Rainfall Prediction Model Research Articles

A Novel Combined Prediction Model for Monthly Mean Precipitation With Error Correction Strategy

Precipitation is an important parameter of water resource management, flood warning and hydrological analysis, so it is important to predict rainfall accurately. However, many previous studies did not extract the information of error series and only used a single model to predict rainfall data, ignoring the importance of model stability. Therefore, based on the idea of combination prediction and error correction strategy, this paper proposes a novel combined prediction model for monthly mean precipitation. It combines the variational mode decomposition (VMD), the improved butterfly optimization algorithm (IBOA), the least squares support vector machine model (LSSVM), the adaptive Volterra and autoregressive moving average (ARMA) model. Firstly, in order to find the best parameters of LSSVM, an improved butterfly optimization algorithm is proposed. The simulation results show the performance of IBOA is better than that of other algorithms, such as PSO, DE and BOA. Then the IBOA-LSSVM model and Volterra model are established for the mode components of the VMD, named VMD-IBOA-LSSVM and VMD-Volterra. Secondly, to solve the problem that the uncertainty of the hydrological prediction model, a combined precipitation prediction method based on the induced ordered weighted average (IOWA) operator of VMD-IBOA-LSSVM and VMD-Volterra is proposed. Finally, the ARMA model is established to correct the error sequence of the combined forecasting model. The precipitation data of two stations in Shaanxi Province are predicted. Experiment 1 is taken as an example, the maximum error of the proposed prediction model for rainfall is less than 9 mm, and the performance of the proposed model is improved by at least 43%. It shows that the proposed model can effectively reduce the prediction error of precipitation, and provide a new idea for precipitation prediction.

Improvement of Rainfall Prediction Model by Using Fuzzy Logic

This paper presents the improvement of the fuzzy inference model for predicting rainfall. Fuzzy rule based system is used in this study to predict rainfall. Fuzzy inference is the actual procedure of mapping with a given set of input and output through a set of fuzzy systems. Two operations were performed on the fuzzy logic model; the fuzzification operation and defuzzification operation. This study is obtaining two input variables and one output variable. The input variables are temperature and wind speed at a particular time and output variable is the amount of predictable rainfall. Temperature, wind speed and rainfall have to construct eight equations for different categories and which are shows the diagram of the graph. Fuzzy levels and membership functions obtained after minimum composition of inference part of the fuzzifications done for temperature and wind speed are considered as they represent the environmental condition enhance a rainfall occurrence which is effect on agricultural production.

Comparative Analysis of Rainfall Prediction Models Using Machine Learning in Islands with Complex Orography: Tenerife Island

We present a comparative study between predictive monthly rainfall models for islands of complex orography using machine learning techniques. The models have been developed for the island of Tenerife (Canary Islands). Weather forecasting is influenced both by the local geographic characteristics as well as by the time horizon comprised. Accuracy of mid-term rainfall prediction on islands with complex orography is generally low when carried out with atmospheric models. Predictive models based on algorithms such as Random Forest or Extreme Gradient Boosting among others were analyzed. The predictors used in the models include weather predictors measured in two main meteorological stations, reanalysis predictors from the National Oceanic and Atmospheric Administration, and the global predictor North Atlantic Oscillation, all of them obtained over a period of time of more than four decades. When comparing the proposed models, we evaluated accuracy, kappa and interpretability of the model obtained, as well as the relevance of the predictors used. The results show that global predictors such as the North Atlantic Oscillation Index (NAO) have a very low influence, while the local Geopotential Height (GPH) predictor is relatively more important. Machine learning prediction models are a relevant proposition for predicting medium-term precipitation in similar geographical regions.

Hybrid of Particle Swarm and Levenberg Marquardt Optimization in Neural Network Model for Rainfall Prediction

Neural network model has undergone many modifications from the original model through the development of network architecture and optimization methods. By default, the gradient-based optimization method is a method used to find network weights. But some of the weaknesses and limitations of these methods inspired many researchers to try to use other methods. Non-gradient based heuristic method is a reasonable choice considering the learning algorithm in artificial neural networks is inspired by the characteristics of creatures so that optimization methods that also mimic life patterns in nature will be appropriate. One disadvantage of the heurisic method is the length of the iteration process. In this paper, a method which combines heuristic optimization methods and gradient-based methods is applied, namely particle swarm optimization (PSO) and Levenberg Marquardt. The weight obtained from the PSO method becomes the initial weight for the Levenberg Marquardt method. The proposed procedure is applied to the rainfall data in Cokrotulung Klaten. The results showed that this procedure succeeded in providing better predictions than the Levenberg Marquardt method.

