Long Short-term Memory Training Research Articles

A national-scale hybrid model for enhanced streamflow estimation – consolidating a physically based hydrological model with long short-term memory (LSTM) networks

Abstract. Accurate streamflow estimation is essential for effective water resource management and adapting to extreme events in the face of changing climate conditions. Hydrological models have been the conventional approach for streamflow interpolation and extrapolation in time and space for the past few decades. However, their large-scale applications have encountered challenges, including issues related to efficiency, complex parameterization, and constrained performance. Deep learning methods, such as long short-term memory (LSTM) networks, have emerged as a promising and efficient approach for large-scale streamflow estimation. In this study, we have conducted a series of experiments to identify optimal hybrid modeling schemes to consolidate physically based models with LSTM aimed at enhancing streamflow estimation in Denmark. The results show that the hybrid modeling schemes outperformed the Danish National Water Resources Model (DKM) in both gauged and ungauged basins. While the standalone LSTM rainfall–runoff model outperformed DKM in many basins, it faced challenges when predicting the streamflow in groundwater-dependent catchments. A serial hybrid modeling scheme (LSTM-q), which used DKM outputs and climate forcings as dynamic inputs for LSTM training, demonstrated higher performance. LSTM-q improved the mean Nash–Sutcliffe efficiency (NSE) by 0.22 in gauged basins and 0.12 in ungauged basins compared to DKM. Similar accuracy improvements were achieved with alternative hybrid schemes, i.e., by predicting the residuals between DKM-simulated streamflow and observations using LSTM. Moreover, the developed hybrid models enhanced the accuracy of extreme events, which encourages the integration of hybrid models within an operational forecasting framework. This study highlights the advantages of synergizing existing physically based hydrological models (PBMs) with LSTM models, and the proposed hybrid schemes hold the potential to achieve high-quality large-scale streamflow estimations.

Intelligent Dust Monitoring System Based on IoT

Dust is a frequent contributor to health risks and changes in the climate, one of the most dangerous issues facing people today. Desertification, drought, agricultural practices, and sand and dust storms from neighboring regions bring on this issue. Deep learning (DL) long short-term memory (LSTM) based regression was a proposed solution to increase the forecasting accuracy of dust and monitoring. The proposed system has two parts to detect and monitor the dust; at the first step, the LSTM and dense layers are used to build a system using to detect the dust, while at the second step, the proposed Wireless Sensor Networks (WSN) and Internet of Things (IoT) model is used as a forecasting and monitoring model. The experiment DL system train and test part was applied to dust phenomena historical data. Its data has been collected through the Iraqi Meteorological Organization and Seismology (IMOS) raw dataset with 170237 of 17023 rows and 10 columns. The LSTM model achieved small time, computationally complexity of, and layers number while being effective and accurate for dust prediction. The simulation results reveal that the model's mean square error test reaches 0.12877 and Mean Absolute Error (MAE) test is 0.07411 at the same rates of learning and exact features values of vector in the dense layer, representing a neural network layer deeply is connected to the LSTM training proposed model. Finally, the model suggested enhances monitoring performance.

Stock Market Prediction

The prediction of stock market trends is a challenging yet critical task in the financial sector, given its significant implications for investors, traders, and financial institutions. This research leverages the Long Short-Term Memory (LSTM) algorithm, a type of recurrent neural network (RNN), to develop a robust model for forecasting stock prices. The study utilizes historical stock market data sourced from Yahoo Finance, accessed via the yfinance package in Python. The primary objectives are to preprocess the data, implement the LSTM model, and evaluate its performance against traditional models such as Random Forest and Linear Regression. Data preprocessing involved handling missing values, normalizing the dataset, and transforming it into sequences suitable for LSTM training. The model's architecture includes multiple LSTM layers designed to capture temporal dependencies in the data. The study evaluates the model's performance using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and prediction accuracy. Comparative analysis shows that the LSTM model outperforms both Random Forest and Linear Regression models, with lower MSE and RMSE values and higher accuracy in predicting stock prices. This research discovered that LSTM's ability to retain long-term dependencies makes it particularly effective for stock market prediction, where historical trends and patterns significantly influence future prices. The results indicate that the LSTM model provides more reliable and precise predictions, which can enhance decision-making in trading and investment. This research highlights the potential of advanced neural network architectures in financial forecasting, offering a valuable tool for investors aiming to optimize their strategies and mitigate risks. The significance of this study lies in its practical application in the financial industry, demonstrating that machine learning models, particularly LSTM, can substantially improve the accuracy of stock market predictions. Future research could explore the integration of additional features, such as macroeconomic indicators and sentiment analysis, to further enhance model performance. This study underscores the importance of continuous innovation and the adoption of sophisticated algorithms to navigate the complexities of financial markets.

