Rainfall Prediction Research Articles

An objective identification technique for potential vorticity structures associated with African easterly waves

Abstract. Tropical Africa and the North Atlantic Ocean are significantly influenced by African easterly waves (AEWs), which play a fundamental role in tropical rainfall and cyclogenesis in that region. The dynamics of AEWs can be described in a potential vorticity (PV) framework. The important impact of latent heat release by cloud processes is captured in this framework by the diabatic generation of PV anomalies. This paper introduces an innovative approach for the identification and tracking of PV structures within AEWs. By employing AEW tracking and computing the wave phase of each point within the AEW domain using a Hilbert transform, we are able to effectively identify and collect 3-D PV structures associated with specific AEWs. To facilitate a climatological analysis, performed here over the months of June to October from 2002 to 2022, these structures are subsequently characterized by low-dimensional descriptors, including their location, intensity, and orientation. Our climatological analysis reveals the seasonal evolution and the structural attributes of PV anomalies within AEWs over the study domain. PV feature locations closely align with the African easterly jet's latitudinal shift during the summer season. Analysis of the mean pressure level of the 3-D PV structures shows a remarkable shift during their life cycle, indicating deep moist convection characteristics over land and more shallow convection characteristics over the ocean. On average, PV features identified within AEW troughs tilt downshear over land and equatorward over the ocean. The trough-centered analysis reveals distinct differences between satellite-estimated and model-predicted rainfall. Agreement between the results of a more traditional composite analysis and our new feature analysis provides confidence in our feature approach as a novel diagnostic tool. The feature framework provides a low-dimensional representation of the PV structure of AEWs, which facilitates future statistical analyses of the relation of this structure to, for example, tropical cyclogenesis or the predictability of tropical rainfall.

Direct and Indirect Effects of Mountain Heights on Heavy Rainfall in the Hokitika Region of New Zealand

In the Hokitika region, on the west coast of the South Island of New Zealand on 18 June 2015, very heavy stratiform precipitation (>200 mm/per day) occurred under north-westerlies with small CAPE (<25 J/kg). Analyses of model simulations and observations showed that this heavy rainfall was due to cold-front lifting enhanced by orographic lifting over the Southern Alps. At 1.5 km grid-length, the model terrain underestimated the average height of the 103 tallest mountains over the South Island by ~800 m. This led to weaker orographic lifting and mountain blocking, and a faster-moving and stronger cold front in the Hokitika region. As a result, large errors in the heavy rainfall prediction occurred. By increasing either the resolved or the sub-grid mountain heights, the simulated rainfall errors were largely reduced through stronger orographic lifting and mountain blocking, and simulation of the cold front movement and strength was improved. All the experiments have the same “flow-over” regime with mountain waves and/or wave breaking (Fm ranges 0.61–1.21). However, the rainfall amount and distribution on the windward side of the mountains varied significantly. Our new findings were that the Southern Alps can have significant indirect effects on heavy rainfall by altering the speed and strength of the cold front, in addition to the well-known direct dynamical effects (i.e., orographic lifting and mountain blocking). A combination of these direct and indirect effects makes the heavy rainfall simulation sensitive to mountain heights even under the same “flow-over” regime.

Correction to “The predictability of daily rainfall during rainy season over East Asia by a Bayesian nonhomogeneous hidden Markov model”

Correction to “The predictability of daily rainfall during rainy season over East Asia by a Bayesian nonhomogeneous hidden Markov model”

Hybrid dynamic arithmetic city council optimization for improved rainfall prediction

Hybrid dynamic arithmetic city council optimization for improved rainfall prediction

A novel coupled rainfall prediction model based on stepwise decomposition technique

