal dependencies in local rainfall. Kumar et al [ 68 ] used LSTM for forecasting monthly rainfall by using long sequential raw data for time-series analysis. Chhetri et al [ 69 ] presented a GRU-based model for rainfall prediction using weather parameters (temperature, rainfall, relative humidity, sunshine hour, and wind speed).…”

Section: Introductionmentioning

confidence: 99%

Real-Time Rainfall Forecasts Based on Radar Reflectivity during Typhoons: Case Study in Southeastern Taiwan

Wei

Hsu

2021

Sensors

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This study developed a real-time rainfall forecasting system that can predict rainfall in a particular area a few hours before a typhoon’s arrival. The reflectivity of nine elevation angles obtained from the volume coverage pattern 21 Doppler radar scanning strategy and ground-weather data of a specific area were used for accurate rainfall prediction. During rainfall prediction and analysis, rainfall retrievals were first performed to select the optimal radar scanning elevation angle for rainfall prediction at the current time. Subsequently, forecasting models were established using a single reflectivity and all elevation angles (10 prediction submodels in total) to jointly predict real-time rainfall and determine the optimal predicted values. This study was conducted in southeastern Taiwan and included three onshore weather stations (Chenggong, Taitung, and Dawu) and one offshore weather station (Lanyu). Radar reflectivities were collected from Hualien weather surveillance radar. The data for a total of 14 typhoons that affected the study area in 2008–2017 were collected. The gated recurrent unit (GRU) neural network was used to establish the forecasting model, and extreme gradient boosting and multiple linear regression were used as the benchmarks. Typhoons Nepartak, Meranti, and Megi were selected for simulation. The results revealed that the input data set merged with weather-station data, and radar reflectivity at the optimal elevation angle yielded optimal results for short-term rainfall forecasting. Moreover, the GRU neural network can obtain accurate predictions 1, 3, and 6 h before typhoon occurrence.

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Forecasting monthly precipitation using sequential modelling

Cited by 112 publications

References 32 publications

Precipitation Forecasting Using Multilayer Neural Network and Support Vector Machine Optimization Based on Flow Regime Algorithm Taking into Account Uncertainties of Soft Computing Models

Precipitation Forecasting Using Multilayer Neural Network and Support Vector Machine Optimization Based on Flow Regime Algorithm Taking into Account Uncertainties of Soft Computing Models

Hydrologically Informed Machine Learning for Rainfall-Runoff Modelling: Towards Distributed Modelling

Real-Time Rainfall Forecasts Based on Radar Reflectivity during Typhoons: Case Study in Southeastern Taiwan

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