| 1. |
- Ahmadi, Farshad, et al.
(författare)
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Development of Bio-Inspired- and Wavelet-Based Hybrid Models for Reconnaissance Drought Index Modeling
- 2021
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Ingår i: Water Resources Management. - : Springer Science and Business Media LLC. - 0920-4741 .- 1573-1650. ; 35:12, s. 4127-4147
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Tidskriftsartikel (refereegranskat)abstract
- The present study aimed to model reconnaissance drought index (RDI) time series at three various time scales (i.e., RDI-6, RDI-9, RDI-12). Two weather stations located at Iran, namely Tehran and Dezful, were selected as the case study. First, support vector regression (SVR) was utilized as the standalone modeling technique. Then, hybrid models were implemented via coupling the standalone SVR with two bio-inspired-based techniques including firefly algorithm (FA) and whale optimization algorithm (WOA) as well as wavelet analysis (W). Accordingly, the hybrid SVR-FA, SVR-WOA, and W-SVR models were proposed. It is worth mentioning that six mother wavelets (i.e., Haar, Daubechies (db2, db4), Coifflet, Symlet, and Fejer-Korovkin) were employed in development of the hybrid W-SVR models. The performance of models was assessed through root mean square error (RMSE), mean absolute error (MAE), Willmott index (WI), and Nash-Sutcliffe efficiency (NSE). Generally, the implemented coupled models illustrated better results than the standalone SVR in modeling the RDI time series of studied locations. Besides, the Coifflet mother wavelet was found to be the best-performing wavelet. The most accurate results were achieved for RDI-12 modeling via the W-SVR utilizing db4(2) at Tehran station (RMSE = 0.253, MAE = 0.174, WI= 0.888, NSE = 0.934) and Coifflet(2) at Dezful station (RMSE = 0.301, MAE = 0.166, WI= 0.910, NSE = 0.936). As a result, the hybrid models developed in the current study, specifically W-SVR ones, can be proposed as suitable alternatives to the single SVR.
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| 2. |
- Coetzer-Liversage, A., et al.
(författare)
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Modeling Predictors of Water Conservation-Friendly Behavior Among the General Public : Structural Equation Modeling
- 2024
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Ingår i: Water resources management. - : Springer. - 0920-4741 .- 1573-1650.
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Tidskriftsartikel (refereegranskat)abstract
- Amid escalating global drought concerns and the imperative of water-saving practices, this 2022 methodological study in the southwest and central regions of Iran, involving 287 participants, employed structural equation modeling to investigate correlates of pro-water conservation behaviors using a validated TPB questionnaire. Findings revealed that attitudes positively influenced intentions (p < 0.05), subjective norms had dual effects on intentions and perceived control (p < 0.05), and perceived behavioral control positively impacted both intentions and behavior (p < 0.05), yet intentions did not significantly predict behavior (p > 0.05). Confirmatory factor analysis demonstrated favorable fit indices for TPB (CMin/df = 1.59, RMSEA = 0.04, CFI = 0.96, TLI = 0.95) and SEM models (CMin/df = 1.58, RMSEA = 0.04, CFI = 0.92, TLI = 0.91), reaffirming the model's validity. The Theory of Planned Behavior offers a potent framework for shaping water-conservation efforts, emphasizing attitudes, subjective norms, and perceived control. Further research is needed to gain a deeper understanding of the disparities that may exist between intentions and actual behaviors in conserving water.
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| 3. |
- Ek, Kristina, et al.
(författare)
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Priorities and Preferences in Water Quality Management : a Case Study of the Alsterån River Basin
- 2020
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Ingår i: Water resources management. - : Springer. - 0920-4741 .- 1573-1650. ; 34:1, s. 155-173
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Tidskriftsartikel (refereegranskat)abstract
- Sweden is a decentralised country where local managers, who are key actors in water management, often deal with relatively difficult prioritisations, tradeoffs and conflicting goals. Many of these challenges relate to the effective implementation of the European Union Water Framework Directive. As an input to these challenges, the present paper elicits and analyses local and semi-local citizens’ preferences for water quality attributes related to the European Water Framework directive in a river basin located in southeast of Sweden. Based on a choice experiment tailored to the case study area, the paper analyses preferences for selected attributes based on real criteria for ecological water status in the implementation of the directive. The target population lives in the municipalities through which the river passes, or in municipalities neighbouring those. Despite this spatial proximity to the river, the analysis reveals limited knowledge and interest in matters related to the environmental quality of the river. There is no evidence that preferences differ between respondents with regard to experience or knowledge about the water basin, nor with regard to recreational habits in the area. These results offer input to local water management by providing information about preferences for explicit water quality attributes.
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| 4. |
- Ha, Duong Hai, et al.
