Water Level Forecasts Research Articles

Using ARIMA and ETS models for forecasting water level changes for sustainable environmental management

It is vital to provide useful hydrological forecasts for urban and agricultural water management, hydropower generation, flood protection and management, drought mitigation and alleviation, and river basin planning and management, among other things. This paper introduces a simple and flexible hydrological time series forecasting framework. Predicting water levels is crucial, given the need for sustainable environmental management. The prognosis should be reasonable to persuade individuals to take proper precautions. While many methods have been developed to predict water levels, here the effectiveness of two approaches to predicting river water levels was assessed. For this purpose, nine years of data were used, which were divided into input data (2014–2021) and validation data (2022), on water levels in the Morava e Binçës river for the Vitia station, in the form of monthly time series records, to identify the best model among those used and to identify the information necessary for water resource management and hazard control. Models Autoregressive Integrated Moving Average(ARIMA) and Error Trend and Seasonality, or Exponential Smoothing (ETS), were examined using the R package to determine the most accurate. The results indicate the applicability of both models, as evidenced by the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE). The predictive analysis based on historical water levels allowed the identification of distinct periods characterized by high and low water levels between 2022 and 2024, which is important for the area in question due to the numerous flood events occurring here.

VTNet: A multi-domain information fusion model for long-term multi-variate time series forecasting with application in irrigation water level

Time series forecasting is intricately tied to production and life, garnering widespread attention over an extended period. Enhancing the performance of long-term multivariate time series forecasting (MTSF) poses a highly challenging task, as it requires mining complicated and obscure temporal patterns in many aspects. For this reason, this paper proposes a long-term forecasting model based on multi-domain fusion (VTNet) to adaptively capture and refine multi-scale intra- and inter-variate dependencies. In contrast to previous techniques, we devise a dual-stream learning architecture. Firstly, the fast Fourier Transform (FFT) is adopted to extract frequency domain information. The original sequences are then transformed into 2D visual features in the temporal-frequency domain, and a 2D-TBlock is designed for multi-scale dynamic learning. Secondly, a combination of convolution and recurrent networks continues to explore the local temporal features and preserve the global trend. Finally, multi-modal circulant fusion is applied to achieve a more comprehensive and enriched feature fusion representation, further promoting overall performance. Extensive experiments are conducted on 9 public benchmark datasets and the real-world irrigation water level to showcase VTNet’s promoted performance and generalization. Moreover, VTNet yields 46.93% and 25.36% relative improvements for water level forecasting, revealing its potential application value in water-saving planning and extreme event early warning.

Assessment and Forecasting of Water Level of Hatia Dam of Ranchi, Jharkhand

Assessment and Forecasting of Water Level of Hatia Dam of Ranchi, Jharkhand

Multi-step-ahead prediction of water levels using machine learning: A comparative analysis in the Vietnamese Mekong Delta

This study evaluates the efficacy of five machine learning algorithms Support Vector Regression (SVR), Decision Tree (DT), Random Forest (RF), Light Gradient Boosting Machine Regressor (LGBM), and Linear Regression (LR) in predicting water levels in the Vietnamese Mekong Delta's tidal river system, a complex nonlinear hydrological phenomenon. Using daily maximum, minimum, and mean water level data from the Cao Lanh gauging station on the Tien River (2000-2020), models were developed to forecast water levels one, three, five, and seven days in advance. Performance was assessed using Nash-Sutcliffe Efficiency, coefficient of determination, Root Mean Square Error, and Mean Absolute Error. Results indicate that all models performed well, with SVR consistently outperforming others, followed by RF, DT, and LGBM. The study demonstrates the viability of machine learning in water level prediction using solely historical water level data, potentially enhancing flood warning systems, water resource management, and agricultural planning. These findings contribute to the growing knowledge of machine learning applications in hydrology and can inform sustainable water resource management strategies in delta regions.