Optimizing energy consumption in smart homes using GA-LSTM

The need to optimize energy consumption arises from the inadequate energy supply many homes face. However, to optimize energy consumption in a home, one must be equipped with the knowledge of the energy consumption rate and energy supply rate in the home. This paper proposed the use of a Long Short-Term Memory (LSTM) model optimized by Genetic Algorithm (GA) to optimize the energy consumption in a smart home. The model was designed using 8 input variables, which were observed weather information of a given region over a span of 350 days. The data set was split into a training data set and a test data set in the ratio 4:1. Both the baseline LSTM model and our proposed GA-LSTM model was used to train and test the data. The results of the baseline LSTM model showed a Mean Average Percentage Error (MAPE) of 0.15 and a Root Mean Square Error (RMSE) of 0.0007, while our proposed GA-LSTM recorded an MAPE of 0.07 and RMSE of 0.0004. The implication of these results is a better performance of our proposed GA-LSTM model over the baseline LSTM model. This paper demonstrated how the Genetic Algorithm was able to optimize the baseline LSTM model by selecting the best weights during the LSTM training process. The findings of this paper are recommended for smart homes having the capability to monitor energy usage of installed electrical appliances and the ability to predict to weather condition of the region the home is sited.

Enhancing long short-term memory (LSTM)-based streamflow prediction with a spatially distributed approach

Abstract. Deep learning (DL) algorithms have previously demonstrated their effectiveness in streamflow prediction. However, in hydrological time series modelling, the performance of existing DL methods is often bound by limited spatial information, as these data-driven models are typically trained with lumped (spatially aggregated) input data. In this study, we propose a hybrid approach, namely the Spatially Recursive (SR) model, that integrates a lumped long short-term memory (LSTM) network seamlessly with a physics-based hydrological routing simulation for enhanced streamflow prediction. The lumped LSTM was trained on the basin-averaged meteorological and hydrological variables derived from 141 gauged basins located in the Great Lakes region of North America. The SR model involves applying the trained LSTM at the subbasin scale for local streamflow predictions which are then translated to the basin outlet by the hydrological routing model. We evaluated the efficacy of the SR model with respect to predicting streamflow at 224 gauged stations across the Great Lakes region and compared its performance to that of the standalone lumped LSTM model. The results indicate that the SR model achieved performance levels on par with the lumped LSTM in basins used for training the LSTM. Additionally, the SR model was able to predict streamflow more accurately on large basins (e.g., drainage area greater than 2000 km2), underscoring the substantial information loss associated with basin-wise feature aggregation. Furthermore, the SR model outperformed the lumped LSTM when applied to basins that were not part of the LSTM training (i.e., pseudo-ungauged basins). The implication of this study is that the lumped LSTM predictions, especially in large basins and ungauged basins, can be reliably improved by considering spatial heterogeneity at finer resolution via the SR model.

An investigation of multivariate data-driven deep learning models for predicting COVID-19 variants

The Coronavirus Disease 2019 (COVID-19) pandemic has swept almost all parts of the world. With the increasing number of COVID-19 cases worldwide, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has mutated into various variants. Given the increasingly dangerous conditions of the pandemic, it is crucial to predict the number of COVID-19 cases. Deep Learning and Long Short-Term Memory (LSTM) have predicted disease progress with reasonable accuracy and small errors. LSTM training is used to predict confirmed cases of COVID-19 based on variants identified using the European Centre for Disease Prevention and Control (ECDC) COVID-19 dataset containing confirmed cases identified from 30 European countries. Tests were conducted using the LSTM and Bidirectional LSTM (BiLSTM) models with the addition of Recurrent Neural Network (RNN) as comparisons on hidden size and layer size. The obtained result showed that in testing hidden sizes 25, 50, 75, and 100, the RNN model provided better results, with the minimum Mean Squared Error (MSE) value of 0.01 and the Root Mean Square Error (RMSE) value of 0.012 for B.1.427/B.1.429 variant with a hidden size of 100. Further testing layer sizes 2, 3, 4, and 5 shows that the BiLSTM model provided better results, with a minimum MSE value of 0.01 and an RMSE of 0.01 for the B.1.427/B.1.429 variant with a hidden size of 100 and layer size of 2.

