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The Use of GIS and Soft Computing Methods in Water...
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The Use of GIS and Soft Computing Methods in Water Resource Planning

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 11010

Special Issue Editors

Dr. Paraskevas Tsangaratos

E-Mail Website
Guest Editor
Department of Geological Studies, School of Mining and Metallurgical Engineering, National University of Athens, 15780 Athens, Greece
Interests: natural hazards; water resources; engineering geology; GIS; machine learning; soft computing; remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Ioanna Ilia

E-Mail Website
Guest Editor
Department of Geological Studies, School of Mining and Metallurgical Engineering, National University of Athens, 15780 Athens, Greece
Interests: natural hazards; environmental geology; engineering geology; GIS; machine learning
Special Issues, Collections and Topics in MDPI journals
Mr. Haoyuan Hong

E-Mail Website1 Website2
Guest Editor
Department of Geography and Regional Research, University of Vienna, 1010 Vienna, Austria
Interests: natural hazards; water resources; machine learning; soft computing; GIS
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The main objective of this Special Issue “The Use of GIS and Soft Computing Methods in Water Resource Planning” is to provide a forum for advancing the successful implementation of geographic information systems (GIS) and soft computing (SC) methods in water resources assessments.

SC methods have been acknowledged as advanced methods capable of discovering hidden and unknown patterns and trends from large databases, whereas GIS is presently the main and most important technology for spatial and temporal data manipulation and advanced modeling. The implementation of SC methods and GIS have contributed to the identification and evaluation of potential solutions related to water resource problems, providing knowledge essential to water resources management plans.

This Special Issue aims to provide an outlet for peer-reviewed publications that implement state-of-the-art methods and techniques incorporating SC methods and GIS to map, monitor, evaluate, and assess water resources. This Special Issue aims to cover, without being limited to, the following areas: (a) hydrologic and hydrogeological modeling of surface water, (b) groundwater monitoring, (c) groundwater spring potential mapping, (d) water supply and sewer systems modeling, (e) source pollution modeling, (f) stormwater control, and (g) flood susceptibility mapping and floods disaster management.


Dr. Paraskevas Tsangaratos
Dr. Ioanna Ilia
Mr. Haoyuan Hong
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. ISPRS International Journal of Geo-Information 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 1700 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

  • Geographic Information Systems
  • Soft computing
  • Machine learning
  • Hydrologic management
  • Hydrogeological modeling
  • Groundwater modeling
  • Groundwater spring potential mapping
  • Quality analysis of water
  • Flood susceptibility mapping
  • Floods disaster management
  • Water supply
  • Sewer systems modeling

Published Papers (3 papers)