Water Level Prediction and Forecasting Using a Long Short-Term Memory Model for Nam Ngum River Basin in Lao PDR

The process of implementing neural networks in a computer system is known as deep learning. In this study, a deep learning model, namely long short-term memory (LSTM), was established to predict and forecast water levels for stations located at the Nam Ngum River Basin in Lao PDR. Water levels are predicted and forecasted based on the rainfall and water level data observed at previous time steps. It is proposed that the optimal sequence length for modeling should be determined based on the threshold of the correlation coefficient obtained from the water level and rainfall time series. The trained LSTM models in this study can be considered fair and adequate for water level prediction, as NSE values from 0.5 to 0.7 were mostly obtained from the model validations in the testing periods. The results showed that the autocorrelation and cross-correlation analysis did help in determining the optimal sequence length in an LSTM model. The performance levels of the LSTM model in forecasting future water levels in the Nam Ngum River Basin varied; the forecasted water level hydrographs for the Pakkayoung station generally corresponded with the observed ones, while the forecasted water level hydrographs for the other stations deviated significantly from the observed hydrographs.

Краткосрочное и среднесрочное прогнозирование уровней воды на реках бассейна Тобола

In order to provide the necessary hydrological information for operational decisions on the use of water resources and protecting the population from dangerous floods for the rivers of the Tobol river basin, a methodology for daily forecasting of average daily water levels with a lead time of 1 to 10 days has been developed. The hydrograph extrapolation method was used, which takes into account water levels for the date of the forecast and for the 5 previous days. The methodology was developed for 64 sections located on the Tobol River and its tributaries of various orders. To estimate the parameters of the scheme for obtaining forecasts and their verification, hydrological observation data for the period from 1985 to 2022 were used. For most rivers in the basin, the methodology provides satisfactory results. In general, the accuracy of forecasts and their lead time increase with increasing catchment area. The methodology can be used within an automated system for preparing and issuing forecasts.

Multivariate Hydrological Modeling Based on Long Short-Term Memory Networks for Water Level Forecasting

In the Department of Chocó, flooding poses a recurrent and significant challenge due to heavy rainfall and the dense network of rivers characterizing the region. However, the lack of adequate infrastructure to prevent and predict floods exacerbates this situation. The absence of early warning systems, the scarcity of meteorological and hydrological monitoring stations, and deficiencies in urban planning contribute to the vulnerability of communities to these phenomena. It is imperative to invest in flood prediction and prevention infrastructure, including advanced monitoring systems, the development of hydrological prediction models, and the construction of hydraulic infrastructure, to reduce risk and protect vulnerable communities in Chocó. Additionally, raising public awareness of the associated risks and encouraging the adoption of mitigation and preparedness measures throughout the population are essential. This study introduces a novel approach for the multivariate prediction of hydrological variables, specifically focusing on water level forecasts for two hydrological stations along the Atrato River in Colombia. The model, utilizing a specialized type of recurrent neural network (RNN) called the long short-term memory (LSTM) network, integrates data from hydrological variables, such as the flow, precipitation, and level. With a model architecture featuring four inputs and two outputs, where flow and precipitation serve as inputs and the level serves as the output for each station, the LSTM model is adept at capturing the complex dynamics and cross-correlations among these variables. Validation involves comparing the LSTM model’s performance with linear and nonlinear Autoregressive with Exogenous Input (NARX) models, considering factors such as the estimation error and computational time. Furthermore, this study explores different scenarios for water level prediction, aiming to utilize the proposed approach as an effective flood early warning system.

Method of Data Preprocessing on the Basis of Pulse Neural Network to Improve the Accuracy of Water Level Forecast on the Example of Ufa City of the Republic of Bashkortostan

The developed method of data preprocessing based on impulse neural network is considered. The essence of this method is to improve the quality of the initial dataset by minimising data noise. The peculiarity is to improve the accuracy of the predicted values of the time series of water levels obtained at the output of the pulse neural network. Retrospective data were obtained from hydrological posts (river gauge) and automatic stations with the help of FSUE «Centre of Register and Cadastre» from 01.01.1997 to 30.06.2023. On the example of hydrological post 76289 (Ufa) the experiment on forecasting of water levels with the help of the developed system «Flood 2.0» was carried out. The experiment proves the efficiency of the data preprocessing method developed in this study to improve the accuracy of water level forecasts.

