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Artificial Intelligence and Machine Learning: Application in Predictive Hydrological Models

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A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".

Deadline for manuscript submissions: closed (15 November 2020) | Viewed by 48499

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Dr. Mohammad Zare

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Research Scientist, Research and Education Department (RED), RSS-Hydro, Innovation Hub Dudelange, 100, route de Volmerange, L-3593 Dudelange, Luxembourg
Interests: artificial intelligence; evolutionary algorithms; flood frequency analysis; fuzzy logic; hydrological modeling; signal processing; statistical hydrology; time series analysis

Dr. Guy J.-P. Schumann

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1. School of Geographical Sciences, University of Bristol, Bristol BS8 1TH, UK
2. Research and Education Department (RED), RSS-Hydro, Dudelange, 100, route de Volmerange, L-3593 Dudelange, Luxembourg
Interests: remote sensing; flood frequency analysis; flood hazard and risk modeling; hydrological modeling; statistics; climate change
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are in the era of climate change phenomena that may possibly intensify the hydrological cycle. Additionally, increasing urban development causes growing water demands as well as higher exposure to water-related risks, such as floods and droughts. Therefore, fundamental changes in the surface water and groundwater regimes are seen in many regions around the world. Responding appropriately to the rapidly growing water demands and increasing risks necessitates the use of innovative and novel techniques for better water resource management. The main priority is to develop suitable methods and models in order to establish, simulate, and predict the optimal management and use of available water resources.

In recent years, appropriate models such as artificial intelligence (AI) and machine learning (ML) algorithms have been widely used in many research areas and applications but less applied in the fields of hydrology. In this regard, Atmosphere will publish a Special Issue on “Artificial Intelligence and Machine Learning; Application in Predictive Hydrological Models”. This Special Issue seeks high-quality contributions on practical applications of AI and ML methods and models in prediction, forecast, and projection of hydrological events. You are cordially invited to submit your research papers to this upcoming Special Issue. All elements relevant to predictive studies in hydrology and groundwater modelling using, but not limited to, one or more of the below listed methods are within the scope of this Special Issue: neural networks, support vector machines (SVM), fuzzy systems, ANFIS, evolutionary computation, Bayesian network, Markov model, Kalman Filter, and chaos theory.

Authors of articles that either combine these models and algorithms together or combine AI and ML with other data-driven/physical models (e.g., development of AI algorithms to complement or integrated with hydrological conceptual or process-based modeling) are also encouraged to submit.

Dr. Mohammad Zare
Dr. Guy Schumann
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Artificial intelligence
Data-driven approaches
Deep learning
Intelligent forecasting
Machine learning
Neural networks
Neurofuzzy systems
Signal/Image processing

Published Papers (11 papers)

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Research

13 pages, 10278 KiB

Open AccessEditor’s ChoiceArticle

Generating Flood Hazard Maps Based on an Innovative Spatial Interpolation Methodology for Precipitation

by Mohammad Zare, Guy J.-P. Schumann, Felix Norman Teferle and Ruja Mansorian

Atmosphere 2021, 12(10), 1336; https://doi.org/10.3390/atmos12101336 - 13 Oct 2021

Cited by 3 | Viewed by 1866

Abstract

In this study, a new approach for rainfall spatial interpolation in the Luxembourgian case study is introduced. The method used here is based on a Fuzzy C-Means (FCM) clustering method. In a typical FCM procedure, there are a lot of available data and each data point belongs to a cluster, with a membership degree [0 1]. On the other hand, in our methodology, the center of clusters is determined first and then random data are generated around cluster centers. Therefore, this approach is called inverse FCM (i-FCM). In order to calibrate and validate the new spatial interpolation method, seven rain gauges in Luxembourg, Germany and France (three for calibration and four for validation) with more than 10 years of measured data were used and consequently, the rainfall for ungauged locations was estimated. The results show that the i-FCM method can be applied with acceptable accuracy in validation rain gauges with values for R² and RMSE of (0.94–0.98) and (9–14 mm), respectively, on a monthly time scale and (0.86–0.89) and (1.67–2 mm) on a daily time scale. In the following, the maximum daily rainfall return periods (10, 25, 50 and 100 years) were calculated using a two-parameter Weibull distribution. Finally, the LISFLOOD FP flood model was used to generate flood hazard maps in Dudelange, Luxembourg with the aim to demonstrate a practical application of the estimated local rainfall return periods in an urban area. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning: Application in Predictive Hydrological Models)

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16 pages, 12750 KiB

Open AccessArticle

Influence of Random Forest Hyperparameterization on Short-Term Runoff Forecasting in an Andean Mountain Catchment

by Pablo Contreras, Johanna Orellana-Alvear, Paul Muñoz, Jörg Bendix and Rolando Célleri

