Prediction Of Monthly Rainfall Research Articles

Application of Long Short-Term Memory (LSTM) Network for seasonal prediction of monthly rainfall across Vietnam

Application of Long Short-Term Memory (LSTM) Network for seasonal prediction of monthly rainfall across Vietnam

Comparison of Monthly Rainfall Prediction using Long Short Term Memory and Multi Layer Perceptron Methods in South Tangerang City

Rainfall is one of the meteorological and climatological parameters whose information must be disseminated to the public and related stakeholders. Rainfall information has an important role in the sectors of people's lives. In agriculture, the amount of rainfall has an important role in determining the planting season, so that this can prevent potential crop failure. On Disaster, South Tangerang City during the 2016-2021 period experienced floods, landslides, and droughts. Therefore, the importance of rainfall prediction information can improve meteorological and climatological information services in various sectors. Nevertheless, it is still difficult for the community and stakeholders to get monthly rainfall predictions with high accuracy in the long term. In this research, monthly rainfall prediction is designed using MLP (Multi Layer Perceptron) and LSTM (Long Short Term Memory). The data used is the monthly rainfall data of Climate Hazards Group InfraRed Precipitations (CHIRPS) for 42 years (period 1981-2022) with coordinate boundaries according to the research location, namely South Tangerang City, which is located between 106.625 º - 106.825 º East and 6.4 ° - 6.2 ° LS as many as 16 grids with a resolution of 0.05 ° each grid. Monthly rainfall prediction using MLP produces an RMSE value of 90.19, and a MAPE of 40.55, while the LSTM method produces an RMSE value of 88.12 and a MAPE of 40.49. Monthly rainfall prediction results using the LSTM method are better than the MLP method; this can be seen from the RMSE value of the LSTM method is smaller than MLP.

A Novel Coupled Model for Monthly Rainfall Prediction Based on ESMD-EWT-SVD-LSTM

A Novel Coupled Model for Monthly Rainfall Prediction Based on ESMD-EWT-SVD-LSTM

Hybridized gated recurrent unit with variational mode decomposition and an error compensation mechanism for multi-step-ahead monthly rainfall forecasting.

Highly accurate monthly rainfall predictions can provide early warnings for rain-related disasters, such as floods and droughts, and allow governments to make timely decisions. This paper proposes a two-phase error compensation model based on a gated recurrent unit (GRU), variational mode decomposition (VMD), and error compensation mechanism (ECM) (GRU-VMD-ECM) for accurate multi-step-ahead monthly rainfall forecasts. In the first phase, the GRU model is used to make an initial monthly rainfall prediction, and the error series is extracted. In the second phase, the error series is decomposed into eight subseries using the VMD method. Each subseries is then input into the GRU model to build different forecasting models. These predicted error sequences are added to the initial prediction results to obtain the final forecast. The model's performance is tested using six evaluation indicators based on Beijing's monthly rainfall data from 1951 to 2018. The results show that the error compensation mechanism significantly improved the prediction accuracy, particularly in the Nash-Sutcliffe efficiency (NSE) of single-step-ahead prediction which recorded a substantial increase of 281.16% from 0.259981 to 0.990944, as well as a decrease in root mean square error (RMSE) from 2.257580 to 0.249746. Furthermore, the GRU-VMD-ECM model outperforms the RF, GRU-CNN, and VMD-GRU models in terms of precision across all forecasting horizons. These findings highlight the potential of the GRU-VMD-ECM model in providing highly accurate monthly rainfall predictions for early warnings and informed decision-making by governments.

Data-driven multi-step prediction and analysis of monthly rainfall using explainable deep learning

Monthly rainfall prediction is a crucial topic for the management of water resources and prevention of hydrological disasters. To make a multi-step monthly rainfall prediction and discover the primary factors affecting rainfall, this study presents an explainable deep learning approach that integrates four consecutive modules, namely the Gated-Recurrent-Unit-based (GRU-based) encoder module, attention mechanism module, GRU-based decoder module, and expected-gradient-based explanation module, respectively. The first three modules constitute an encoder-decoder with an attention mechanism which could predict monthly rainfall in multiple continuous months in the future, while the explanation module computes attribution values for the input weather and climate features to quantify their importance. A case study is conducted on monthly rainfall data collected from Darwin and Perth, Australia, from Jan 1921 to Dec 2020. The results mainly indicate that: (1) Compared with the baseline methods, the multi-step monthly rainfall predictions made by the encoder-decoder with an attention mechanism show better agreement with the ground truth. (2) For January rainfall in Darwin, the most important weather factors are last January and February rainfall, while the most critical climate index is last February’s Southern Oscillation Index, with its high values inhibit while low values promote January rainfall in Darwin. (3) The most significant feature for June rainfall in Perth is last June’s solar radiation whose feature value is weakly negatively correlated with its attribution value. The study’s significance lies in enhancing the accuracy of multi-step rainfall prediction and providing interpretability through identification of influential factors, which facilitates long-term planning of water resources and deeper understanding of complex weather systems.

