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Application of Data Pre-post Processing Methods for Modeling Hydro-Climatologic Processes

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A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".

Deadline for manuscript submissions: closed (25 February 2022) | Viewed by 35749

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Prof. Dr. Vahid Nourani

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Guest Editor

Center of Excellence in Hydroinformatics and Faculty of Civil Engineering, University of Tabriz, 29 Bahman Ave., Tabriz 5166616471, Iran
Interests: artificial intelligence in hydrology; numerical methods in water sciences; geostatistics; stochastic hydrology; GIS and remote sensing applications in water science; climate change modeling
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Hüseyin Gökçekuş

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Guest Editor

Civil Engineering Department, Faculty of Civil and Environmental Engineering, Near East University, 99138 Nicosia, Cyprus
Interests: groundwater modeling; climate change; sedimentation; hydro-geological modeling; hydro-meteorology; water treatment

Special Issue Information

Dear Colleagues,

Understanding hydroclimatic processes (i.e., climate change, floods, droughts, etc.) is a fundamental issue in any water resource engineering study. There are several approaches to modeling hydroclimatic processes, usually categorized into three main groups of physical-based, conceptual, and black-box models, in all of which, the quantity, quality and precision of data are presumed to directly affect the simulation results of the modeling. Ensuring the quality of hydroclimatologic data has become a key issue in this field of study. Each type of data source (e.g., in situ data, radar data, satellite-based data, etc.) can contain noise, outliers, missing values, duplicate data or wrong data, which are unavoidable problems affecting the data collection that should be resolved via appropriate data processing and preparation approach(es).

On the other hand, with efficient computing facilities, hydrological data have grown exponentially and flooded into hydrological real-time databases. Mining more practical and valuable information from big data is receiving more and more attention with the rapid growth of hydrological data. In the big data mining of massive hydrological data, the accuracy and credibility of experiments and applications can be guaranteed only when the data quality and quantity are sufficient. Thus, data pre–post processing is an important task. It is a data mining technique that transforms raw data into a more understandable, useful and efficient format. Thus, employing pre–post processing methods for data (e.g., data transforming, data cleaning, filling data, and ensemble models) is a necessary step for the effective and more accurate modeling of hydroclimatologic processes with this high volume of available data. Via this Special Issue, the scientific community may offer new ideas that will go far beyond the theoretical approaches and prove to be applicable in practice.

Prof. Dr. Vahid Nourani
Prof. Dr. Hüseyin Gökçekuş
Guest Editors

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Keywords

hydroclimatological modeling
machine learning
artificial intelligence
data processing
data mining
data transformation
model fusion
remote sensing
ensemble modeling

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Published Papers (9 papers)

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Research

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18 pages, 2917 KiB

Open AccessArticle

Groundwater Extraction Reduction within an Irrigation District by Enhancing the Surface Water Distribution

by Hamed Tork, Saman Javadi, Seyed Mehdy Hashemy Shahdany, Ronny Berndtsson and Sami Ghordoyee Milan