A novel prediction model for ship fuel consumption considering shipping data privacy: An XGBoost-IGWO-LSTM-based personalized federated learning approach

Due to the protection of shipping data privacy, shipping companies rarely share shipping raw data. Therefore, how to protect shipping data privacy is a crucial issue when predicting ship fuel consumption (SFC). To tackle such issues and guarantee prediction accuracy, a tailored personalized federated learning (PFL) approach is designed and proposed in this study. In the proposed PFL approach, an improved long short-term memory (LSTM) neural network is adopted as the Deep Learning (DL) model for each ship client, in which the improved Grey Wolf Optimization (IGWO) algorithm is juxtaposed to optimize the hyperparameter tuning process in LSTM neural network. In addition, considering that different ships have unique navigation environments and distinct ship characteristics, XGBoost is introduced to make feature selections before LSTM training. Given the personalized features of different ships after XGBoost selection, an FL with personalized layers (Fedper) is employed, where the personalized layers are trained on the client side respectively and the basic layers are uploaded to the central server. To validate the efficiency of the proposed approach, we collect datasets and conduct experiments on 18 bulk carriers. To further prove the superiority of XGBoost-IGWO-LSTM-based PFL, four kinds of comparative experiments are conducted. The results show that our proposed approach can make more accurate SFC prediction results while guaranteeing shipping data privacy. The research could provide meaningful suggestions for shipping companies that prioritize corporate data privacy to reduce SFC.

Sentiment Analysis on the Quality of Public Services with User Satisfaction Prediction of YuhSinau Application Managed by BKPSDM Kabupaten Kebumen Using LSTM Method

The quality of public services is critical in providing effective and responsive governance in an increasingly digital society. The development of the YuhSinau application by the Personnel and Human Resource Development Agency (BKPSDM) of Kebumen Regency offers an innovative response to the growing need for e-learning solutions for local government civil servants (Pegawai Negeri Sipil or PNS). However, determining the app’s effectiveness and user satisfaction is critical. This demands a thorough sentiment analysis in order to acquire insights into users’ thoughts and opinions about the quality of public services supplied by YuhSinau. The Long Short-Term Memory (LSTM) approach is used in this article to examine feelings and forecast customer pleasure. Data collection from multiple sources, initial data preprocessing, LSTM model construction, training, validation, and prediction are all part of the process. The results show that the model has some drawbacks, most notably its failure to appropriately explain variation in target data due to a negative R-squared value. Enhancements to the LSTM architecture, hyperparameter adjustment, and the use of more diverse and representative training data are proposed to improve the model. Continuous review and responsiveness to user comments are critical for improving the quality of public services via the YuhSinau application.
 Keywords: sentiment analysis, public service quality, user satisfaction, YuhSinau application,LSTM method

High-Frequency Surface Wave Radar Current Measurement Corrections via Machine Learning and Towed Acoustic Doppler Current Profiler Integration

High-Frequency Surface Wave Radar Current Measurement Corrections via Machine Learning and Towed Acoustic Doppler Current Profiler Integration

Data-augmented landslide displacement prediction using generative adversarial network

Landslides are destructive natural disasters that cause catastrophic damage and loss of life worldwide. Accurately predicting landslide displacement enables effective early warning and risk management. However, the limited availability of on-site measurement data has been a substantial obstacle in developing data-driven models, such as state-of-the-art machine learning (ML) models. To address these challenges, this study proposes a data augmentation framework that uses generative adversarial networks (GANs), a recent advance in generative artificial intelligence (AI), to improve the accuracy of landslide displacement prediction. The framework provides effective data augmentation to enhance limited datasets. A recurrent GAN model, RGAN-LS, is proposed, specifically designed to generate realistic synthetic multivariate time series that mimics the characteristics of real landslide on-site measurement data. A customized moment-matching loss is incorporated in addition to the adversarial loss in GAN during the training of RGAN-LS to capture the temporal dynamics and correlations in real time series data. Then, the synthetic data generated by RGAN-LS is used to enhance the training of long short-term memory (LSTM) networks and particle swarm optimization-support vector machine (PSO-SVM) models for landslide displacement prediction tasks. Results on two landslides in the Three Gorges Reservoir (TGR) region show a significant improvement in LSTM model prediction performance when trained on augmented data. For instance, in the case of the Baishuihe landslide, the average root mean square error (RMSE) increases by 16.11%, and the mean absolute error (MAE) by 17.59%. More importantly, the model's responsiveness during mutational stages is enhanced for early warning purposes. However, the results have shown that the static PSO-SVM model only sees marginal gains compared to recurrent models such as LSTM. Further analysis indicates that an optimal synthetic-to-real data ratio (50% on the illustration cases) maximizes the improvements. This also demonstrates the robustness and effectiveness of supplementing training data for dynamic models to obtain better results. By using the powerful generative AI approach, RGAN-LS can generate high-fidelity synthetic landslide data. This is critical for improving the performance of advanced ML models in predicting landslide displacement, particularly when there are limited training data. Additionally, this approach has the potential to expand the use of generative AI in geohazard risk management and other research areas.