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Research

18 pages, 2541 KiB  
Article
Geographic Information System Technology Combined with Back Propagation Neural Network in Groundwater Quality Monitoring
by Jing Sun and Genhou Wang
ISPRS Int. J. Geo-Inf. 2020, 9(12), 736; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9120736 - 09 Dec 2020
Cited by 3 | Viewed by 1889
Abstract
This study was conducted to explore the distribution and changes of groundwater resources in the research area, and to promote the application of geographic information system (GIS) technology and its deep learning methods in chemical type distribution and water quality prediction of groundwater. [...] Read more.
This study was conducted to explore the distribution and changes of groundwater resources in the research area, and to promote the application of geographic information system (GIS) technology and its deep learning methods in chemical type distribution and water quality prediction of groundwater. The Shiyang River Basin in Minqin County was selected as the research object for analyzing the natural components distribution and its preliminary forecast in partial areas. With the priority control of groundwater pollutants, the concentration changes of four indicators (including the permanganate index) in different spatial distributions were analyzed based on the GIS technology, so as to provide a basis for the groundwater quality prediction. Taking the permanganate as a benchmark, this study evaluated the prediction effects of the conventional back propagation (BP) neural network (BPNN) model and the optimized BPNN based on the golden section (GBPNN) and wavelet transform (WBPNN). The algorithm proposed in this study is compared with several classic prediction algorithms for analysis. Groundwater quality level and distribution rules in the research area are evaluated with the proposed algorithm and GIS technology. The results reveal that GIS technology can characterize the spatial concentration distribution of natural indicators and analyze the chemical distribution of groundwater quality based on it. In contrast, the WBPNN has the best prediction result. Its average error of the whole process is 3.66%, and the errors corresponding to the six predicated values are all below 10%, which is dramatically better than the values of the other two models. The maximal prediction accuracy of the proposed algorithm is 97.68%, with an average accuracy of 96.12%. The prediction results on the water quality level are consistent with the actual condition, and the spatial distribution rules of the groundwater water quality can be shown clearly with the GIS technology combined with the proposed algorithm. Therefore, it is of great significance to explore the distribution and changes of regional groundwater quality, and this studywill play a critical role in determining the groundwater quality. Full article
(This article belongs to the Special Issue The Use of GIS and Soft Computing Methods in Water Resource Planning)
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19 pages, 3216 KiB  
Article
Daily Water Level Prediction of Zrebar Lake (Iran): A Comparison between M5P, Random Forest, Random Tree and Reduced Error Pruning Trees Algorithms
by Viet-Ha Nhu, Himan Shahabi, Ebrahim Nohani, Ataollah Shirzadi, Nadhir Al-Ansari, Sepideh Bahrami, Shaghayegh Miraki, Marten Geertsema and Hoang Nguyen
ISPRS Int. J. Geo-Inf. 2020, 9(8), 479; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9080479 - 31 Jul 2020
Cited by 43 | Viewed by 3739
Abstract
Zrebar Lake is one of the largest freshwater lakes in Iran and it plays an important role in the ecosystem of the environment, while its desiccation has a negative impact on the surrounded ecosystem. Despite this, this lake provides an interesting recreation setting [...] Read more.
Zrebar Lake is one of the largest freshwater lakes in Iran and it plays an important role in the ecosystem of the environment, while its desiccation has a negative impact on the surrounded ecosystem. Despite this, this lake provides an interesting recreation setting in terms of ecotourism. The prediction and forecasting of the water level of the lake through simple but practical methods can provide a reliable tool for future lake water resource management. In the present study, we predict the daily water level of Zrebar Lake in Iran through well-known decision tree-based algorithms, including the M5 pruned (M5P), random forest (RF), random tree (RT) and reduced error pruning tree (REPT). We used five different water input combinations to find the most effective one. For our modeling, we chose 70% of the dataset for training (from 2011 to 2015) and 30% for model evaluation (from 2015 to 2017). We evaluated the models’ performances using different quantitative (root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), percent bias (PBIAS) and ratio of the root mean square error to the standard deviation of measured data (RSR)) and visual frameworks (Taylor diagram and box plot). Our results showed that water level with a one-day lag time had the highest effect on the result and, by increasing the lag time, its effect on the result was decreased. This result indicated that all the developed models had a good prediction capability, but the M5P model outperformed the others, followed by RF and RT equally and then REPT. Our results showed that these algorithms can predict water level accurately only with a one-day lag time in water level as an input and they are cost-effective tools for future predictions. Full article
(This article belongs to the Special Issue The Use of GIS and Soft Computing Methods in Water Resource Planning)
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20 pages, 14418 KiB  
Article
Performance Evaluation of GIS-Based Artificial Intelligence Approaches for Landslide Susceptibility Modeling and Spatial Patterns Analysis
by Xinxiang Lei, Wei Chen and Binh Thai Pham
ISPRS Int. J. Geo-Inf. 2020, 9(7), 443; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9070443 - 17 Jul 2020
Cited by 41 | Viewed by 4611
Abstract
The main purpose of this study was to apply the novel bivariate weights-of-evidence-based SysFor (SF) for landslide susceptibility mapping, and two machine learning techniques, namely the naïve Bayes (NB) and Radial basis function networks (RBFNetwork), as benchmark models. Firstly, by using aerial photos [...] Read more.
The main purpose of this study was to apply the novel bivariate weights-of-evidence-based SysFor (SF) for landslide susceptibility mapping, and two machine learning techniques, namely the naïve Bayes (NB) and Radial basis function networks (RBFNetwork), as benchmark models. Firstly, by using aerial photos and geological field surveys, the 263 landslide locations in the study area were obtained. Next, the identified landslides were randomly classified according to the ratio of 70/30 to construct training data and validation models, respectively. Secondly, based on the landslide inventory map, combined with the geological and geomorphological characteristics of the study area, 14 affecting factors of the landslide were determined. The predictive ability of the selected factors was evaluated using the LSVM model. Using the WoE model, the relationship between landslides and affecting factors was analyzed by positive and negative correlation methods. The above three hybrid models were then used to map landslide susceptibility. Thirdly, the ROC curve and various statistical data (SE, 95% CI and MAE) were used to verify and compare the predictive power of the model. Compared with the other two models, the Sysfor model had a larger area under the curve (AUC) of 0.876 (training dataset) and 0.783 (validation dataset). Finally, by quantitatively comparing the susceptibility values of each pixel, the differences in spatial morphology of landslide susceptibility maps were compared, and the model was found to have limitations and effectiveness. The landslide susceptibility maps obtained by the three models are reasonable, and the landslide susceptibility maps generated by the SysFor model have the highest comprehensive performance. The results obtained in this paper can help local governments in land use planning, disaster reduction and environmental protection. Full article
(This article belongs to the Special Issue The Use of GIS and Soft Computing Methods in Water Resource Planning)
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