Water-Level Prediction Analysis for the Three Gorges Reservoir Area Based on a Hybrid Model of LSTM and Its Variants

The Three Gorges Hydropower Station, the largest in the world, plays a pivotal role in hydroelectric power generation, flood control, navigation, and ecological conservation. The water level of the Three Gorges Reservoir has a direct impact on these aspects. Accurate prediction of the reservoir’s water level, especially in the dam area, is of utmost importance for downstream regions’ safety and economic development. This study investigates the application and performance of four distinct deep-learning models in predicting water levels. The models evaluated include the Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM), and Convolutional Neural Network–Attention–Long Short-Term Memory (CNN–Attention–LSTM). The performance of these models was assessed using several metrics, namely the Coefficient of Determination (R2), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The findings indicate that the CNN–Attention–LSTM model outperforms the others in all metrics, achieving an R2 value of 0.9940, MAE of 0.5296, RMSE of 0.6748, and MAPE of 0.0032. Moreover, the CNN–LSTM model exhibited exceptional predictive accuracy for lower water levels. These results underscore the potential of deep-learning models in water-level forecasting, particularly highlighting the efficacy of attention mechanisms in enhancing predictive accuracy. Precise water-level predictions are instrumental in optimizing hydropower generation and providing a scientific basis for effective flood control and water resource management.

A new approach for hydrograph data interpolation and outlier removal for vector autoregressive modelling: a case study from the Odra/Oder River

This study presents a new approach for predicting water levels of the Odra/Oder river using vector autoregressive models (VAR). We use water level time series from 27 gauging stations, on which we interpolate no-data gaps using the LinAR method and detect outliers with two separate methods: the extreme values (EV) approach and the isolation forest (IFO) algorithm. Before removing potential outliers, we propose a hydrological evaluation based on multivariate data analysis. Finally, we consider three separate data scenarios, i.e. LinAR (no outlier rejection), EV, and IFO. VAR models for six prediction gauges were built in a moving window manner on the most recent 720 hourly water levels prior to each prediction. The analysis covered the time range from January 2016 to May 2022 and resulted in ≈\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varvec{\\approx }$$\\end{document} 1,000,000 water level forecasts (3 scenarios x 6 gauges x 55,000 hourly time steps) with lead time of 72 h. The analysis of root mean squared error (RMSE) indicates that the VAR model performs well, especially for 24-hour predictions, with RMSE values ranging from 8 to 28 cm. The model was also found to have skills in predicting a rising limb of a hydrograph. Our numerical experiments showed the susceptibility of the VAR predictions to artefacts. The IFO method was found to detect outliers skilfully, which allowed to produce the most accurate VAR-based predictions.

Краткосрочное и среднесрочное прогнозирование стока рек бассейна Дона

For the rivers of the Don basin, the Hydrometcentre of Russia has developed a methodology for daily short- and medium-range forecasting of streamflow and water levels. Average daily discharges and water levels are predicted throughout the year with a lead time of 1 to 10 days based on the hydrograph extrapolation method, which takes into account their values for the date of the forecast and for the previous 5 days. To estimate the parameters of the forecasting scheme, hydrological observation data for each river section are used. The technique gives satisfactory results and can be used within the framework of an automated system for preparing and issuing forecasts in order to provide necessary forecast information for making operational decisions on the use of water resources of the Don basin rivers and protecting the population from dangerous floods.