Deep Learning-Based Bitcoin Price Prediction Using Long Short-Term Memory Networks

Cryptocurrencies, notably Bitcoin, have surged in popularity and importance, attracting widespread attention due to their volatile nature and potential for significant financial gains. Predicting Bitcoin prices accurately has emerged as a challenging yet indispensable task for investors, traders, and policymakers seeking to navigate the complexities of the cryptocurrency market. Traditional forecasting methods often struggle to capture the intricate temporal patterns inherent in this domain, prompting the exploration of advanced techniques such as deep learning. This paper presents a comprehensive study on employing Long Short-Term Memory (LSTM) networks for Bitcoin price prediction, a variant of Recurrent Neural Networks (RNNs). Our research encompasses the entire process, from data collection to model evaluation, focusing on addressing the unique challenges posed by cryptocurrency market data. We begin by discussing the methodology involved in data acquisition, preprocessing, and feature engineering, which are crucial steps for ensuring the quality and relevance of input data. Next, we delve into the intricacies of LSTM model selection, hyperparameter tuning, and training strategies, aiming to optimize predictive performance while mitigating overfitting. Through extensive experimentation on real-world Bitcoin price datasets spanning multiple periods, we rigorously evaluate the effectiveness of LSTM networks in capturing long-term dependencies and forecasting future price movements. Comparative analyses against traditional time-series forecasting methods underscore the superior predictive capabilities of deep learning approaches in this context. Furthermore, we investigate the interpretability of LSTM-based models, examining the insights gained from analyzing learned representations and hidden states. These analyses provide valuable perspectives for market participants seeking to understand the driving factors behind Bitcoin price dynamics. Our findings demonstrate the practical utility of LSTM networks in Bitcoin price prediction and contribute to advancing the broader discourse on deep learning applications in financial forecasting. By shedding light on the potential of deep learning techniques to enhance predictive accuracy and robustness in cryptocurrency markets, our research offers valuable insights for stakeholders navigating this rapidly evolving landscape.

Reconstructing 42 Years (1979–2020) of Great Lakes Surface Temperature through a Deep Learning Approach

Accurate estimates for the lake surface temperature (LST) of the Great Lakes are critical to understanding the regional climate. Dedicated lake models of various complexity have been used to simulate LST but they suffer from noticeable biases and can be computationally expensive. Additionally, the available historical LST datasets are limited by either short temporal coverage (<30 years) or lower spatial resolution (0.25° × 0.25°). Therefore, in this study, we employed a deep learning model based on Long Short-Term Memory (LSTM) neural networks to produce a daily LST dataset for the Great Lakes that spans an unparalleled 42 years (1979–2020) at a spatial resolution of ~1 km. In our dataset, the Great Lakes are represented by ~33,000 unstructured grid points and the LSTM training incorporated the information from each grid point. The LSTM was trained with seven meteorological variables from reanalysis data as feature variables and the LST from a historical satellite-derived dataset as the target variable. The LSTM was able to capture the spatial heterogeneity of LST in the Great Lakes well and exhibited high correlation (≥0.92) and low bias (limited to ±1.5 °C) for the temporal evolution of LST during the training (1995–2020) and testing (1979–1994) periods.