Water 2022, 14(10), 1610; https://doi.org/10.3390/w14101610 - 17 May 2022

Cited by 4 | Viewed by 2092

Abstract

Today, in developing countries, the low surface water distribution efficiency and the lack of supplying water needs of farmers by surface water resources are compensated by excessive aquifer water withdrawal. This mismanagement has caused a sharp drop in the groundwater level in many countries. On the other hand, climate change and drought have intensified the pressure on water resources. This study aims to evaluate novel strategies for developing surface water distribution systems for stress reduction of the Najafabad aquifer in Isfahan, central plateau of Iran. The performance of several strategies for agricultural water distribution and delivery, such as hydro-mechanical operating system, manual-based operating system, and centralized automatic operating system, was evaluated in this study. In the first step, two indices, i.e., water distribution adequacy and dependability, were obtained using a flow hydraulic simulation model. Then, the water distribution adequacy map and amount of reduction in the water withdrawal of existing wells were determined for each strategy. Finally, using the MODFLOW groundwater simulation model, the changes in groundwater levels due to the normal and drought scenarios (15 and 30%) were extracted during five years for each strategy. The findings for the normal scenario showed that the centralized automatic operating system strategy had the most significant impact on agricultural water management in the surface water distribution system with a 30% increase in agricultural water distribution adequacy index compared to the current situation. This strategy increased the groundwater level by 11.6 m and closed 35% of the groundwater wells. In this scenario, the hydro-mechanical operating system strategy had the weakest performance by increasing the aquifer level by only 1.31 m. In the 15% and 30% drought scenarios, the centralized automatic operating system strategy exerted the best performance among other strategies by increasing the aquifer water level by 10.18 and 9.4 m, respectively, compared to the current situation. Finally, the results showed that the spatial segmentation of the aquifer exerted better efficiency and better monitoring in the more susceptible regions. Full article

(This article belongs to the Special Issue Application of Data Pre-post Processing Methods for Modeling Hydro-Climatologic Processes)

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12 pages, 2087 KiB

Open AccessFeature PaperArticle

A New Evolutionary Hybrid Random Forest Model for SPEI Forecasting

by Ali Danandeh Mehr, Ali Torabi Haghighi, Masood Jabarnejad, Mir Jafar Sadegh Safari and Vahid Nourani

Water 2022, 14(5), 755; https://doi.org/10.3390/w14050755 - 27 Feb 2022

Cited by 12 | Viewed by 3497

Abstract

State-of-the-art random forest (RF) models have been documented as versatile tools to solve regression and classification problems in hydrology. They can model stochastic time series by bagging different decision trees. This article introduces a new hybrid RF model that increases the forecasting accuracy of RF-based models. The new model, called GARF, is attained by integrating genetic algorithm (GA) and hybrid random forest (RF), in which different decision trees are bagged. We applied GARF to model and forecast a multitemporal drought index (SPEI-3 and SPEI-6) at two meteorology stations (Beypazari and Nallihan) in Ankara, Turkey. We compared the associated results with classic RF, standalone extreme learning machine (ELM), and a hybrid ELM model optimized by Bat algorithm (Bat-ELM) to verify the new model accuracy. The performance assessment was performed using graphical and statistical analysis. The forecasting results demonstrated that the GARF outperformed the benchmark models. GARF achieved the least error in a quantitative assessment for the prediction of both SPEI-3 and SPEI-6, particularly in the testing period. The results of this study showed that the new model can improve the forecasting accuracy of the classic RF technique up to 30% and 40% at Beypazari and Nallihan stations, respectively. Full article

(This article belongs to the Special Issue Application of Data Pre-post Processing Methods for Modeling Hydro-Climatologic Processes)

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25 pages, 55512 KiB

Open AccessFeature PaperArticle

A Combination of Metaheuristic Optimization Algorithms and Machine Learning Methods Improves the Prediction of Groundwater Level

by Zahra Kayhomayoon, Faezeh Babaeian, Sami Ghordoyee Milan, Naser Arya Azar and Ronny Berndtsson