Prediction of Rainfall Using Intensified LSTM Based Recurrent Neural Network with Weighted Linear Units

Prediction of rainfall is one of the major concerns in the domain of meteorology. Several techniques have been formerly proposed to predict rainfall based on statistical analysis, machine learning and deep learning techniques. Prediction of time series data in meteorology can assist in decision-making processes carried out by organizations responsible for the prevention of disasters. This paper presents Intensified Long Short-Term Memory (Intensified LSTM) based Recurrent Neural Network (RNN) to predict rainfall. The neural network is trained and tested using a standard dataset of rainfall. The trained network will produce predicted attribute of rainfall. The parameters considered for the evaluation of the performance and the efficiency of the proposed rainfall prediction model are Root Mean Square Error (RMSE), accuracy, number of epochs, loss, and learning rate of the network. The results obtained are compared with Holt–Winters, Extreme Learning Machine (ELM), Autoregressive Integrated Moving Average (ARIMA), Recurrent Neural Network and Long Short-Term Memory models in order to exemplify the improvement in the ability to predict rainfall.

Association Rule Mining for South West Monsoon Rainfall Prediction and Estimation over Mumbai Station

Rainfall is important for agricultural yield and hence early prediction is required. It has a vital role in the improving the economy of a country. Accurate and timely weather prediction for rainfall forecasting has been one of the most challenging problems around the world as it changes the physical characteristics of the hydrologic system. Rainfall prediction model involves observation of weather data, deriving knowledge from it and implementing using computer models. The proposed work observed rainfall during south-west monsoon months of Mumbai (Latitude 19.0760°N / Longitude 72.8777°E) city. Predictive Apriori Algorithm was used to derive association rules for spot prediction, 24 hours ahead prediction and 48 hours ahead prediction, also to estimate a no rain day, moderate rain day and heavy rain day.

Rainfall Prediction using Regression Model

Artificial intelligence is been widely used in all the applications and weather forecasting is not an exception. When it comes to weather forecasting, rainfall prediction is one of the most widely used research areas as numerous lives and property damages occur due to this. Intense rainfall has abundant impacts on society and on our daily life from cultivation to disaster measures. Previous rainfall prediction models that are widely used, makes use of many the complicated blend of mathematical instruments which was insufficient to get a higher classification rate. In this project, we propose new novel methods for predicting monthly rainfall using linear regressionanalysis. Rainfall predictions are made by collecting quantitative data about the current state of the atmosphere. Numerous machine learning algorithms can learn complex mappings from inputs to outputs, based solely on samples and require limited. Accurate prediction of rainfall is a difficult task due to the dynamic nature of the atmosphere. To predict the future’s rainfall condition, the variation in the conditions in past years must be utilized. We have proposed the use of linear regressions by making use of various parameters such as temperature, humidity,and wind. The proposed model tends to forecast rainfall based on the previous records of a particular geographic area, therefore, this prediction will prove to be much reliable. The performance of the model is more accurate when compared with traditional rainfall prediction systems

Rain prediction using fuzzy rule based system in North-West malaysia

<span>The warm and humid condition is the characteristic of Malaysia tropical climate. Prediction of rain occurrences is important for the daily operations and decisions for the country that rely on agriculture needs. However predicting rainfall is a complex problem because it is effected by the dynamic nature of the tropical weather parameters of atmospheric pressure, temperature, humidity, dew point and wind speed. Those parameters have been used in this study. The rainfall prediction are compared and analyzed. Fuzzy Logic and Fuzzy Inference System can deal with ambiguity that often occurred in meteorological prediction; it can easily incorporate with expert knowledge and empirical study into standard mathematical. This paper will determine the dependability of Fuzzy Logic approach in rainfall prediction within the given approximation of rainfall rate, exploring the use of Fuzzy Logic and to develop the fuzzified model for rainfall prediction. The accuracy of the proposed Fuzzy Inference System model yields 72%</span>

Improved Rainfall Prediction Using Combined Pre-Processing Methods and Feed-Forward Neural Networks