A Review of the use of Web-based Climate Information Systems for Agriculture Purposes

<p>The development of information system technology is needed in facing current and future agricultural challenges. An integrated agricultural information system is needed based on climate information system technology in answering this challenge. The Indonesian Agency for Agricultural Research and Development Ministry of Agriculture has developed a web-based Indonesia Integrated Cropping Calendar Information System (ICCIS). The ICCIS provides information for users in preparing planting plans. Likewise, Jaxa's Satellite-based Monitoring Network system (JASMIN-JAXA) web application that uses a satellite database covers all provinces in South Asia, Southeast Asia, and East Asia. This paper aims to review the use of web-based climate information systems in the ICCIS and the JASMIN-JAXA web-based application. Integrated climate information system in ICCIS, among others: flood estimation, drought and crop pests, prediction dry season and rainy season, district-level monthly rainfall prediction map. While JASMIN is a map (rainfall, drought index, soil moisture, groundwater index, solar radiation, surface temperature, and vegetation index) with time series and data sources. Both are able to monitor climate developments for agricultural purposes, dynamic, and growing. The importance of updating and verifying data in accordance with field conditions and equipment maintenance in the system is to be continuously improved to get benefits and accuracy of results in accordance with the interests of agriculture.<strong></strong></p>

A Review of the use of Web-based Climate Information Systems for Agriculture Purposes

<p>The development of information system technology is needed in facing current and future agricultural challenges. An integrated agricultural information system is needed based on climate information system technology in answering this challenge. The Indonesian Agency for Agricultural Research and Development Ministry of Agriculture has developed a web-based Indonesia Integrated Cropping Calendar Information System (ICCIS). The ICCIS provides information for users in preparing planting plans. Likewise, Jaxa's Satellite-based Monitoring Network system (JASMIN-JAXA) web application that uses a satellite database covers all provinces in South Asia, Southeast Asia, and East Asia. This paper aims to review the use of web-based climate information systems in the ICCIS and the JASMIN-JAXA web-based application. Integrated climate information system in ICCIS, among others: flood estimation, drought and crop pests, prediction dry season and rainy season, district-level monthly rainfall prediction map. While JASMIN is a map (rainfall, drought index, soil moisture, groundwater index, solar radiation, surface temperature, and vegetation index) with time series and data sources. Both are able to monitor climate developments for agricultural purposes, dynamic, and growing. The importance of updating and verifying data in accordance with field conditions and equipment maintenance in the system is to be continuously improved to get benefits and accuracy of results in accordance with the interests of agriculture.<strong></strong></p>

Artificial intelligence models for prediction of monthly rainfall without climatic data for meteorological stations in Ethiopia

Global climate change is affecting water resources and other aspects of life in many countries. Rainfall is the most significant climate element affecting the livelihood and well-being of the majority of Ethiopians. Rainfall variability has a great impact on agricultural production, water supply, transportation, the environment, and urban planning. Because all agricultural activities and subsequent national crop production hinge on the amount and distribution of rainfall, accurate monthly and seasonal predictions of this rainfall are vital for agricultural planning. Rainfall prediction is also useful for governmental, non-governmental, and private agencies in making long-term decisions and planning in numerous areas such as farming, early warning of potential hazards, drought mitigation, disaster prevention, and insurance policy. Artificial Intelligence (AI) has been widely used in almost every area, and rainfall prediction is one of them. In this study, we attempt to investigate the use of AI-based models to predict monthly rainfall at 92 Ethiopian meteorological stations. The applicability of Artificial Neural Networks (ANNs) and Adaptive Neuro-Fuzzy Inference System (ANFIS) models in predicting long-term monthly precipitation was investigated using geographical and periodicity component (longitude, latitude, and altitude) data collected from 2011 to 2021. The experimental results reveal that the ANFIS model outperforms the ANN model in all assessment criteria across all testing stations. The Nash–Sutcliffe efficiency coefficients were 0.995 for ANFIS and 0.935 for ANN over testing stations.