Atmosphere 2021, 12(2), 238; https://doi.org/10.3390/atmos12020238 - 10 Feb 2021

Cited by 25 | Viewed by 3410

Abstract

The Random Forest (RF) algorithm, a decision-tree-based technique, has become a promising approach for applications addressing runoff forecasting in remote areas. This machine learning approach can overcome the limitations of scarce spatio-temporal data and physical parameters needed for process-based hydrological models. However, the influence of RF hyperparameters is still uncertain and needs to be explored. Therefore, the aim of this study is to analyze the sensitivity of RF runoff forecasting models of varying lead time to the hyperparameters of the algorithm. For this, models were trained by using (a) default and (b) extensive hyperparameter combinations through a grid-search approach that allow reaching the optimal set. Model performances were assessed based on the R², %Bias, and RMSE metrics. We found that: (i) The most influencing hyperparameter is the number of trees in the forest, however the combination of the depth of the tree and the number of features hyperparameters produced the highest variability-instability on the models. (ii) Hyperparameter optimization significantly improved model performance for higher lead times (12- and 24-h). For instance, the performance of the 12-h forecasting model under default RF hyperparameters improved to R² = 0.41 after optimization (gain of 0.17). However, for short lead times (4-h) there was no significant model improvement (0.69 < R² < 0.70). (iii) There is a range of values for each hyperparameter in which the performance of the model is not significantly affected but remains close to the optimal. Thus, a compromise between hyperparameter interactions (i.e., their values) can produce similar high model performances. Model improvements after optimization can be explained from a hydrological point of view, the generalization ability for lead times larger than the concentration time of the catchment tend to rely more on hyperparameterization than in what they can learn from the input data. This insight can help in the development of operational early warning systems. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning: Application in Predictive Hydrological Models)

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17 pages, 4208 KiB

Open AccessArticle

The Development of a Hybrid Wavelet-ARIMA-LSTM Model for Precipitation Amounts and Drought Analysis

by Xianghua Wu, Jieqin Zhou, Huaying Yu, Duanyang Liu, Kang Xie, Yiqi Chen, Jingbiao Hu, Haiyan Sun and Fengjuan Xing

Atmosphere 2021, 12(1), 74; https://doi.org/10.3390/atmos12010074 - 06 Jan 2021

Cited by 47 | Viewed by 4800

Abstract

Investigation of quantitative predictions of precipitation amounts and forecasts of drought events are conducive to facilitating early drought warnings. However, there has been limited research into or modern statistical analyses of precipitation and drought over Northeast China, one of the most important grain production regions. Therefore, a case study at three meteorological sites which represent three different climate types was explored, and we used time series analysis of monthly precipitation and the grey theory methods for annual precipitation during 1967–2017. Wavelet transformation (WT), autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) methods were utilized to depict the time series, and a new hybrid model wavelet-ARIMA-LSTM (W-AL) of monthly precipitation time series was developed. In addition, GM (1, 1) and DGM (1, 1) of the China Z-Index (CZI) based on annual precipitation were introduced to forecast drought events, because grey system theory specializes in a small sample and results in poor information. The results revealed that (1) W-AL exhibited higher prediction accuracy in monthly precipitation forecasting than ARIMA and LSTM; (2) CZI values calculated through annual precipitation suggested that more slight drought events occurred in Changchun while moderate drought occurred more frequently in Linjiang and Qian Gorlos; (3) GM (1, 1) performed better than DGM (1, 1) in drought event forecasting. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning: Application in Predictive Hydrological Models)

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15 pages, 4815 KiB

Open AccessArticle

Application of a Novel Hybrid Wavelet-ANFIS/Fuzzy C-Means Clustering Model to Predict Groundwater Fluctuations

by Mohammad Mahdi Jafari, Hassan Ojaghlou, Mohammad Zare and Guy Jean-Pierre Schumann