LSTM 신경망을 이용한 이동 로봇 장착상태의 IMU 오차 보정

In this paper, we proposed a calibration method for an IMU already attached to a mobile robot by using an long short-term memory (LSTM) neural network. The LSTM network takes IMU and lidar measurements as inputs and outputs compensated IMU measurements. The proposed method offers the advantage of compensating for deterministic errors of the IMU, including settling misalignment errors, while maintaining the IMU attached to the mobile robot. In the LSTM training process, true values of the IMU measurements are required. To obtain these values, we used an external camera to capture a marker on the mobile robot. By numerically differentiating the position and attitude calculated from the captured image, we acquired the true acceleration and angular velocity. We verified the performance of the proposed IMU error compensation method through experiments by using a mobile robot in an indoor environment.

Magnetic-Field-Based Indoor Positioning Using Temporal Convolutional Networks.

Traditional magnetic-field positioning methods collect magnetic-field information from each spatial point to construct a magnetic-field fingerprint database. During the positioning phase, real-time magnetic-field measurements are matched to a magnetic-field map to predict the user's location. However, this approach requires a significant amount of time to traverse the entire magnetic-field fingerprint database and does not effectively leverage the magnetic-field sequence's unique patterns to improve the accuracy and robustness of the positioning system. In recent years, the application of deep learning for the indoor positioning of magnetic fields has grown rapidly, especially by using the magnetic-field sequence as a time series and a trained long short-term memory (LSTM) model to predict the position, directly avoiding the time-consuming matching process. However, the training of LSTM is time-consuming, and the degradation problem occurs as the stack of layers increases. This article proposes a temporal convolutional network (TCN)-based magnetic-field positioning system that extracts magnetic-field sequence features by preprocessing them with coordinate transformation, smoothing filtering, and first-order differencing. The proposed method is seamlessly applicable to heterogeneous smartphones. The trained TCN models are compared with the LSTM and gated recurrent unit (GRU) models, showing the high accuracy and robustness of the proposed algorithm.

Prediction of instantaneous yield of bio-oil in fluidized biomass pyrolysis using long short-term memory network based on computational fluid dynamics data

Computational fluid dynamics (CFD) is an effective tool to investigate biomass fast pyrolysis in fluidized bed reactor for bio-oil production, while it requires huge computational time when optimizing operating conditions or simulating large/industrial units. Machine learning (ML) is a promising approach to achieving both accuracy and efficiency. In this work, a reduced-order model including long short-term memory (LSTM) layer, pooling layer, and fully connected layer was established to predict future mass flow rates by training the historical CFD data. Unsteady mass flow rates, which are normally used to determine product yields, were treated as data of time series in ML. Influencing factors, such as sequence length, number of neurons, learning rate, subsequences order (shuffle or not), number of LSTM layers, and ratio of testing set, were evaluated to obtain their optimal values. The developed LSTM model framework and training process showed good applicability for the dataset of different species and temperatures. Product yields predicted by the derived LSTM were in good agreement with those obtained by CFD, while nearly 30% computational effort was saved. Thus, it is clearly seen that the well-predicted fluctuating characteristics and final product yields are helpful to improve accuracy of process simulation for digitalizing key reactors and building smart factories.

Intelligent Financial Auditing Model Based on Deep Learning.

The entire auditing process is complicated and tedious and requires a lot of human resources. Therefore, the intelligent development of auditing is the general trend. In order to improve the audit quality, this paper establishes an intelligent financial audit model that can predict the audit opinion of the consolidated financial statements. This paper proposes an audit opinion prediction model based on the fusion of deep belief neural network (DBN) and long-short term memory (LSTM). First, an indicator system is established for audit opinions, and multiple financial parameters are used to describe possible audit opinions. On this basis, a DBN network is designed to complete deep feature extraction and used for LSTM training. According to the prediction model obtained by training, the subsequent audit opinion can be scientifically predicted. In the experiment, the method in this paper is tested based on financial audit related data sets and compared with the prediction results of traditional multilayer perceptron (MLP), convolutional neural network (CNN), and LSTM models. The results verify the validity and reliability of the model in this paper.

The Prediction of Enterprise Stock Change Trend by Deep Neural Network Model.