Long-Short Term Memory Technique for Monthly Rainfall Prediction in Thale Sap Songkhla River Basin, Thailand

Rainfall is a primary factor for agricultural production, especially in a rainfed agricultural region. Its accurate prediction is therefore vital for planning and managing farmers’ plantations. Rainfall plays an important role in the symmetry of the water cycle, and many hydrological models use rainfall as one of their components. This paper aimed to investigate the applicability of six machine learning (ML) techniques (i.e., M5 model tree: (M5), random forest: (RF), support vector regression with polynomial (SVR-poly) and RBF kernels (SVR- RBF), multilayer perceptron (MLP), and long-short-term memory (LSTM) in predicting for multiple-month ahead of monthly rainfall. The experiment was set up for two weather gauged stations located in the Thale Sap Songkhla basin. The model development was carried out by (1) selecting input variables, (2) tuning hyperparameters, (3) investigating the influence of climate variables on monthly rainfall prediction, and (4) predicting monthly rainfall with multi-step-ahead prediction. Four statistical indicators including correlation coefficient (r), mean absolute error (MAE), root mean square error (RMSE), and overall index (OI) were used to assess the model’s effectiveness. The results revealed that large-scale climate variables, particularly sea surface temperature, were significant influence variables for rainfall prediction in the tropical climate region. For projections of the Thale Sap Songkhla basin as a whole, the LSTM model provided the highest performance for both gauged stations. The developed predictive rainfall model for two rain gauged stations provided an acceptable performance: r (0.74), MAE (86.31 mm), RMSE (129.11 mm), and OI (0.70) for 1 month ahead, r (0.72), MAE (91.39 mm), RMSE (133.66 mm), and OI (0.68) for 2 months ahead, and r (0.70), MAE (94.17 mm), RMSE (137.22 mm), and OI (0.66) for 3 months ahead.

Monthly Rainfall Prediction at Catchment Level with the Facebook Prophet Model Using Observed and CMIP5 Decadal Data

Early prediction of rainfall is important for the planning of agriculture, water infrastructure, and other socio-economic developments. The near-term prediction (e.g., 10 years) of hydrologic data is a recent development in GCM (General Circulation Model) simulations, e.g., the CMIP5 (Coupled Modelled Intercomparison Project Phase 5) decadal experiments. The prediction of monthly rainfall on a decadal time scale is an important step for catchment management. Previous studies have considered stochastic models using observed time series data only for rainfall prediction, but no studies have used GCM decadal data together with observed data at the catchment level. This study used the Facebook Prophet (FBP) model and six machine learning (ML) regression algorithms for the prediction of monthly rainfall on a decadal time scale for the Brisbane River catchment in Queensland, Australia. Monthly hindcast decadal precipitation data of eight GCMs (EC-EARTH MIROC4h, MRI-CGCM3, MPI-ESM-LR, MPI-ESM-MR, MIROC5, CanCM4, and CMCC-CM) were downloaded from the CMIP5 data portal, and the observed data were collected from the Australian Bureau of Meteorology. At first, the FBP model was used for predictions based on: (i) the observed data only; and (ii) a combination of observed and CMIP5 decadal data. In the next step, predictions were performed through ML regressions where CMIP5 decadal data were used as features and corresponding observed data were used as target variables. The prediction skills were assessed through several skill tests, including Pearson Correlation Coefficient (PCC), Anomaly Correlation Coefficient (ACC), Index of Agreement (IA), and Mean Absolute Error (MAE). Upon comparing the skills, this study found that predictions based on a combination of observed and CMIP5 decadal data through the FBP model provided better skills than the predictions based on the observed data only. The optimal performance of the FBP model, especially for the dry periods, was mainly due to its multiplicative seasonality function.

IMPORTANCE OF UPDATING FOR MONTHLY RAINFALL PREDICTION BASED ON ECMWFs4

<em>There is uniqueness in climate services in East Java. Rainfall information is delivered as N-1 analysis for ongoing months N+1, N+2, and N+3 for monthly prediction. This study aims to investigate whether updating monthly predictions improves prediction accuracy. The verification method for this study is based on the percentage accuracy of the rain class category according to SNI 8196: 2015. The data used for this study is ECMWF's monthly rainfall prediction that has three lags system (1, 2, and 3). Rasters of monthly rainfall interpolation from the main rainfall observation (197 locations) in East Java from April 2015 to May 2020 (62 months) are used for the verification process. The temporal and spatial analysis then conducted using R (+ package raster). Studies based on the local governmental zone are also used. In general, the result shows that almost all months need updating, except April-September-October. Verification of ECMWFs4 shows a better verification result (0,56) in the past five years (2016-2020) for March. The regions that need monthly updating are Bawean island, the coast of Gresik, Pasuruan, and Banyuwangi</em>