Rainfall prediction is a fundamental process in providing inputs for climate impact studies and hydrological process assessments. Rainfall events are, however, a complicated phenomenon and continues to be a challenge in forecasting. This paper introduces novel hybrid models for monthly rainfall prediction in which we combined two pre-processing methods (Seasonal Decomposition and Discrete Wavelet Transform) and two feed-forward neural networks (Artificial Neural Network and Seasonal Artificial Neural Network). In detail, observed monthly rainfall time series at the Ca Mau hydrological station in Vietnam were decomposed by using the two pre-processing data methods applied to five sub-signals at four levels by wavelet analysis, and three sub-sets by seasonal decomposition. After that, the processed data were used to feed the feed-forward Neural Network (ANN) and Seasonal Artificial Neural Network (SANN) rainfall prediction models. For model evaluations, the anticipated models were compared with the traditional Genetic Algorithm and Simulated Annealing algorithm (GA-SA) supported by Autoregressive Moving Average (ARMA) and Autoregressive Integrated Moving Average (ARIMA). Results showed both the wavelet transform and seasonal decomposition methods combined with the SANN model could satisfactorily simulate non-stationary and non-linear time series-related problems such as rainfall prediction, but wavelet transform along with SANN provided the most accurately predicted monthly rainfall.

SENSITIVITY ANALYSIS ON DATA ARRAY AND MODEL STRUCTURE OF CONVOLUTIONAL NEURAL NETWORK FOR RAINFALL OCCURRENCE PREDICTION

We have developed a rainfall prediction model based on Convolutional Neural Network (CNN), which is one of the standardized deep-learning algorithms for image processing. In our previous study, we used input data with 3-dimensional array and fed into the processes called convolution and pooling. In this paper, the data array has been modified into a 2-dimensional array to see how the models’ performance vary. Moreover, the models were also tested with variant filter sizes and model structures. From the experimental results, we found that using the modified data array as an input was good enough to lead to similar results and it gives more effective results in some cases than the previous results with the 3-dimensional data array. For an additional experiment, data with 10 minutes resolution was tested with the same model structure but the result was not improved in predicting rainfall occurrence 1 hour ahead compared to the simulation done with the hourly data.

Monthly Rainfall Prediction Model of Peninsular Malaysia Using Clonal Selection Algorithm

Nowadays, various algorithms inspired by natural processes have been extensively applied in solving engineering problems. This study proposed Artificial Immune Systems (AIS), a computational approach inspired by the processes of human immune system, as an algorithm to predict future rainfall. This proposed algorithm is another alternative technique as compared to the commonly used Statistical, Stochastic and Artificial Neural Network techniques traditionally use in Hydrology. Rainfall prediction is pertinent in order to solve many hydrological problems. The proposed Clonal Selection Algorithm (CSA) is one of the main algorithms in AIS, which inspired on Clonal selection theory in the immune system of human body that includes selection, hyper mutation, and receptor editing processes. This study proposed algorithm is utilised to predict future monthly rainfall in Peninsular Malaysia. The collected data includes rainfall and other four (4) meteorological parameters from year 1988 to 2017 at four selected meteorological stations. The parameters used in this analysis are humidity, wind speed, temperature and pressure at monthly interval. Four (4) meteorological stations involved are Chuping (north), Subang Jaya(west), Senai (south) and Kota Bharu (west) represented peninsular Malaysia. Based on the results at testing stage, it is found that the trend and peaks of the hydrographs from generated data are approximately similar to the actual historical data. The highest similarity percentage obtained is 91%. The high values of similarity percentage obtained between simulated and actual rainfall data in this study, reinforced the hypothesis that CSA is suitable to be used for prediction of continuous time series data such as monthly rainfall data which highly variable in nature. As a conclusion, the results showed that the proposed Clonal Selection Algorithm is acceptable and stable at all stations.