(författare)
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Quadratic Discriminant Analysis Based Ensemble Machine Learning Models for Groundwater Potential Modeling and Mapping
- 2021
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Ingår i: Water resources management. - : Springer. - 0920-4741 .- 1573-1650. ; 35:13, s. 4415-4433
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Tidskriftsartikel (refereegranskat)abstract
- In this study, the AdaBoost, MultiBoost and RealAdaBoost methods were combined with the Quadratic Discriminant Analysis method to develop three new GIS-based Machine Learning ensemble models, i.e., ABQDA, MBQDA, and RABQDA for groundwater potential mapping in the Dak Nong Province, Vietnam. In total, 227 groundwater wells and 12 conditioning factors (infiltration, rainfall, river density, topographic wetness index, sediment transport index, stream power index, elevation, aspect, curvature, slope, soil, and land use) were used for this study. Performance of the models was evaluated using the Area Under the Receiver Operating Characteristics Curve AUC (AUC) and several other performance metrics. The results showed that the ABQDA model that achieved AUC = 0.741 was superior to the other models in producing an accurate map of groundwater potential for the Dak Nong Province. The models and potential maps produced here can help policymakers and water resources managers to preserve an optimal exploit from these vital resources.
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| 5. |
- Hjorth, Peder, et al.
(författare)
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Adaptive Water Management : On the Need for Using the Post-WWII Science in Water Governance
- 2023
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Ingår i: Water Resources Management. - : Springer Science and Business Media LLC. - 0920-4741 .- 1573-1650. ; 37:6-7, s. 2247-2270
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Tidskriftsartikel (refereegranskat)abstract
- Although the UN concluded, already in 1997, that water would be the most contentious issue of the 21st century, water governance is still confused, nearly everywhere. Even the severe impacts of escalating water bankruptcy and global warming have so far failed to incur a marked improvement in governance systems. The global community has adopted sustainable development as a common vision and guide for the future. Yet, the adoption of the underlying principles of sustainable development has been slow in the water sector and elsewhere. Despite the realization that water governance is a political issue, the near-universal neoliberal agenda tends to only employ technologic and economic solutions to address water problems. This paper presents a historical overview, from the end of the Second World War (WWII) and onwards, of events that could, or should, have had an impact on water management frameworks. It evidences some important consequences of the institutional rigidity exposed during that period. The paper also turns to the fields of science, policy, and management, to pinpoint failures in the translation of political rhetoric as well as new scientific findings into change at the operational level. It explores how an updated knowledge base could serve a quest for sustainable water governance strategies. It is argued that a persistent failure to learn is an important reason behind the dire state that we are now in. As a result, water management is still based on century-old, technocratic, and instrumental methodologies that fail to take advantage of important scientific advancements since WWII and remain unable to properly deal with real-world complexities and uncertainties. The paper concludes that when it is linked to a transformation of the institutional superstructure, adaptive water management (AWM), a framework rooted in systems thinking, emerges as a prominent way to embark on a needed, radical transformation of the water governance systems.
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| 6. |
- Jeihouni, Mehrdad, et al.
(författare)
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Decision Tree-Based Data Mining and Rule Induction for Identifying High Quality Groundwater Zones to Water Supply Management : a Novel Hybrid Use of Data Mining and GIS
- 2020
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Ingår i: Water Resources Management. - : Springer Science and Business Media LLC. - 0920-4741 .- 1573-1650. ; 34:1, s. 139-154
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Tidskriftsartikel (refereegranskat)abstract
- Groundwater is an important source to supply drinking water demands in both arid and semi-arid regions. Nevertheless, locating high quality drinking water is a major challenge in such areas. Against this background, this study proceeds to utilize and compare five decision tree-based data mining algorithms including Ordinary Decision Tree (ODT), Random Forest (RF), Random Tree (RT), Chi-square Automatic Interaction Detector (CHAID), and Iterative Dichotomiser 3 (ID3) for rule induction in order to identify high quality groundwater zones for drinking purposes. The proposed methodology works by initially extracting key relevant variables affecting water quality (electrical conductivity, pH, hardness and chloride) out of a total of eight existing parameters, and using them as inputs for the rule induction process. The algorithms were evaluated with reference to both continuous and discrete datasets. The findings were speculative of the superiority, performance-wise, of rule induction using the continuous dataset as opposed to the discrete dataset. Based on validation results, in continuous dataset, RF and ODT showed higher and RT showed acceptable performance. The groundwater quality maps were generated by combining the effective parameters distribution maps using inducted rules from RF, ODT, and RT, in GIS environment. A quick glance at the generated maps reveals a drop in the quality of groundwater from south to north as well as from east to west in the study area. The RF showed the highest performance (accuracy of 97.10%) among its counterparts; and so the generated map based on rules inducted from RF is more reliable. The RF and ODT methods are more suitable in the case of continuous dataset and can be applied for rule induction to determine water quality with higher accuracy compared to other tested algorithms.
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| 7. |
- Lindqvist, Andreas, et al.