A novel rainfall prediction model based on CEEMDAN-PSO-ELM coupled model

Abstract Rainfall prediction is a very important guideline for water resources management as well as ecological protection, and its changes are the result of multiple factors with obvious uncertainties and nonlinearities. Based on the advantages of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) non-smooth signal decomposition, the Particle Swarm Optimization (PSO) can be used to optimize the input weights and thresholds of the Extreme Learning Machine (ELM), which can effectively improve the prediction effect and accuracy of ELM, and a rainfall prediction model based on CEEMDAN-PSO-ELM is constructed. The model is applied to the monthly rainfall prediction of Zhongwei City, and the results show that the CEEMDAN-PSO-ELM coupled model has a high prediction accuracy, the mean absolute error (MAE) is 1.29, relative percentage error (RPE) is 0.45, root mean square error (RMSE) is 1.43 and the Nash efficiency coefficient (NSE) is 0.93. It has obvious advantages in hydrological simulation prediction when compared and analyzed with the deep Long-Short Term Memory (LSTM), PSO-ELM coupled model and ELM model.

A Stacking Ensemble Learning Model for Monthly Rainfall Prediction in the Taihu Basin, China

The prediction of monthly rainfall is greatly beneficial for water resources management and flood control projects. Machine learning (ML) techniques, as an increasingly popular approach, have been applied in diverse climatic regions, showing their respective superiority. On top of that, the ensemble learning model that synthesizes the advantages of different ML models deserves more attention. In this study, an ensemble learning model based on stacking approach was proposed. Four prevalent ML models, namely k-nearest neighbors (KNN), extreme gradient boosting (XGB), support vector regression (SVR), and artificial neural networks (ANN) are taken as base models. To combine the outputs from the base models, the weighting algorithm is used as second-layer learner to generate predictions. Large-scale climate indices, large-scale atmospheric variables, and local meteorological variables were used as predictors. R2, RMSE and MAE, were used as evaluation metrics. The results show that the performance of base models varied among the nine stations in the Taihu Basin, while the stacking approach generally performed better than the four base models. The stacking model showed better performance in spring and winter than in summer and autumn. During wet months, the accuracy of model prediction varied more significantly. On the whole, based on performance evaluation measures, it is concluded that the proposed stacking ensemble multi-ML model can provide a flexible and reasonable prediction framework applicable to other regions.

Development of ANN models using monthly rainfall for central Telangana region

In the present study, artificial neural network technique has been employed to predict monthly rainfall for Medak, Khammam and Warangal stations of Central Telangana, India. The eighty-five years (January, 1901 to December, 1985) of rainfall data were used for training of models and twenty-eight years (January, 1986 to December, 2014) of rainfall data were used for testing of models. Gamma test, autocorrelation function and cross correlation function were used for selection of appropriate input variables. The ANN models were trained using multilayer perceptron with two learning rules i.e. Levenberg-Marquardt and Delta-bar-delta and two transfer functions viz. Sigmoid axon and Tanh axon. It was observed that the better results of monthly rainfall prediction of developed models were observed when rainfall of adjoining stations was used as inputs variable as compared to lagged rainfall of the same station. suggest that the M-8 model, K-7 model and W-5 may be used to predict monthly rainfall of Medak, Khammam and Warangal stations respectively for Central Telangana region.

Application of Feed Forward Backpropagation Neural Network in Monthly Rainfall Prediction

Application of Feed Forward Backpropagation Neural Network in Monthly Rainfall Prediction

Simulasi Prediksi Total Hujan Bulanan di Tanjungpinang (Studi Kasus Tahun 2017)

The amount of rainfall that occurs in an area is affected by meteorological and non-meteorological factors. Meteorological factors or physical parameters such as air temperature, air pressure, air humidity, wind speed, and solar radiation time are indicated to affect the amount of rainfall. Simulation of the estimated monthly rainfall for 2017 using the 1981-2016 weather parameter data in Tanjungpinang was formulated using the multiple linear regression equation method. Validating of the correct results of the estimated amount of rainfall on the actual rainfall using Pearson Correlation to determine data deviations occurs between predictor to observation data. The data processing results show that the monthly rainfall prediction simulation will be quite good if the weather conditions in an area are in a normal condition which there is no weather and climate anomaly especially on a global, regional and local factor scale.