Spatio-temporal multivariable time vario-zoom network for water level forecasting based on high-resolution hydrological dataset

Water level forecasting is a crucial element in river-basin water resource management, particularly for shipping and extreme events, such as floods and droughts. However, accurately forecasting water levels is challenging due to the influence of non-stationarity hydrological processes, complex characteristic variables, and noisy data. Data-driven approaches in water level forecasting generally lack annotated high-resolution data. To address these problems, this paper presents the first hydrological dataset on the Xijiang River basin-Xijiang Water Dataset (XJWD) and a Spatio-Temporal Multivariable-based Time Vario-Zoom Network (STM- TVZN) model. XJWD combines multi-source raw hydrological data and has real-time and higher resolution data than comparable datasets. STM-TVZN is specifically tailored to enhance the discernment of salient features within key time windows for each variable by fusing spatio-temporal multivariate features through vario-zoom time windows. Finally, a novel metric, Extremum Time Offsets (ETO), is proposed to evaluate the accuracy of the occurrence of local extremum in time series, and it is significant for predicting and evaluating extremum events such as floods and droughts. Experimental results show that the proposed method outperformed existing models on XJWD dataset regarding error rate, stationarity, similarity, and ETO. Overall, the proposed method of combining time-variable focusing network design and spatio-temporal multivariate feature extraction is appealing for short-term water level forecasting in inland rivers, exhibiting potential capacity in strategic water-saving planning and extremum event forecasting caused by climate change.

Superior performance of hybrid model in ungauged basins for real-time hourly water level forecasting – A case study on the Lancang-Mekong mainstream

In the context of climate change and human influence, timely and reliable information about water level variations is crucial for downstream flood control, navigation, and water resource management, particularly for the Lancang-Mekong River, the largest transboundary river in Southeast Asia. However, accurate real-time water level forecasts remain challenging, especially in data-limited regions. This study focused on the Chiang Saen Station, the uppermost Mekong River hydrological station, comparing three methods to predict 48-hour water levels: the Variable Infiltration Capacity (VIC) and Hydrology-Hydraulic (HH) physics-based models, the Gated Recurrent Unit (GRU) model, and hybrid models (VIC-GRU and HH-GRU). Evaluations were conducted at the 1-h, 3-h, 6-h, 12-h, 24-h, and 48-h forecast lead times. Assessments at various lead times showed that hybrid models incorporating physics-based mechanisms significantly enhanced predictions. VIC-GRU demonstrated superior performance, with KGE values surpassing 0.94, NSE values exceeding 0.95, MAE below 0.17 m, and PBIAS within ± 1 % across all lead times. Compared to GRU, PBIAS decreased by 84 %, while MAE dropped by 76 % to 81 % relative to VIC and HH, respectively. Notably, seasonal variations affected hybrid model performance, especially in the dry season. Optimal parameter selections enhanced model accuracy by 48 % to 49 %. These results underscore the potential of combining physics-based and deep learning models for more accurate, high-temporal-resolution real-time water level forecasts in data-scarce regions. A comprehensive study of streamflow characteristics enhances the advantages of this integration. This research contributes valuable insights to water level prediction in data-scarce regions and informs flood risk reduction and management in the Lancang-Mekong mainstream.

Analysing the performance of the NARX model for forecasting the water level in the Chikugo River estuary, Japan

Estuaries are dynamic environments which are driven by various natural processes like river discharge, tides, waves, influx of saline water and sediments, etc. These ecosystems are the most sensitive to sea level rise and fluctuations in river discharge associated with climate change. A direct response of sea level rise and river discharge can be observed in the water level of estuaries. However, existing models have not considered these parameters for forecasting water level. This paper focuses on developing a water level forecast model for the Chikugo River estuary in Japan using Nonlinear Autoregressive with Exogenous inputs (NARX Model). NARX neural network was used to do the one-step-ahead prediction of water level considering the various parameters that can very well be influenced by climate change: previous water level, river discharge, and salinity. Accordingly, three models were developed: (i) Model I considering previous water level; (ii) Model II additionally considering river discharge; and (iii) Model III additionally considering salinity. All the models showed appreciable performance in forecasting the water level. Model III had the best correlation with the water level with a cross-correlation value of 0.6030, while the river discharge had only a cross-correlation of 0.1113 indicating that the Chikugo River estuary is tide-dominated. The model was trained using different combinations of available data - previous water level, river discharge, and salinity. Cross-correlation results showed a better correlation between water level and salinity than various other combinations trained. Therefore, tidal intrusion influences the water level in the estuary, thereby depicting that sea level rise can affect the water level, and its influence can be well predicted by the developed model. The water level significantly affects the flora and fauna and the predictability of future estuarine floods can help in taking necessary mitigation strategies.