The traditional decomposed ensemble prediction model decomposes the entire rainfall sequence into several sub-sequences, dividing them into training and testing periods for modeling. During sample construction, future information is erroneously mixed into the training data, making it challenging to apply in practical rainfall forecasting. This paper proposes a novel stepwise decomposed ensemble coupling model, realized through variational mode decomposition (VMD) and bidirectional long short-term memory neural network (BiLSTM) models. Model parameters are optimized using an improved particle swarm optimization (IPSO). The performance of the model was evaluated using rainfall data from the Southern Four Lakes basin. The results indicate that: (1) Compared to the PSO algorithm, the IPSO algorithm-coupled model shows a minimum decrease of 2.70% in MAE and at least 2.62% in RMSE across the four cities in the Southern Four Lakes basin; the IPSO algorithm results in a minimum decrease of 25.58% in MAE and at least 28.19% in RMSE for the VMD-BiLSTM model. (2) When compared to IPSO-BiLSTM, the VMD-IPSO-BiLSTM based on the stepwise decomposition technique exhibits a minimum decrease of 26.54% in MAE and at least 34.16% in RMSE. (3) The NSE for the testing period of the VMD-IPSO-BiLSTM model in each city surpasses 0.88, indicating higher prediction accuracy and providing new insights for optimizing rainfall forecasting.

Predictive Modeling of Extreme Weather Forecasting Events: an LSTM Approach

For a variety of industries, including agriculture, water resource management, and flood forecasting, accurate rainfall prediction is crucial. The purpose of this research work is to improve rainfall forecast system by employing the Long Short-Term Memory (LSTM) based system. The LSTM utilized in the aforementioned study made predictions by using meteorological input variables such as temperature, humidity, and rainfall. Numerous elements affect the LSTM network's performance, such as the kind and volume of data, the suitability of the model architecture, and the tuning of hyperparameters. The dataset used for model training spans from January 2015 to December 2021 and includes rainfall data collected from the Zonal Agricultural Research Station (ZARS), Shenda Park, Kolhapur. Prior to model training, the input data undergoes rigorous preprocessing. This preprocessing involves data correction, achieved through moving averages, followed by feature scaling and normalization methods. These steps are crucial to align the dataset with the unique capabilities of the LSTM model. The total dataset has a R squared (R2) value 0.23517 and a mean squared error (MSE) value 92.1839, according to the simulated findings. These metrics affirm the robust performance of the LSTM model, suggesting a high probability of accurate rainfall predictions, particularly in non-linear and complex scenarios. Decision-makers in flood predictions, agriculture, and water resource management will find the knowledge gathered from this study to be useful. They emphasize how crucial it is to use cutting-edge techniques like LSTM to increase rainfall forecast accuracy and guide strategic planning in associated industries.

Impacts of Fengyun-4A and Ground-Based Observation Data Assimilation on the Forecast of Kaifeng’s Heavy Rainfall (2022) and Mechanism Analysis of the Event

The advancement of Numerical Weather Prediction (NWP) is pivotal for enhancing high-impact weather forecasting and warning systems. However, due to the high spatial and temporal inhomogeneity, the moisture field is difficult to describe by initial conditions in NWP models, which is the essential thermodynamic variable in the simulation of various physical processes. Data Assimilation techniques are central to addressing these challenges, integrating observational data with background fields to refine initial conditions and improve forecasting accuracy. This study evaluates the effectiveness of integrating observations from the Fengyun-4A (FY-4A) Advanced Geosynchronous Radiation Imager (AGRI) and ground-based microwave radiometer (MWR) in forecasts and mechanism analysis of a heavy rainfall event in the Kaifeng region of central China. Our findings reveal that jointly assimilating AGRI radiance and MWR data significantly enhances the model’s humidity profile accuracy across all atmospheric layers, resulting in improved heavy rainfall predictions. Analysis of the moisture sources indicates that the storm’s water vapor predominantly originates from westward air movement ahead of a high-altitude trough, with sustained channeling towards the rainfall zone, ensuring a continuous supply of moisture. The storm’s development is further facilitated by a series of atmospheric processes, including the interplay of high and low-level vorticity and divergence, vertical updrafts, the formation of a low-level jet, and the generation of unstable atmospheric energy. Additionally, this study examines the influence of Tai-hang Mountain’s terrain on precipitation patterns in the Kaifeng area. Our experiments, comparing a control setup (CTL) with varied terrain heights, demonstrate that reducing terrain height by 50–60% significantly decreases precipitation coverage and intensity. In contrast, increasing terrain height enhances precipitation, although this effect plateaus when the elevation increase exceeds 100%, closely mirroring the precipitation changes observed with a 75% terrain height increment.