Atmosphere 2021, 12(1), 9; https://doi.org/10.3390/atmos12010009 - 23 Dec 2020

Cited by 23 | Viewed by 2595

Abstract

In order to optimize the management of groundwater resources, accurate estimates of groundwater level (GWL) fluctuations are required. In recent years, the use of artificial intelligence methods based on data mining theory has increasingly attracted attention. The goal of this research is to evaluate and compare the performance of adaptive network-based fuzzy inference system (ANFIS) and Wavelet-ANFIS models based on FCM for simulation/prediction of monthly GWL in the Maragheh plain in northwestern Iran. A 22-year dataset (1996–2018) including hydrological parameters such as monthly precipitation (P) and GWL from 25 observation wells was used as models input data. To improve the prediction accuracy of hybrid Wavelet-ANFIS model, different mother wavelets and different numbers of clusters and decomposition levels were investigated. The new hybrid model with Sym4-mother wavelet, two clusters and a decomposition level equal to 3 showed the best performance. The maximum values of R2 in the training and testing phases were 0.997 and 0.994, respectively, and the best RMSE values were 0.05 and 0.08 m, respectively. By comparing the results of the ANFIS and hybrid Wavelet-ANFIS models, it can be deduced that a hybrid model is an acceptable method in modeling of GWL because it employs both the wavelet transform and FCM clustering technique. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning: Application in Predictive Hydrological Models)

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21 pages, 8049 KiB

Open AccessEditor’s ChoiceArticle

Smart Climate Hydropower Tool: A Machine-Learning Seasonal Forecasting Climate Service to Support Cost–Benefit Analysis of Reservoir Management

by Arthur H. Essenfelder, Francesca Larosa, Paolo Mazzoli, Stefano Bagli, Davide Broccoli, Valerio Luzzi, Jaroslav Mysiak, Paola Mercogliano and Francesco dalla Valle

Atmosphere 2020, 11(12), 1305; https://doi.org/10.3390/atmos11121305 - 01 Dec 2020

Cited by 12 | Viewed by 3572

Abstract

This study proposes a climate service named Smart Climate Hydropower Tool (SCHT) and designed as a hybrid forecast system for supporting decision-making in a context of hydropower production. SCHT is technically designed to make use of information from state-of-art seasonal forecasts provided by the Copernicus Climate Data Store (CDS) combined with a range of different machine learning algorithms to perform the seasonal forecast of the accumulated inflow discharges to the reservoir of hydropower plants. The machine learning algorithms considered include support vector regression, Gaussian processes, long short-term memory, non-linear autoregressive neural networks with exogenous inputs, and a deep-learning neural networks model. Each machine learning model is trained over past decades datasets of recorded data, and forecast performances are validated and evaluated using separate test sets with reference to the historical average of discharge values and simpler multiparametric regressions. Final results are presented to the users through a user-friendly web interface developed from a tied connection with end-users in an effective co-design process. Methods are tested for forecasting the accumulated seasonal river discharges up to six months in advance for two catchments in Colombia, South America. Results indicate that the machine learning algorithms that make use of a complex and/or recurrent architecture can better simulate the temporal dynamic behaviour of the accumulated river discharge inflow to both case study reservoirs, thus rendering SCHT a useful tool in providing information for water resource managers in better planning the allocation of water resources for different users and for hydropower plant managers when negotiating power purchase contracts in competitive energy markets. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning: Application in Predictive Hydrological Models)

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17 pages, 5307 KiB

Open AccessEditor’s ChoiceArticle

Prediction of Short-Time Cloud Motion Using a Deep-Learning Model

by Xinyue Su, Tiejian Li, Chenge An and Guangqian Wang

Atmosphere 2020, 11(11), 1151; https://doi.org/10.3390/atmos11111151 - 26 Oct 2020

Cited by 10 | Viewed by 5420

Abstract

A cloud image can provide significant information, such as precipitation and solar irradiation. Predicting short-time cloud motion from images is the primary means of making intra-hour irradiation forecasts for solar-energy production and is also important for precipitation forecasts. However, it is very challenging to predict cloud motion (especially nonlinear motion) accurately. Traditional methods of cloud-motion prediction are based on block matching and the linear extrapolation of cloud features; they largely ignore nonstationary processes, such as inversion and deformation, and the boundary conditions of the prediction region. In this paper, the prediction of cloud motion is regarded as a spatiotemporal sequence-forecasting problem, for which an end-to-end deep-learning model is established; both the input and output are spatiotemporal sequences. The model is based on gated recurrent unit (GRU)- recurrent convolutional network (RCN), a variant of the gated recurrent unit (GRU), which has convolutional structures to deal with spatiotemporal features. We further introduce surrounding context into the prediction task. We apply our proposed Multi-GRU-RCN model to FengYun-2G satellite infrared data and compare the results to those of the state-of-the-art method of cloud-motion prediction, the variational optical flow (VOF) method, and two well-known deep-learning models, namely, the convolutional long short-term memory (ConvLSTM) and GRU. The Multi-GRU-RCN model predicts intra-hour cloud motion better than the other methods, with the largest peak signal-to-noise ratio and structural similarity index. The results prove the applicability of the GRU-RCN method for solving the spatiotemporal data prediction problem and indicate the advantages of our model for further applications. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning: Application in Predictive Hydrological Models)