Hybrid machine learning model based on feature decomposition and entropy optimization for higher accuracy flood forecasting

The advancement of machine learning model has widely been adopted to provide flood forecast. However, the model must deal with the challenges to determine the most important features to be used in in flood forecast with high-dimensional non-linear time series when involving data from various stations. Decomposition of time-series data such as empirical mode decomposition, ensemble empirical mode decomposition and discrete wavelet transform are widely used for optimization of input; however, they have been done for single dimension time-series data which are unable to determine relationships between data in high dimensional time series. In this study, hybrid machine learning models are developed based on this feature decomposition to forecast the monthly water level using monthly rainfall data. Rainfall data from eight stations in Kelantan River Basin are used in the hybrid model. To effectively select the best rainfall data from the multi-stations that provide higher accuracy, these rainfall data are analyzed with entropy called Mutual Information that measure the uncertainty of random variables from various stations. Mutual Information act as optimization method helps the researcher to select the appropriate features to score higher accuracy of the model. The experimental evaluations proved that the hybrid machine learning model based on the feature decomposition and ranked by Mutual Information can increase the accuracy of water level forecasting. This outcome will help the authorities in managing the risk of flood and helping people in the evacuation process as an early warning can be assigned and disseminate to the citizen.

Deep learning for Multi-horizon Water levelForecasting in KRS reservoir, India

In recent times, the densely populated Bengaluru metropolis in India has faced challenges related to water scarcity, particularly relying on the Krishna Raja Sagara (KRS) dam. The forecasting of reservoir water levels has become challenging due to spatio-temporal fluctuations in meteorological conditions and complex physical processes. As a result, developing suitable water management to meet the population's water demand requires an accurate and dependable estimate of the dam's water level. This work attempted to use a daily reservoir and weather data by utilizing long short-term memory (LSTM) networks. Seven high-performance models (viz., M1 to M7) with varying window sizes and horizons have been trained on this data and their performance is compared. The performance metrics revealed that the M7 model outperformed the other models for reservoir water level prediction, with coefficient of determination (R2) score of 0.93, root mean square error (RMSE) of 2.94, and mean absolute percentage error (MAPE) of 0.01. This study also provides a valuable dashboard for tracking forecasted water levels in the largest reservoir in the Cauvery basin. Finally, the innovative integration of LSTM technology in water level predictions for the Krishna Raja Sagara (KRS) dam not only addresses the challenges posed by spatio-temporal fluctuations but also sets a new standard for precision in forecasting, thereby establishing a crucial decision-support tool for real-time monitoring and enhanced water resource management in India's Bengaluru metropolis.

Methodology for target forecasting of water level in hydroelectric plant reservoirs under conditions of low inflow

Methodology for target forecasting of water level in hydroelectric plant reservoirs under conditions of low inflow

A new aspect of the ApEn application to improve the PSO-ANFIS model to forecast Caspian Sea levels