(författare)
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Human-Water Dynamics and their Role for Seasonal Water Scarcity – a Case Study
- 2021
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Ingår i: Water resources management. - : Springer Science and Business Media B.V.. - 0920-4741 .- 1573-1650. ; 35:10, s. 3043-3061
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Tidskriftsartikel (refereegranskat)abstract
- Ensuring sustainable management and an adequate supply of freshwater resources is a growing challenge around the world. Even in historically water abundant regions climate change together with population growth and economic development are processes that are expected to contribute to an increase in permanent and seasonal water scarcity in the coming decades. Previous studies have shown how policies to address water scarcity often fail to deliver lasting improvements because they do not account for how these processes influence, and are influenced by, human-water interactions shaping water supply and demand. Despite significant progress in recent years, place-specific understanding of the mechanisms behind human-water feedbacks remain limited, particularly in historically water abundant regions. To this end, we here present a Swedish case study where we, by use of a qualitative system dynamics approach, explore how human-water interactions have contributed to seasonal water scarcity at the local-to-regional scale. Our results suggest that the current approach to address water scarcity by inter-basin water transports contributes to increasing demand by creating a gap between the perceived and actual state of water resources among consumers. This has resulted in escalating water use and put the region in a state of systemic lock-in where demand-regulating policies are mitigated by increases in water use enabled by water transports. We discuss a combination of information and economic policy instruments to combat water scarcity, and we propose the use of quantitative simulation methods to further assess these strategies in future studies. © 2021, The Author(s).
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| 8. |
- Mokhtar, Ali, et al.
(författare)
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Prediction of Irrigation Water Requirements for Green Beans-Based Machine Learning Algorithm Models in Arid Region
- 2023
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Ingår i: Water resources management. - : Springer. - 0920-4741 .- 1573-1650. ; 37, s. 1557-1580
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Tidskriftsartikel (refereegranskat)abstract
- Water scarcity is the most obstacle faced by irrigation water requirements, likewise, limited available meteorological data to calculate reference evapotranspiration. Consequently, the focal aims of the investigation are to assess the potential of machine learning models in forecasting irrigation water requirements (IWR) of snap beans by evolving multi-scenarios of inputs parameters to figure out the impact of meteorological, crop, and soil parameters on IWR. Six models were applied, support vector regressor (SVR), random forest (RF), deep neural networks (DNN), convolutional neural networks (CNN), long short-term memory (LSTM), and Hybrid CNN-LSTM. Ten variables including maximum and minimum temperature, Relative humidity, wind speed, precipitation, root depth, basal crop coefficient, soil evaporation, a fraction of surface wetted and, exposed and soil wetted fraction were used as the input data for models with their combination, 8 input scenarios were designed. Overall models, the best scenario was scenario 4 (relative humidity, wind speed, basal crop coefficient, soil evaporation), however, the best scenario for DNN and RF model was scenario 7 (root depth, basal crop coefficient, soil evaporation, fraction of surface wetted, exposed and soil wetted fraction). While the weakest one was the group of climatic factors in scenario 6 (maximum temperature, minimum temperature, relative humidity, wind speed, and precipitation). Among the models, the hybrid LTSM & CNN was the most accurate and the SVR model had the lowest estimation accuracy. The outcomes of this research work could set up a modeling strategy that would set in motion the improvement of efforts to identify the shortages in IWR forecasting, which sequentially may support alleviation strategies such as policies for sustainable water use and water resources management. The current approach was promising and has research value for other similar regions.
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| 9. |
- Moosavi, Vahid, et al.
(författare)
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Linking Hydro-Physical Variables and Landscape Metrics using Advanced Data Mining for Stream-Flow Prediction
- 2022
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Ingår i: Water Resources Management. - : Springer Science and Business Media LLC. - 0920-4741 .- 1573-1650. ; 36:11, s. 4255-4273
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Tidskriftsartikel (refereegranskat)abstract
- In Streamflow prediction the most important triggering/controlling variables are related to climate, physiography, and landscape patterns. This study investigated the effect of different landscape metrics to relate spatial patterns to surface runoff processes and predict monthly streamflow using climatic and physiographic variables for the 42 sub-basins of the Urmia Lake Basin in Iran. We developed an innovative data-driven framework and considered two different modelling approaches i.e., modelling in homogenous clusters (local approach) and modelling in the entire area as an entity (global approach). The results of basin LULC monitoring from the 20-year experimental period display drastic changes in the land use of the basin such as reduction in lake area (48.3%) due to increasing irrigated areas (22.5%), increasing residential areas (14.2%), and decrease in rangeland (6.0%). Streamflow prediction results in the global experiment showed Group Method of Data Handling (GMDH) and Random Forest (RF) with NSE of 0.76 and NRMSE of 6.44% have similar results and outperformed Partial Least Squares regression (PLS), but in clustering experiment GMDH with NSE of 0.88 and NRMSE of 5% shows the highest accuracy and outperformed both RF and PLS. The results confirmed that modelling in homogenous clusters (local prediction) significantly enhanced the performance of prediction.
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