Performance Comparison of Various Machine Learning Models for Rainfall Prediction Using Wind Information

Forecasting rainfall, crucial for agriculture, water management, and disaster preparedness, presents significant challenges due to intricate relationships often missed by conventional statistical methods. Hence, machine learning (ML) models offer promising alternatives, enhancing the precision and dependability of rainfall predictions. This paper presents a comprehensive comparison of various ML models with diverse model structures and regularization strategies for rainfall prediction in urban metropolitan cities. The results show that the random forest model, and gradient boosting model outperform the other models such as logistic regression, support vector machine (SVM), decision tree, K-nearest neighbor (KNN), Naive Bayes, linear SVM, and neural network in terms of accuracy. Validation accuracies of 75%, 77%, 68%, 78%, 76%, 78%, 74%, 75% and 76% were achieved for logistic regression, SVM, decision tree, random forest model, KNN, gradient boosting model, Naive Bayes, linear SVM, and neural network, respectively. The choice of ML models for rainfall prediction should consider the characteristics of the data, e.g. a lag feature for 20 days was employed that uses previous time steps to predict the next time step. The paper concludes that ML models, especially the random forest and gradient boosting models are powerful and robust tools for rainfall prediction in urban metropolitan cities. Key Words: rainfall prediction, wind information, machine learning, gradient boosting, random forest

Rainfall Prediction Using Deep Learning Algorithms

Water scarcity presents a significant challenge for many countries. Given that rainfall serves as the primary source of drinking water, irrigation, and various other purposes, the accuracy of rainfall prediction holds utmost importance for decision-makers. Climate change has exacerbated the unpredictability of rainfall patterns, posing challenges to sustainable water resource management. Artificial Intelligence (AI) techniques, especially deep neural networks, have demonstrated effectiveness in predicting rainfall patterns and enhancing water resource management. This study involved reprocessing data obtained from the Jordanian Ministry of Water and Irrigation Jordan Valley Authority to create a dataset. The dataset was then evaluated using Long Short-Term Memory (LSTM), a type of Recurrent Neural Network (RNN), and Support Vector Machine (SVM) models for rainfall prediction. Evaluation metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared were employed, revealing the reliability of the forecasting models.

Analysis and Prediction of Rainfall with Oceanic Nino Index and Climate Variables Using Correlation Coefficient and Deep Learning

This article presents the relationship between the Oceanic Nino Index (ONI) and monthly rainfall on the southern and eastern coast of Thailand, specifically in Narathiwat, Pattani, and Yala provinces, where influences have been commonly observed. This research aims to study the relationship between the Oceanic Nino Index (ONI) and monthly rainfall to develop a model for predicting monthly rainfall. Despite previous related research, there has been no in-depth study on the relationship between the Oceanic Nino Index (ONI) and monthly rainfall in areas adjacent to the sea. The correlation coefficient was used to determine the relationship, revealing that the ONI value is significantly correlated with the amount of rainfall in the current month and the following month. This correlation paved the way for developing a model to predict monthly rainfall. Multiple linear regression, recurrent neural networks, and long short-term memory models were employed for this purpose. The study found that utilizing a recurrent neural network yielded the best prediction efficiency, with Mean Absolute Error (MAE) values of 112.76 mm for Narathiwat province, 81.06 mm for Pattani province, and 97.67 mm for Yala province. Doi: 10.28991/CEJ-2024-010-05-01 Full Text: PDF

A Hybrid Feature Selection Gradient Recurrent Neural Network (HFSGRNN) Model for Rainfall Prediction in India Regions

A Hybrid Feature Selection Gradient Recurrent Neural Network (HFSGRNN) Model for Rainfall Prediction in India Regions