This study aims to accurately predict the changing trend of stocks in stock trading so that company investors can obtain higher returns. In building a financial forecasting model, historical data and learned parameters are used to predict future stock prices. Firstly, the relevant theories of stock forecasting are discussed, and problems in stock forecasting are raised. Secondly, the inadequacies of deep neural network (DNN) models are discussed. A prediction trend model of enterprise stock is established based on long short-term memory (LSTM). The uniqueness and innovation lie in using the stock returns of Bank of China securities in 2022 as the training data set. LSTM prediction models are used to perform error analysis on company data training. The 20-day change trend of the company's stock returns under different models is predicted and analyzed. The results show that as the number of iterations increases, the loss rate of the LSTM training curve keeps decreasing until 0. The average return price of the LSTM prediction model is 14.01. This figure is closest to the average real return price of 13.89. Through the forecast trend analysis under different models, LSTM predicts that the stock change trend of the enterprise model is closest to the changing trend of the actual earnings price. The prediction accuracy is better than other prediction models. In addition, this study explores the characteristics of high noise and complexity of corporate stock time series, designs a DNN prediction model, and verifies the feasibility of the LSTM model to predict corporate stock changes with high accuracy.

Optimal PID control for hovering stabilization of quadcopter using long short term memory

Drones are a type of unmanned aerial vehicle. They use several rotors to control their flight motion and stabilize their attitude. This study aims to determine the optimal proportional-integral–differential (PID) gain values that can stabilize a quadcopter with four rotors quickly when its attitude is disturbed. Generally, expert knowledge and a great deal of time and money are required to obtain the PID gain values to stabilize the attitude of a drone. However, in this study, long short-term memory (LSTM), which is a type of neural network algorithm, was used to evaluate the flight motions based on PID gain values without expert knowledge of quadcopters (quadcopter flight motion, PID control algorithms, and quadcopter expert experience). To obtain the optimal values of the PID gain for stabilizing the attitude of a drone, a PID simulator algorithm was developed in this study. The developed algorithm used dynamic equations of motion for the drones. Simulations were used to acquire the drone stabilization data, and a back-propagation neural network was applied to establish an approximate model. Subsequently, the non-dominated sorting genetic algorithm-II was used to obtain the optimal PID gain values that could restore the attitude of the drone quickly. The drone motion data obtained using the simulations were used as the LSTM training data, and the optimal PID gain values obtained using the genetic algorithm were used by the LSTM to predict the motion. To verify the results, a drone was constructed, and the LSTM and dynamic simulation values were compared with the drone experimental values using an experimental device that allowed the motion of the drone to be examined. The results indicated that the motions resulting from the optimal values and the experimental results were in agreement.

Risk Analysis of Textile Industry Foreign Investment Based on Deep Learning.

With the decline of China's economic growth rate and the uproar of antiglobalization, the textile industry, one of the business cards of China's globalization, is facing a huge impact. When the economic model is undergoing transformation, it is more important to prevent enterprises from falling into financial distress. So, the financial risk early warning is one of the important means to prevent enterprises from falling into financial distress. Aiming at the risk analysis of the textile industry's foreign investment, this paper proposes an analysis method based on deep learning. This method combines residual network (ResNet) and long short-term memory (LSTM) risk prediction model. This method first establishes a risk indicator system for the textile industry and then uses ResNet to complete deep feature extraction, which are further used for LSTM training and testing. The performance of the proposed method is tested based on part of the measured data, and the results show the effectiveness of the proposed method.

Forecasting Monthly Rainfall using Bio-Inspired Artificial Algae Deep Learning Network

Rainfall forecasting is the most critical and challenging task because of its dependence on different climatic and weather parameters. Hence, robust and accurate rainfall forecasting models need to be created by applying various machine learning and deep learning approaches. Several automatic systems were created to predict the weather, but it depends on the type of weather pattern, season and location, which leads in maximizing the processing time. Therefore, in this work, significant artificial algae long short-term memory (LSTM) deep learning network is introduced to forecast the monthly rainfall. During this process, Homogeneous Indian Monthly Rainfall Data Set (1871–2016) is utilized to collect the rainfall information. The gathered information is computed with the help of an LSTM approach, which is able to process the time series data and predict the dependency between the data effectively. The most challenging phase of LSTM training process is finding optimal network parameters such as weight and bias. For obtaining the optimal parameters, one of the Meta heuristic bio-inspired algorithms called Artificial Algae Algorithm (AAA) is used. The forecasted rainfall for the testing dataset is compared with the existing models. The forecasted results exhibit superiority of our model over the state-of-the-art models for forecasting Indian Monsoon rainfall. The LSTM model combined with AAA predicts the monsoon from June–September accurately.