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18 pages, 1507 KiB

Open AccessArticle

Robust Decadal Hydroclimate Predictions for Northern Italy Based on a Twofold Statistical Approach

by Sara Rubinetti, Carla Taricco, Silvia Alessio, Angelo Rubino, Ilaria Bizzarri and Davide Zanchettin

Atmosphere 2020, 11(6), 671; https://doi.org/10.3390/atmos11060671 - 25 Jun 2020

Cited by 7 | Viewed by 2722

Abstract

The Mediterranean area belongs to the regions most exposed to hydroclimatic changes, with a likely increase in frequency and duration of droughts in the last decades. However, many climate records like, e.g., North Italian precipitation and river discharge records, indicate that significant decadal variability is often superposed or even dominates long-term hydrological trends. The capability to accurately predict such decadal changes is, therefore, of utmost environmental and social importance. Here, we present a twofold decadal forecast of Po River (Northern Italy) discharge obtained with a statistical approach consisting of the separate application and cross-validation of autoregressive models and neural networks. Both methods are applied to each significant variability component extracted from the raw discharge time series using Singular Spectrum Analysis, and the final forecast is obtained by merging the predictions of the individual components. The obtained 25-year forecasts robustly indicate a prominent dry period in the late 2020s/early 2030s. Our prediction provides information of great value for hydrological management, and a target for current and future near-term numerical hydrological predictions. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning: Application in Predictive Hydrological Models)

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13 pages, 1938 KiB

Open AccessArticle

Temporal Hydrological Drought Index Forecasting for New South Wales, Australia Using Machine Learning Approaches

by Abhirup Dikshit, Biswajeet Pradhan and Abdullah M. Alamri

Atmosphere 2020, 11(6), 585; https://doi.org/10.3390/atmos11060585 - 03 Jun 2020

Cited by 48 | Viewed by 5190

Abstract

Droughts can cause significant damage to agriculture and water resources leading to severe economic losses. One of the most important aspects of drought management is to develop useful tools to forecast drought events, which could be helpful in mitigation strategies. The recent global trends in drought events reveal that climate change would be a dominant factor in influencing such events. The present study aims to understand this effect for the New South Wales (NSW) region of Australia, which has suffered from several droughts in recent decades. The understanding of the drought is usually carried out using a drought index, therefore the Standard Precipitation Evaporation Index (SPEI) was chosen as it uses both rainfall and temperature parameters in its calculation and has proven to better reflect drought. The drought index was calculated at various time scales (1, 3, 6, and 12 months) using a Climate Research Unit (CRU) dataset. The study focused on predicting the temporal aspect of the drought index using 13 different variables, of which eight were climatic drivers and sea surface temperature indices, and the remainder were various meteorological variables. The models used for forecasting were an artificial neural network (ANN) and support vector regression (SVR). The model was trained from 1901–2010 and tested for nine years (2011–2018), using three different performance metric scores (coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE). The results indicate that ANN was better than SVR in predicting temporal drought trends, with the highest R² value of 0.86 for the former compared to 0.75 for the latter. The study also reveals that sea surface temperatures and the climatic index (Pacific Decadal Oscillation) do not have a significant effect on the temporal drought aspect. The present work can be considered as a first step, wherein we only study the temporal trends, towards the use of climatological variables and drought incidences for the NSW region. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning: Application in Predictive Hydrological Models)

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25 pages, 6321 KiB

Open AccessArticle

Pan Evaporation Estimation in Uttarakhand and Uttar Pradesh States, India: Validity of an Integrative Data Intelligence Model

by Anurag Malik, Priya Rai, Salim Heddam, Ozgur Kisi, Ahmad Sharafati, Sinan Q. Salih, Nadhir Al-Ansari and Zaher Mundher Yaseen