Smart Automated Liquid Sprinkler Robot Integrating IoT and Deep Learning for Precision Crop Management

Abstract: The Smart Automated Liquid Sprinkler (SALS) is a groundbreaking solution aimed at revolutionizing traditional watering systems through the integration of IoT technology. Its development addresses pressing challenges such as water scarcity and labor-intensive maintenance in outdoor environments. SALS combines advanced sensors to monitor humidity and weather conditions in real-time, ensuring precise watering tailored to the specific needs of plants while minimizing manual intervention. Furthermore, it incorporates obstacle detection and avoidance features, enhancing its efficiency and durability by mitigating the risk of accidents. By integrating deep learning models for rainfall prediction, SALS optimizes water usage preemptively, adjusting watering schedules to environmental conditions. Offering independent operation and a modular design, SALS provides flexibility to users, catering to gardens of all sizes and promoting greener and healthier environments. The longterm savings from reduced water consumption and labor costs make SALS a wise investment for homeowners, businesses, and municipalities, heralding a new era of efficiency and sustainability in outdoor maintenance

RAINFALL PREDICTION USING MACHINE LEARNING

- India is an agricultural country and its economy is largely based upon crop productivity and rainfall. For analyzing the crop productivity, rainfall prediction is require and necessary to all farmers. Rainfall Prediction is the application of science and technology to predict the state of the atmosphere. It is important to exactly determine the rainfall for effective use of water resources, crop productivity and pre planning of water structures. Using different data mining techniques it can predict rainfall. Data mining techniques are used to estimate the rainfall numerically. This paper focuses some of the popular data mining algorithms for rainfall prediction. Random Forest, K-Nearest Neighbor algorithm, Logistic regression, Decision Tree are some of the algorithms have been used. From that comparison, it can analyze which method gives better accuracy for rainfall prediction. Key Words: Rain fall, Agricultural, Economy , Farmers, Water resources.

Rainfall forecasting in arid regions in response to climate change using ARIMA and remote sensing

Climate changes in increasing at alarming rates which caused outward changes in the hydrological cycle parameters such as temperature and precipitation. The spatial and temporal distribution of rainfall has changed all over the world. Rainfall forecasting is considered an important tool that could help decisionmakers for managing water resources. Syria is facing several drought events that may affect its water resources. Due to the lack of available rainfall data from land stations in Syria after the civil war started in 2010 till now, one of the aims of this study is to depend on rainfall data from satellite images after correcting these data using the available land stations data. This study also aims to assess rainfall trends in Syria due to climate change and forecasting rainfall. The rainfall data were collected from 71 land stations from 1991 to 2009. Satellite images from Climate Data Record (CDR) were downloaded from 1983 to 2020 and ArcMap was used to extract rainfall data at locations of land stations. The extracted rainfall data from satellite images were corrected by land stations data from 1991 to 2009. Then Statistical Package for the Social Sciences (SPSS) program with Auto-Regressive Integrated Moving Average (ARIMA) models are used to fit and forecasting rainfall in Syria. The rainfall trends at the studied stations were analyzed for 40 years (1983–2022) and a forecast of one year is conducted. The results showed decreasing trends at all the stations that lead to a decrease the quantity of water received every year which could affect the water resources in Syria and agriculture and food security will be also affected. The results are consistent with Intergovernmental Panel on Climate Change (IPCC) reports that concluded a decrease in rainfall at southern and eastern Mediterranean regions. The results of the current study could help in the management of water resources in Syria and forecasting using ARIMA is recommended for rainfall predictions.