Development and Assessment of Localized Seasonal Rainfall Prediction Models: Mapping and Characterizing Rift Valley Fever Hotspot Areas in the Southern and Southeastern Ethiopia

Rift Valley Fever disease has been recognized as being among permanent threats for the sustainability of livestock production in Ethiopia, owing to shared boarders with RVF endemic countries in East Africa. Above-normal and widespread rainfall have outweighed as immediate risk factor that facilitated historical outbreaks of the disease in the East Africa. The objective of the present study, thus, was to develop prospective localized seasonal rainfall anomaly prediction models, and assess their skills as early indicators to map high risk localized rift valley fever disease outbreak areas (hotspots) over the southern and southeastern part of Ethiopia. 21 years of daily rainfall data; for five meteorological stations, was employed in diagnosing existences of any anomalous patterns of rainfall, along with a cumulative rainfall analysis to determine if there were ideal conditions for potential flooding. The results indicated that rainfall in the region is highly variable; with non-significant trends, and attributed to be the results of the effects of large-scale climatic-teleconnection. The moderate to strong positive correlations found between the regional average rainfall and large scale teleconnection variables (r ≥ 0.48), indicated some potentials for early prediction of seasonal patterns of rainfall. Accordingly, models developed, based on the regional average rainfall and emerging developments of El Niño/Southern Oscillation and other regional climate forcings, showed maximum skills (ROC scores ≥ 0.7) and moderate reliability. Deterministically, most of the positive rainfall anomaly patterns, corresponding to El Niño years, were portrayed with some skills. The study demonstrated that localized climate prediction models are invaluable as early indicators to skillfully map climatically potential RVF hotspot areas.

A fuzzy inference model for rainfall prediction

A fuzzy inference model for rainfall prediction

Temporal evolution of hydroclimatic teleconnection and a time-varying model for long-lead prediction of Indian summer monsoon rainfall

Several cases of failure in the prediction of Indian Summer Monsoon Rainfall (ISMR) are the major concern for long-lead prediction. We propose that this is due to the temporal evolution of association/linkage (inherent concept of temporal networks) with various factors and climatic indices across the globe, such as El Niño-Southern Oscillation (ENSO), Equatorial Indian Ocean Oscillation (EQUINOO), Atlantic Multidecadal Oscillation (AMO), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO) etc. Static models establish time-invariant (permanent) connections between such indices (predictors) and predictand (ISMR), whereas we hypothesize that such systems are temporally varying in nature. Considering hydroclimatic teleconnection with two major climate indices, ENSO and EQUINOO, we showed that the temporal persistence of the association is as low as three years. As an application of this concept, a statistical time-varying model is developed and the prediction performance is compared against its static counterpart (time-invariant model). The proposed approach is able to capture the ISMR anomalies and successfully predicts the severe drought years too. Specifically, 64% more accurate performance (in terms of RMSE) is achievable by the recommended time-varying approach as compared to existing time-invariant concepts.

Different types of Rainfall Prediction Models and Precipitation Analysis Algorithms

Different types of Rainfall Prediction Models and Precipitation Analysis Algorithms

Decision Tree Based Rainfall Prediction Model with Data Driven Model using Multiple Linear Regression

Decision Tree Based Rainfall Prediction Model with Data Driven Model using Multiple Linear Regression

Rainfall model on area of rice production in Sragen, Karanganyar and Klaten by using Generalized Space Time Autoregressive (GSTAR)

ABSTRACTThe planning of planting period became non-optimal due to the uncertain rainfall fluctuation condition in the past few years. To anticipate food insecurity caused by such a matter, the development of rainfall prediction models in several rice production areas in Central Java, which represent the center for rice production, is required. This paper provides the application of GSTAR model on the rainfall data in Sragen Regency, Karanganyar Regency, and Klaten Regency with considering time and location conditions. The weighting applied in the model was normalized cross-correlation. By applying the least square method and stepwise procedure, GSTAR model for six, seven in Sragen, seven areas in Karanganyar, and seven areas in Klaten respectively was obtained. The validity of the model was tested by applying RMSE.

Application of Rainfall Prediction Model on Two Rice Production Centers, in West Java