Water 2022, 14(5), 751; https://doi.org/10.3390/w14050751 - 26 Feb 2022

Cited by 27 | Viewed by 3306

Abstract

Groundwater is a crucial source of water supply in drought conditions, and an auxiliary water source in wet seasons. Due to its increasing importance in view of climate change, predicting groundwater level (GWL) needs to be improved to enhance management. We used adaptive neuro-fuzzy inference systems (ANFIS) to predict the GWL of the Urmia aquifer in northwestern Iran under various input scenarios using precipitation, temperature, groundwater withdrawal, GWL during the previous month, and river flow. In total, 11 input patterns from various combinations of variables were developed. About 70% of the data were used to train the models, while the rest were used for validation. In a second step, several metaheuristic algorithms, such as genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization for continuous domains (ACOR), and differential evolution (DE) were used to improve the model and, consequently, prediction performance. The results showed that (i) RMSE, MAPE, and NSE of 0.51 m, 0.00037 m, and 0.86, respectively, were obtained for the ANFIS model using all input variables, indicating a rather poor performance, (ii) metaheuristic algorithms were able to optimize the parameters of the ANFIS model in predicting GWL, (iii) the input pattern that included all input variables resulted in the most appropriate performance with RMSE, MAPE, and NSE of 0.28 m, 0.00019 m, and 0.97, respectively, using the ANIFS-ACOR hybrid model, (iv) results of Taylor’s diagram (CC = 0.98, STD = 0.2, and RMSD = 0.30), as well as the scatterplot (R² = 0.97), showed that best prediction was achieved by ANFIS-ACOR, and (v) temperature and evaporation exerted stronger influence on GWL prediction than groundwater withdrawal and precipitation. The findings of this study reveal that metaheuristic algorithms can significantly improve the performance of the ANFIS model in predicting GWL. Full article

(This article belongs to the Special Issue Application of Data Pre-post Processing Methods for Modeling Hydro-Climatologic Processes)

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17 pages, 2741 KiB

Open AccessFeature PaperArticle

Modelling Precipitation Intensities from X-Band Radar Measurements Using Artificial Neural Networks—A Feasibility Study for the Bavarian Oberland Region

by Stefanie Vogl, Patrick Laux, Joachim Bialas and Christian Reifenberger

Water 2022, 14(3), 276; https://doi.org/10.3390/w14030276 - 18 Jan 2022

Cited by 1 | Viewed by 1612

Abstract

Radar data may potentially provide valuable information for precipitation quantification, especially in regions with a sparse network of in situ observations or in regions with complex topography. Therefore, our aim is to conduct a feasibility study to quantify precipitation intensities based on radar measurements and additional meteorological variables. Beyond the well-established Z–R relationship for the quantification, this study employs Artificial Neural Networks (ANNs) in different settings and analyses their performance. For this purpose, the radar data of a station in Upper Bavaria (Germany) is used and analysed for its performance in quantifying in situ observations. More specifically, the effects of time resolution, time offsets in the input data, and meteorological factors on the performance of the ANNs are investigated. It is found that ANNs that use actual reflectivity as only input are outperforming the standard Z–R relationship in reproducing ground precipitation. This is reflected by an increase in correlation between modelled and observed data from 0.67 (Z–R) to 0.78 (ANN) for hourly and 0.61 to 0.86, respectively, for 10 min time resolution. However, the focus of this study was to investigate if model accuracy benefits from additional input features. It is shown that an expansion of the input feature space by using time-lagged reflectivity with lags up to two and additional meteorological variables such as temperature, relative humidity, and sunshine duration significantly increases model performance. Thus, overall, it is shown that a systematic predictor screening and the correspondent extension of the input feature space substantially improves the performance of a simple Neural Network model. For instance, air temperature and relative humidity provide valuable additional input information. It is concluded that model performance is dependent on all three ingredients: time resolution, time lagged information, and additional meteorological input features. Taking all of these into account, the model performance can be optimized to a correlation of 0.9 and minimum model bias of 0.002 between observed and modelled precipitation data even with a simple ANN architecture. Full article

(This article belongs to the Special Issue Application of Data Pre-post Processing Methods for Modeling Hydro-Climatologic Processes)