Atmosphere 2020, 11(6), 553; https://doi.org/10.3390/atmos11060553 - 27 May 2020

Cited by 40 | Viewed by 3975

Abstract

Appropriate input selection for the estimation matrix is essential when modeling non-linear progression. In this study, the feasibility of the Gamma test (GT) was investigated to extract the optimal input combination as the primary modeling step for estimating monthly pan evaporation (EP_m). A new artificial intelligent (AI) model called the co-active neuro-fuzzy inference system (CANFIS) was developed for monthly EP_m estimation at Pantnagar station (located in Uttarakhand State) and Nagina station (located in Uttar Pradesh State), India. The proposed AI model was trained and tested using different percentages of data points in scenarios one to four. The estimates yielded by the CANFIS model were validated against several well-established predictive AI (multilayer perceptron neural network (MLPNN) and multiple linear regression (MLR)) and empirical (Penman model (PM)) models. Multiple statistical metrics (normalized root mean square error (NRMSE), Nash–Sutcliffe efficiency (NSE), Pearson correlation coefficient (PCC), Willmott index (WI), and relative error (RE)) and graphical interpretation (time variation plot, scatter plot, relative error plot, and Taylor diagram) were performed for the modeling evaluation. The results of appraisal showed that the CANFIS-1 model with six input variables provided better NRMSE (0.1364, 0.0904, 0.0947, and 0.0898), NSE (0.9439, 0.9736, 0.9703, and 0.9799), PCC (0.9790, 0.9872, 0.9877, and 0.9922), and WI (0.9860, 0.9934, 0.9927, and 0.9949) values for Pantnagar station, and NRMSE (0.1543, 0.1719, 0.2067, and 0.1356), NSE (0.9150, 0.8962, 0.8382, and 0.9453), PCC (0.9643, 0.9649, 0.9473, and 0.9762), and WI (0.9794, 0.9761, 0.9632, and 0.9853) values for Nagina stations in all applied modeling scenarios for estimating the monthly EP_m. This study also confirmed the supremacy of the proposed integrated GT-CANFIS model under four different scenarios in estimating monthly EP_m. The results of the current application demonstrated a reliable modeling methodology for water resource management and sustainability. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning: Application in Predictive Hydrological Models)

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19 pages, 9697 KiB

Open AccessArticle

Precipitation Nowcasting with Orographic Enhanced Stacked Generalization: Improving Deep Learning Predictions on Extreme Events

by Gabriele Franch, Daniele Nerini, Marta Pendesini, Luca Coviello, Giuseppe Jurman and Cesare Furlanello

Atmosphere 2020, 11(3), 267; https://doi.org/10.3390/atmos11030267 - 07 Mar 2020

Cited by 42 | Viewed by 8590

Abstract

One of the most crucial applications of radar-based precipitation nowcasting systems is the short-term forecast of extreme rainfall events such as flash floods and severe thunderstorms. While deep learning nowcasting models have recently shown to provide better overall skill than traditional echo extrapolation models, they suffer from conditional bias, sometimes reporting lower skill on extreme rain rates compared to Lagrangian persistence, due to excessive prediction smoothing. This work presents a novel method to improve deep learning prediction skills in particular for extreme rainfall regimes. The solution is based on model stacking, where a convolutional neural network is trained to combine an ensemble of deep learning models with orographic features, doubling the prediction skills with respect to the ensemble members and their average on extreme rain rates, and outperforming them on all rain regimes. The proposed architecture was applied on the recently released TAASRAD19 radar dataset: the initial ensemble was built by training four models with the same TrajGRU architecture over different rainfall thresholds on the first six years of the dataset, while the following three years of data were used for the stacked model. The stacked model can reach the same skill of Lagrangian persistence on extreme rain rates while retaining superior performance on lower rain regimes. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning: Application in Predictive Hydrological Models)

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17 pages, 1508 KiB

Open AccessArticle

Prediction of Precipitation Based on Recurrent Neural Networks in Jingdezhen, Jiangxi Province, China

by Jinle Kang, Huimin Wang, Feifei Yuan, Zhiqiang Wang, Jing Huang and Tian Qiu

Atmosphere 2020, 11(3), 246; https://doi.org/10.3390/atmos11030246 - 29 Feb 2020

Cited by 38 | Viewed by 4669

Abstract

Precipitation is a critical input for hydrologic simulation and prediction, and is widely used for agriculture, water resources management, and prediction of flood and drought, among other activities. Traditional precipitation prediction researches often established one or more probability models of historical data based on the statistical prediction methods and machine learning techniques. However, few studies have been attempted deep learning methods such as the state-of-the-art for Recurrent Neural Networks (RNNs) networks in meteorological sequence time series predictions. We deployed Long Short-Term Memory (LSTM) network models for predicting the precipitation based on meteorological data from 2008 to 2018 in Jingdezhen City. After identifying the correlation between meteorological variables and the precipitation, nine significant input variables were selected to construct the LSTM model. Then, the selected meteorological variables were refined by the relative importance of input variables to reconstruct the LSTM model. Finally, the LSTM model with final selected input variables is used to predict the precipitation and the performance is compared with other classical statistical algorithms and the machine learning algorithms. The experimental results show that the LSTM is suitable for precipitation prediction. The RNN models, combined with meteorological variables, could predict the precipitation accurately in Jingdezhen City and provide sufficient time to prepare strategies against potential related disasters. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning: Application in Predictive Hydrological Models)

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