<div style="left: 99.2127px; top: 907.281px; font-size: 16.9238px; font-family: serif; transform: scaleX(1.00096);" data-canvas-width="788.525">The primary objective of this study was to develop rainfall prediction model using artificial neural network analysis</div><div style="left: 85.0393px; top: 928.948px; font-size: 16.9238px; font-family: serif; transform: scaleX(1.01114);" data-canvas-width="802.7116666666665">techniques, the second objective was to apply the prediction model in rice production centers, and the third objective</div><div style="left: 85.0393px; top: 950.615px; font-size: 16.9238px; font-family: serif; transform: scaleX(0.984825);" data-canvas-width="802.6816666666666">was to compare the model predictions in rice production centers. Research is a desk study with case study in Indramayu</div><div style="left: 85.0393px; top: 972.281px; font-size: 16.9238px; font-family: serif; transform: scaleX(0.991622);" data-canvas-width="802.7016666666663">and Cianjur districts, West Java. The primary step of this study was collection of rainfall data and map information and</div><div style="left: 85.0393px; top: 993.948px; font-size: 16.9238px; font-family: serif; transform: scaleX(1.00627);" data-canvas-width="802.6666666666667">Climatology of Rainfall Stations in each district using a combination of input SST Anomaly Nino3.4 and DMI, using</div><div style="left: 85.0393px; top: 1015.61px; font-size: 16.9238px; font-family: serif; transform: scaleX(0.979254);" data-canvas-width="802.6783333333336">data from 1990 to 2010, the second step was preparation of rainfall prediction models using network analysis techniques</div><div style="left: 85.0393px; top: 1037.28px; font-size: 16.9238px; font-family: serif; transform: scaleX(0.982665);" data-canvas-width="802.7066666666665">nerve propagation, the third step was validation the model by comparing the output that has been formed with the actual</div><div style="left: 85.0393px; top: 1058.95px; font-size: 16.9238px; font-family: serif; transform: scaleX(1.0111);" data-canvas-width="802.7116666666666">rainfall data, and the fourth step was comparing rainfall prediction with the results of global climate predictions. The</div><div style="left: 85.0393px; top: 1080.61px; font-size: 16.9238px; font-family: serif; transform: scaleX(0.969247);" data-canvas-width="802.6983333333333">results showed that formulation and Validation of the model using input anomalies in the sea surface temperature Nino3.4</div><div style="left: 85.0393px; top: 1102.28px; font-size: 16.9238px; font-family: serif; transform: scaleX(0.981492);" data-canvas-width="802.7133333333333">and DMI applied in Indramayu district was able to follow the actual value of the variability of rainfall, especially during</div><div style="left: 85.0393px; top: 1123.95px; font-size: 16.9238px; font-family: serif; transform: scaleX(1.0249);" data-canvas-width="802.6883333333335">the dry season, while in Cianjur district the model was less able to describe it well. The resulting model for Cianjur</div><div style="left: 85.0393px; top: 1147.95px; font-size: 16.9238px; font-family: serif; transform: scaleX(0.9854);" data-canvas-width="570.3333333333331">district validation was low value so it is advisable not to use the model for prediction.</div>

Prediction model for peninsular Indian summer monsoon rainfall using data mining and statistical approaches

In this paper we discuss a data mining application for predicting peninsular Indian summer monsoon rainfall, and propose an algorithm that combine data mining and statistical techniques. We select likely predictors based on association rules that have the highest confidence levels. We then cluster the selected predictors to reduce their dimensions and use cluster membership values for classification. We derive the predictors from local conditions in southern India, including mean sea level pressure, wind speed, and maximum and minimum temperatures. The global condition variables include southern oscillation and Indian Ocean dipole conditions. The algorithm predicts rainfall in five categories: Flood, Excess, Normal, Deficit and Drought. We use closed itemset mining, cluster membership calculations and a multilayer perceptron function in the algorithm to predict monsoon rainfall in peninsular India. Using Indian Institute of Tropical Meteorology data, we found the prediction accuracy of our proposed approach to be exceptionally good.

A 10-min rainfall prediction model for debris flows triggered by a runoff induced mechanism

Debris flows are triggered every year in the Jiangjia Gully, Yunnan Province, China. High-intensity short duration rainfall was the main triggering factor for these gully type debris flows which are triggered by a runoff induced mechanism. The 10-min rainfall intensity is considered as the trigger. A revised prediction model with factors related to hydrology (rainfall) is introduced for this kind of gully type debris flows and applied to the Jiangjia Gully. A 10-min rainfall coefficient intensity is proposed based on the observational data of Jiangjia Gully, and a minimum 10-min rainfall intensity for triggering debris flow is presented. The Jiangjia data were used also to assess critical boundaries between three probability classes of debris flow initiation. The model was successfully validated in debris flow gullies with the same initiation mechanism in other areas of southwest China. The generic character of the model is explained by the fact that its factors are partly based on the initiation mechanisms and not only on the statistical analyses of a unique variety of local factors. The research provides a new way to predict the occurrence of debris flows initiated by a runoff induced mechanism.