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15 pages, 3873 KiB

Open AccessArticle

Sea Level Prediction Using Machine Learning

by Rifat Tur, Erkin Tas, Ali Torabi Haghighi and Ali Danandeh Mehr

Water 2021, 13(24), 3566; https://doi.org/10.3390/w13243566 - 13 Dec 2021

Cited by 16 | Viewed by 4707

Abstract

Sea level prediction is essential for the design of coastal structures and harbor operations. This study presents a methodology to predict sea level changes using sea level height and meteorological factor observations at a tide gauge in Antalya Harbor, Turkey. To this end, two different scenarios were established to explore the most feasible input combinations for sea level prediction. These scenarios use lagged sea level observations (SC1), and both lagged sea level and meteorological factor observations (SC2) as the input for predictive modeling. Cross-correlation analysis was conducted to determine the optimum input combination for each scenario. Then, several predictive models were developed using linear regressions (MLR) and adaptive neuro-fuzzy inference system (ANFIS) techniques. The performance of the developed models was evaluated in terms of root mean squared error (RMSE), mean absolute error (MAE), scatter index (SI), and Nash Sutcliffe Efficiency (NSE) indices. The results showed that adding meteorological factors as input parameters increases the performance accuracy of the MLR models up to 33% for short-term sea level predictions. Moreover, the results contributed a more precise understanding that ANFIS is superior to MLR for sea level prediction using SC1- and SC2-based input combinations. Full article

(This article belongs to the Special Issue Application of Data Pre-post Processing Methods for Modeling Hydro-Climatologic Processes)

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23 pages, 9490 KiB

Open AccessArticle

Exploring the Potential of the Cost-Efficient TAHMO Observation Data for Hydro-Meteorological Applications in Sub-Saharan Africa

by Julia Schunke, Patrick Laux, Jan Bliefernicht, Moussa Waongo, Windmanagda Sawadogo and Harald Kunstmann

Water 2021, 13(22), 3308; https://doi.org/10.3390/w13223308 - 22 Nov 2021

Cited by 5 | Viewed by 2552

Abstract

The Trans-African Hydro-Meteorological Observatory (TAHMO) is a promising initiative aiming to install 20,000 stations in sub-Saharan Africa counteracting the decreasing trend of available measuring stations. To achieve this goal, it is particularly important that the installed weather stations are cost-efficient, appropriate for African conditions, and reliably measure the most important variables for hydro-meteorological applications. Since there exist no performance studies of TAHMO stations while operating in Africa, it is necessary to investigate their performance under different climate conditions. This study provides a first analysis of the performance of 10 selected TAHMO stations across Burkina Faso (BF). More specifically, the analysis consists of missing value statistics, plausibility tests of temperature (minimum, maximum) and precipitation, spatial dependencies (correlograms) by comparison with daily observations from synoptical stations of the BF meteorological service as well as cross-comparison between the TAHMO stations. Based on the results of this study for BF for the period from May 2017 to December 2020, it is concluded that TAHMO potentially offers a reliable and cost-efficient solution for applications in hydro-meteorology. The usage of wind speed measurements cannot be recommended without reservation, at least not without bias correcting of the data. The limited measurement period of TAHMO still prevents its usability in climate (impact) research. It is also stressed that TAHMO cannot replace existing observation networks operated by the local meteorological services, but it can be a complement and has great potential for detailed spatial analyses. Since restricted to BF in this analysis, more evaluation studies of TAHMO are needed considering different environmental and climate conditions across SSA. Full article

(This article belongs to the Special Issue Application of Data Pre-post Processing Methods for Modeling Hydro-Climatologic Processes)

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15 pages, 1891 KiB

Open AccessArticle

Examining the Applicability of Wavelet Packet Decomposition on Different Forecasting Models in Annual Rainfall Prediction