Short-term forecasting of sea levels at the Caspian Sea plays an important issue for coastal planning. Therefore, a new aspect of the Approximate Entropy (ApEn) method application is studied to improve the Combined Particle Swarm Optimization and the Adaptive Network-based Fuzzy Inference System (PSO-ANFIS), Auto-Regressive Integrated Moving Average (ARIMA) models and Back Propagation Neural Networks (BP-NN). To evaluate the idea, seawater levels from 1961 to 2021 are categorized as training, validation (from 1961 to 2007 at Anzali station), and testing data (from 2008 to 2021 at Amirabad station). Results show the PSO-ANFIS model performance for forecasting the seawater level changes for four randomly selected time intervals and three critical periods. In addition, it is concluded that there are no considerable changes in the PSO-ANFIS model accuracy for embedding dimensions greater than 13 in forecasting of sea water level in the Caspian Sea. At the same time, the employed ApEn shows that there are no significant changes in approximate entropy values for block numbers more than 13 for the Caspian Sea levels. This shows the ApEn method performance to determine embedding dimensions for the forecaster models of time series. In addition, it resulted that the Caspian Sea water levels are on a decreasing trend on average by 5.495 cm every year until 2024 and the PSO-ANFIS model is reliable for 36 months as a short-term forecaster model. Comparing the developed PSO-ANFIS model with Long Short-Term Memory (LSTM) networks shows the PSO-ANFIS model significant superiority to the LSTM.

Multimodal deep learning water level forecasting model for multiscale drought alert in Feiyun River basin

Hydrological forecasting is an indispensable tool in intelligent water conservation for flood control and drought mitigation. Due to the influences of human activities and climate variability, accurate water level prediction poses a significant challenge. To address this, we develop a novel hybrid deep architecture, that is Dual-Stage Attention-based Multi-modal Deep Learning (DSAMDL), for reliable and interpretable multi-scale water level forecasting. Unlike previous techniques, multisource data is treated as different modalities in the proposed model. Firstly, we employ the one-dimensional Convolution to capture local trend features, followed by the Bidirectional Long Short-term Memory network to learn long-term dependencies. Subsequently, we design a dual-stage attention mechanism, which assigns contributions in a phased manner to different temporal and spatial. Finally, an adaptive fusion method is applied to enhance overall performance. To validate its accuracy and efficiency, short long-term drought water level forecasting and predictions under various events are implemented over six reservoir stations in the Feiyun River basin, Wenzhou City. Four evaluation metrics, namely RMSE, MAE, CORR, and NSE, are introduced for comprehensive assessment against the start-of-the-art baselines. The results well-document that the DSAMDL framework achieves satisfactory accuracy, with an average improvement of 22.4%, 27.8%, 29.7%, and 11.5% in these four metrics, showcasing the model’s effectiveness in handling complex multiscale drought water level forecasting.

Relevance of Machine Learning Techniques in Water Infrastructure Integrity and Quality: A Review Powered by Natural Language Processing

Water infrastructure integrity, quality, and distribution are fundamental for public health, environmental sustainability, economic development, and climate change resilience. Ensuring the robustness and quality of water infrastructure is pivotal for sectors like agriculture, industry, and energy production. Machine learning (ML) offers potential for bolstering water infrastructure integrity and quality by analyzing extensive data from sensors and other sources, optimizing treatment protocols, minimizing water losses, and improving distribution methods. This study delves into ML applications in water infrastructure integrity and quality by analyzing English-language articles from 2015 onward, compiling a total of 1087 articles. Initially, a natural language processing approach centered on topic modeling was adopted to classify salient topics. From each identified topic, key terms were extracted and utilized in a semi-automatic selection process, pinpointing the most relevant articles for further scrutiny, while unsupervised ML algorithms can assist in extracting themes from the documents, generating meaningful topics often requires intricate hyperparameter adjustments. Leveraging the Bidirectional Encoder Representations from Transformers (BERTopic) enhanced the study’s contextual comprehension in topic modeling. This semi-automatic methodology for bibliographic exploration begins with a broad topic categorization, advancing to an exhaustive analysis of each topic. The insights drawn underscore ML’s instrumental role in enhancing water infrastructure’s integrity and quality, suggesting promising future research directions. Specifically, the study has identified four key areas where ML has been applied to water management: (1) advancements in the detection of water contaminants and soil erosion; (2) forecasting of water levels; (3) advanced techniques for leak detection in water networks; and (4) evaluation of water quality and potability. These findings underscore the transformative impact of ML on water infrastructure and suggest promising paths for continued investigation.