An Evaluation of Bidirectional Long Short-Term Memory Model for Estimating Monthly Rainfall in India

Objectives: Predicting the amount of rainfall is difficult due to its complexity and non-linearity. The objective of this study is to predict the average rainfall one month ahead using the all-India monthly average rainfall dataset from 1871 to 2016. Methods: This study proposed a Bidirectional Long Short-Term Memory (LSTM) model to predict the average monthly rainfall in India. The parameters of the models are determined using the grid search method. This study utilized the average monthly rainfall as an input, and the dataset consists of 1752 months of rainfall data prepared from thirty (30) meteorological sub-divisions in India. The model was compiled using the Mean Square Error (MSE) loss function and Adam optimizer. The models' performances were evaluated using statistical metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Findings: This study discovered that the proposed Bidirectional LSTM model achieved an RMSE of 240.79 and outperformed an existing Recurrent Neural Network (RNN), Vanilla LSTM and Stacked LSTM by 8%, 4% and 2% respectively. The study also finds that increasing the input time step and increasing the number of cells in the hidden layer enhanced the prediction performance of the proposed model, and the Bidirectional LSTM converges at a lower epoch compared to RNN and LSTM models. Novelty: This study applied the Bidirectional LSTM for the first time in predicting all-India monthly average rainfall and provides a new benchmark for this dataset. Keywords: Deep Learning, LSTM, Rainfall prediction, Stacked LSTM, Bidirectional LSTM

Rainfall prediction around Sathanur dam by Naive Bayes classifier, logistic regression models and various classification and regression machine learning techniques

Agriculture is the major backbone of Indian economy. India depends mainly on natural rainfall for agriculture. Hence, predicting rainfall becomes very important which could help farmers to take right decision related to agricultural activities. However, at the same time, it is a challenging problem for the meteorological department to accurately predict the rainfall. The present work uses machine learning approach to investigate the prediction of rainfall around Sathanur Dam in Tamil Nadu, India. The rainfall prediction around the Sathanur Dam is carried out using the dam data for the year 2012. The current work considers the four important features namely, Water Level, In-Flow, Out-flow and storage of the dam and their effects in predicting the rainfall. This is accomplished by applying various machine learning classifiers including CART, LDA, CART, KNN, SVM, Naive Bayes, Logistic Regression and Random Forest method. Out of all the classifiers, the CART model gives the highest prediction accuracy which is 86.11%.

AN IN-DEPTH ANALYSIS OF ARTIFICIAL INTELLIGENCE APPROACHES FOR RAINFALL PREDICTION

Natural disasters and floods brought on by heavy rainfall pose serious threats to human health and lives every year on a global scale. The intricacy of meteorological data makes it difficult to provide accurate rainfall predictions, despite their critical importance in nations like India where agriculture is the primary occupation. Rainfall forecasting has recently benefited from Artificial Intelligence (AI) developments such as Deep Learning (DL) and Machine Learning (ML) techniques. This article provides a comprehensive survey of recent studies that use AI techniques for rainfall prediction, analyzing them based on the ML algorithms and DL methods used, organized by publication year. The findings show that DL approaches are more effective than traditional ML methods and shallow neural network models. This research is important as it has significant impacts on agriculture, disaster preparedness, and water resource management. Finally, it outlines future research directions for further advancements in rainfall prediction through AI methodologies.

Predicting Rainfall Pattern in Kakamega County using Time Series

Rainfall patterns play a critical role in shaping various aspects of our lives. Understanding the patterns, trends and predictability of rainfall is essential for effective planning and decision making in various aspects including agriculture, water resource management, disaster preparedness and social economic planning. In agricultural activities crops require specific amount of water at the right time for growth. By understanding the rainfall patterns, farmers can adapt their farming activities, optimizing irrigation strategies and make informed decisions. In the management, it help policy makers and management authorities for planning efficient water allocations and conservations measures. Therefore, in this paper we fit a time series model that best describes rainfall patterns of Kakamega county for the general ARIMA and generated the values of (P, D, Q) to forecast average expected monthly rainfall. Also we use R software for verification and data fitting of the model. The data we have used is from the Kakamega meteorological station in Kakamega.

Research on a Rainfall Prediction Model in Guizhou Based on Raindrop Spectra

Research on a Rainfall Prediction Model in Guizhou Based on Raindrop Spectra

Spatio-attention-based network to improve heavy rainfall prediction over the complex terrain of Assam

Spatio-attention-based network to improve heavy rainfall prediction over the complex terrain of Assam