by Hua Wang, Wenchuan Wang, Yujin Du and Dongmei Xu

Water 2021, 13(15), 1997; https://doi.org/10.3390/w13151997 - 21 Jul 2021

Cited by 23 | Viewed by 2753

Abstract

Accurate precipitation prediction can help plan for different water resources management demands and provide an extension of lead-time for the tactical and strategic planning of courses of action. This paper examines the applicability of several forecasting models based on wavelet packet decomposition (WPD) in annual rainfall forecasting, and a novel hybrid precipitation prediction framework (WPD-ELM) is proposed coupling extreme learning machine (ELM) and WPD. The works of this paper can be described as follows: (a) WPD is used to decompose the original precipitation data into several sub-layers; (b) ELM model, autoregressive integrated moving average model (ARIMA), and back-propagation neural network (BPNN) are employed to realize the forecasting computation for the decomposed series; (c) the results are integrated to attain the final prediction. Four evaluation indexes (RMSE, MAE, R, and NSEC) are adopted to assess the performance of the models. The results indicate that the WPD-ELM model outperforms other models used in this paper and WPD can significantly enhance the performance of forecasting models. In conclusion, WPD-ELM can be a promising alternative for annual precipitation forecasting and WPD is an effective data pre-processing technique in producing convincing forecasting models. Full article

(This article belongs to the Special Issue Application of Data Pre-post Processing Methods for Modeling Hydro-Climatologic Processes)

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Review

Jump to: Research

28 pages, 3603 KiB

Open AccessReview

The Applications of Soft Computing Methods for Seepage Modeling: A Review

by Vahid Nourani, Nazanin Behfar, Dominika Dabrowska and Yongqiang Zhang

Water 2021, 13(23), 3384; https://doi.org/10.3390/w13233384 - 1 Dec 2021

Cited by 7 | Viewed by 5932

Abstract

In recent times, significant research has been carried out into developing and applying soft computing techniques for modeling hydro-climatic processes such as seepage modeling. It is necessary to properly model seepage, which creates groundwater sources, to ensure adequate management of scarce water resources. On the other hand, excessive seepage can threaten the stability of earthfill dams and infrastructures. Furthermore, it could result in severe soil erosion and consequently cause environmental damage. Considering the complex and nonlinear nature of the seepage process, employing soft computing techniques, especially applying pre-post processing techniques as hybrid methods, such as wavelet analysis, could be appropriate to enhance modeling efficiency. This review paper summarizes standard soft computing techniques and reviews their seepage modeling and simulation applications in the last two decades. Accordingly, 48 research papers from 2002 to 2021 were reviewed. According to the reviewed papers, it could be understood that regardless of some limitations, soft computing techniques could simulate the seepage successfully either through groundwater or earthfill dam and hydraulic structures. Moreover, some suggestions for future research are presented. This review was conducted employing preferred reporting items for systematic reviews and meta-analyses (PRISMA) method. Full article

(This article belongs to the Special Issue Application of Data Pre-post Processing Methods for Modeling Hydro-Climatologic Processes)

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15 pages, 951 KiB

Open AccessReview

A Review of Neural Networks for Air Temperature Forecasting

by Trang Thi Kieu Tran, Sayed M. Bateni, Seo Jin Ki and Hamidreza Vosoughifar

Water 2021, 13(9), 1294; https://doi.org/10.3390/w13091294 - 4 May 2021

Cited by 67 | Viewed by 7737

Abstract

The accurate forecast of air temperature plays an important role in water resources management, land–atmosphere interaction, and agriculture. However, it is difficult to accurately predict air temperature due to its non-linear and chaotic nature. Several deep learning techniques have been proposed over the last few decades to forecast air temperature. This study provides a comprehensive review of artificial neural network (ANN)-based approaches (such as recurrent neural network (RNN), long short-term memory (LSTM), etc.), which were used to forecast air temperature. The focus is on the works during 2005–2020. The review shows that the neural network models can be employed as promising tools to forecast air temperature. Although the ANN-based approaches have been utilized widely to predict air temperature due to their fast computing speed and ability to deal with complex problems, no consensus yet exists on the best existing method. Additionally, it is found that the ANN methods are mainly viable for short-term air temperature forecasting. Finally, some future directions and recommendations are presented. Full article

(This article belongs to the Special Issue Application of Data Pre-post Processing Methods for Modeling Hydro-Climatologic Processes)

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