Statistical Modeling of Monthly Rainfall in Selected Stations in Forest and Savannah Eco-climatic Regions of Nigeria

Objective: Nigerian agriculture is mainly rain-fed and highly dependent on weather especially rainfall. Therefore modeling of monthly rainfall in some selected stations in Nigeria was undertaken in this study. Methodology: Data (rainfall) spanning a period of 30 years (1981-2010) for fourteen stations which were collected from the Nigerian Meteorological Agency (NIMET) were utilized. Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA) models were used. The accuracy and trend of time series was analyzed to give the monthly rainfall prediction for the succeeding year. The results showed that the model fitted into the data well and the stochastic seasonal fluctuation was successfully modeled. Rainfall was minimal in January, February, March and December over the selected stations in northern, Nigeria but increased progressively in strength and amount in the months of June, August and September over the stations in South west, and June and September over the stations in South -south, Nigeria. The highest rainfall of 230 mm was recorded in September over Warri and the lowest rainfall of 52 mm was recorded in August over Maiduguri. The rainfall recorded over the selected stations in South-south stations was visibly higher than what was recorded over the stations in the northern and the South-west stations. In northern Nigeria, the peak monthly mean rainfall amount of 91 mm was observed in August and rainfall amount was very low in January (0.0 mm), February (0.0 mm), March (0.0 mm) and December (0.0 mm). Over South-west, the Peak monthly mean rainfall amount of 215 mm was observed in June and September and rainfall amount was very low in January (0.0 mm) and December (0.0 mm). Over the stations in South-south, the Peak monthly mean rainfall amount of 325 mm was experienced on September and rainfall amount is very low in December (0.0 mm). Conclusion: The study concluded that Seasonal Autoregressive Integrated Moving Average (SARIMA) model was a proper method for modeling and predicting the monthly rainfall. The results are useful for forecasting the pattern of rainfall in the study area and provide information that would be helpful for decision makers in formulating policies to mitigate the problems of water resources management, soil erosion, flooding and drought.

Ascertaining Time Series Predictability in Process Control – Case Study on Rainfall Prediction

Rainfall prediction is a challenging task due to its dependency on many natural phenomenon. Some authors used Hurst exponent as a predictability indicator to ensure predictability of the time series before prediction. In this paper, a detailed analysis has been done to ascertain whether a definite relation exists between a strong Hurst exponent and predictability. The one-lead monthly rainfall prediction has been done for 19 rain gauge station of the Yarra river basin in Victoria, Australia using Artificial Neural Network. The prediction error in terms of normalized Root Mean Squared Error has been compared with Hurst exponent. The study establishes the truth of the hypothesis for only 6 stations out of 19 stations, and thus recommends further investigation to prove the hypothesis. This concept is relevant for any time series which need to be used for real time process control.

Prediction of Monthly Summer Monsoon Rainfall Using Global Climate Models Through Artificial Neural Network Technique

The monthly prediction of summer monsoon rainfall is very challenging because of its complex and chaotic nature. In this study, a non-linear technique known as Artificial Neural Network (ANN) has been employed on the outputs of Global Climate Models (GCMs) to bring out the vagaries inherent in monthly rainfall prediction. The GCMs that are considered in the study are from the International Research Institute (IRI) (2-tier CCM3v6) and the National Centre for Environmental Prediction (Coupled-CFSv2). The ANN technique is applied on different ensemble members of the individual GCMs to obtain monthly scale prediction over India as a whole and over its spatial grid points. In the present study, a double-cross-validation and simple randomization technique was used to avoid the over-fitting during training process of the ANN model. The performance of the ANN-predicted rainfall from GCMs is judged by analysing the absolute error, box plots, percentile and difference in linear error in probability space. Results suggest that there is significant improvement in prediction skill of these GCMs after applying the ANN technique. The performance analysis reveals that the ANN model is able to capture the year to year variations in monsoon months with fairly good accuracy in extreme years as well. ANN model is also able to simulate the correct signs of rainfall anomalies over different spatial points of the Indian domain .

Prediction of rainfall using artificial neural networks for synoptic station of Mashhad: a case study

In this paper, we have utilized ANN (artificial neural network) modeling for the prediction of monthly rainfall in Mashhad synoptic station which is located in Iran. To achieve this black-box model, we have used monthly rainfall data from 1953 to 2003 for this synoptic station. First, the Hurst rescaled range statistical (R/S) analysis is used to evaluate the predictability of the collected data. Then, to extract the rainfall dynamic of this station using ANN modeling, a three-layer feed-forward perceptron network with back propagation algorithm is utilized. Using this ANN structure as a black-box model, we have realized the complex dynamics of rainfall through the past information of the system. The approach employs the gradient decent algorithm to train the network. Trying different parameters, two structures, M531 and M741, have been selected which give the best estimation performance. The performance statistical analysis of the obtained models shows with the best tuning of the developed monthly prediction model the correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE) are 0.93, 0.99, and 6.02 mm, respectively, which confirms the effectiveness of the developed models.