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One of the primary goals of watershed management is to proactively monitor and forecast flood water levels to provide early warning for timely evacuation plans and save lives. One of the most economical ways to accomplish this objective is to use remotely-sensed satellite signals. Previous studies have indicated that an Advanced Microwave Scanning Radiometer (AMSR) sensor can be used for river water level monitoring combined with a few in-situ hydrometric gauges for the ground-truth data collection. However, space-based signals are influnced by many error-inducing natural factors, such as dust and cloud cover. Hence, a hybrid method is proposed, which comprises of a multi-objective particle swarm optimization model, a decision tree classification algorithm, the Hotelling’s $T^{2}$ outlier detection, and a regression model to identify and replace inaccurate space-based signals. This complex hybrid method will be referred to, in this study, with the acronym (OCOR). In the first phase of this hybrid method, the outlier signals are detected and eliminated from the dataset, and in the second phase, the eliminated signals along with signals lost due to satellite technical problems are estimated by ground-truth data calibration using in situ hydrometric stations. The two case studies of the White and Willamette Rivers demonstrate the performance of OCOR in practical situations.

Although climate change impacts vary globally, for the Kabul River Basin (KRB), concerns are primarily associated with frequent flooding. This research describes the influence of headwater reservoirs on projections of climate change impacts and flood frequency, and how riparian countries can benefit from storing of floodwaters for use during dry seasons. Six climate change scenarios and two Representative Concentration Pathways (RCPs) are used in three periods of a quarter-century each. The Soil and Water Assessment Tool (SWAT) is used to assess how the proposed reservoirs will reduce flooding by ~38% during the wet season, reduce the flood frequency from five to 25 years return period, and increase low flows by ~110% during the dry season, which reflect an ~17.5% reduction in the glacier-covered area by the end of the century. The risks and benefits of reservoirs are highlighted in light of the developmental goals of Afghanistan and Pakistan. Key words: flooding, low flow, Soil and Water Assessment Tool (SWAT), Kabul River Basin, climate change. Bien que les repercussions du changement climatique varient a l'echelle mondiale, les preoccupations au sujet du bassin de la riviere Kaboul (BRK) sont principalement liees aux frequentes inondations. Cette recherche decrit l'incidence des reservoirs d'amont sur les projections des effets du changement climatique et de la frequence des inondations, et comment les pays riverains peuvent tirer profit du stockage des eaux de crue pour les utiliser pendant les saisons seches. Six scenarios de changement climatique et deux voies de concentration representatives (<< RCP >>) sont utilises en trois periodes d'un quart de siecle chacune. L'outil d'evaluation des sols et de l'eau (<< SWAT >>) est utilise pour evaluer comment les reservoirs proposes reduiront les inondations d'environ 38 % pendant la saison humide, reduiront la frequence des inondations de 5 a 25 ans et augmenteront les faibles debits d'environ 110 % pendant la saison seche, qui refletent une reduction d'environ 17,5 % de la superficie couverte de glaciers d'ici la fin du siecle. Les risques et les avantages des reservoirs sont mis en evidence a la lumiere des objectifs de developpement de l'Afghanistan et du Pakistan. Mots-cles : inondation, faible debit, outil d'evaluation des sols et de l'eau (<< SWAT >>), bassin de la riviere Kaboul (BRK), changement climatique., Introduction Dependency on freshwater resources in the densely populated areas at the footsteps of the Hindu Kush Himalayan (HKH) Mountains is rapidly increasing. The Kabul River Basin (KRB), as part [...]

Merging machine learning with the power of quantum computing holds great potential for data-driven decision making and the development of powerful models for complex datasets. This area offers the potential for improving the accuracy of the real-time prediction of renewable energy production, such as solar irradiance forecasting. However, the literature on this topic is sparse. Addressing this knowledge gap, this study aims to develop and evaluate a quantum neural network model for solar irradiance prediction up to 3 h in advance. The proposed model was compared with Support Vector Regression, Group Method of Data Handling, and Extreme Gradient Boost classical models. The proposed framework could provide competitive results compared to its competitors, considering forecasting intervals of 5 to 120 min ahead, where it was the fourth best-performing paradigm. For 3 h ahead predictions, the proposed model achieved the second-best results compared with the other approaches, reaching a root mean squared error of 77.55 W/m2 and coefficient of determination of 80.92% for global horizontal irradiance forecasting. The results for longer forecasting horizons suggest that the quantum model may process spatiotemporal information from the input dataset in a manner not attainable by the current classical approaches, thus improving forecasting capacity in longer predictive windows. [ABSTRACT FROM AUTHOR]

The turfgrass industry supports golf courses, sports fields, and the landscaping and lawn care industries worldwide. Identifying the problem spots in turfgrass is crucial for targeted remediation for turfgrass treatment. There have been attempts to create vehicle- or drone-based scanners to predict turfgrass quality; however, these methods often have issues associated with high costs and/or a lack of accuracy due to using colour rather than grass height (R2 = 0.30 to 0.90). The new vehicle-mounted turfgrass scanner system developed in this study allows for faster data collection and a more accurate representation of turfgrass quality compared to currently available methods while being affordable and reliable. The Gryphon Turf Canopy Scanner (GTCS), a low-cost one-dimensional LiDAR array, was used to scan turfgrass and provide information about grass height, density, and homogeneity. Tests were carried out over three months in 2021, with ground-truthing taken during the same period. When utilizing non-linear regression, the system could predict the percent bare of a field (R2 = 0.47, root mean square error < 0.5 mm) with an increase in accuracy of 8% compared to the random forest metric. The potential environmental impact of this technology is vast, as a more targeted approach to remediation would reduce water, fertilizer, and herbicide usage. [ABSTRACT FROM AUTHOR]