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Abstract

Rain gardens are one of the best measures for rainfall runoff and pollutant abatement in sponge city construction. The rain garden system was designed and developed for the problem of severely impeded urban water circulation. The rain gardens monitored the rainfall runoff abatement and pollutant removal capacity for 46 sessions from January 2018 to December 2019. Based on these data, the impact of rain gardens on runoff abatement rate and pollutant removal rate was studied. The results obtained indicated that the rain garden on the runoff abatement rate reached 82.5%, except with extreme rainfall, all fields of rainfall can be effectively abated. The removal rate of suspended solid particles was the highest, followed by total nitrogen and total phosphorus, the total removal rate in 66.35% above. The rain garden is still in the “youth stage”, and all aspects of the operation effect are good.
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Authors and Affiliations

Weijia Liu
1
Qingbao Pei
2
Wenbiao Dong
2
Pengfan Chen
2

  1. East China University of Technology, Nanchang, China
  2. Nanchang Institute of Technology Poyang Lake Basin Water Engineering Safety and Efficient Utilization National and Local Joint Engineering Laboratory, Nanchang, China
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Abstract

Over the past two decades, artificial neural networks (ANN) have exhibited a significant progress in predicting and modeling non-linear hydrological applications, such as the rainfall-runoff process which can provide useful contribution to water resources planning and management. This research aims to test the practicability of using ANNs with various input configurations to model the rainfall-runoff relationship in the Seybouse basin located in a semi-arid region in Algeria. Initially, the ANNs were developed for six sub-basins, and then for the complete watershed, considering four different input configurations. The 1st (ANN IP) considers only precipitation as an input variable for the daily flow simulation. The 2nd (ANN II) considers the 2nd variable in the model input with precipitation; it is one of the meteorological parameters (evapotranspiration, temperature, humidity, or wind speed). The third (ANN IIIP,T,HUM) considers a combination of temperature, humidity, and precipitation. The last (ANN VP,ET,T,HUM,Vw) consists in collating different meteorological parameters with precipitation as an input variable. ANN models are made for the whole basin with the same configurations as specified above. Better flow simulations were provided by (ANN IIP,T) and (ANN IIP,Vw) for the two stations of Medjez-Amar II and Bordj-Sabath, respectively. However, the (ANN VP,ET,T,HUM,Vw)’s application for the other stations and also for the entire basin reflects a strategy for the flow simulation and shows enhancement in the prediction accuracy over the other models studied. This has shown and confirmed that the more input variables, as more efficient the ANN model is.
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Authors and Affiliations

Yamina Aoulmi
1
ORCID: ORCID
Nadir Marouf
1
ORCID: ORCID
Mohamed Amireche
1
ORCID: ORCID

  1. University of Larbi-Ben-M’hidi, Faculty of Sciences and Applied Sciences, Department of Hydraulic, Laboratory of Ecology and Environment, PO Box 358, 04000 Oum El Bouaghi, Algeria
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Abstract

Artificial neural networks are widely employed as data mining methods by researchers across various fields, including rainfall-runoff (R-R) statistical modelling. To enhance the performance of these networks, deep learning (DL) neural networks have been developed to improve modelling accuracy. The present study aims to improve the effectiveness of DL networks in enhancing the performance of artificial neural networks via merging with the gradient boosting (GB) technique for daily runoff data forecasting in the river Amu Darya, Uzbekistan. The obtained results showed that the new hybrid proposed model performed exceptionally well, achieving a 16.67% improvement in determination coefficient ( R2) and a 23.18% reduction in root mean square error ( RMSE) during the training phase compared to the single DL model. Moreover, during the verification phase, the hybrid model displayed remarkable performance, demonstrating a 66.67% increase in R 2 and a 50% reduction in RMSE. Furthermore, the hybrid model outperformed the single GB model by a significant margin. During the training phase, the new model showed an 18.18% increase in R 2 and a 25% reduction in RMSE. In the verification phase, it improved by an impressive 75% in R 2 and a 33.33% reduction in RMSE compared to the single GB model. These findings highlight the potential of the hybrid DL-GB model in improving daily runoff data forecasting in the challenging hydrological context of the Amu Darya River basin in Uzbekistan.
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Authors and Affiliations

Barno S. Abdullaeva
1
ORCID: ORCID

  1. Tashkent State Pedagogical University, Faculty of Math and Physics, 27 Bunyodkor Ave, 100070, Tashkent, Uzbekistan
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Abstract

Scarcity of freshwater is one of the major issues which hinders nourishment in large portion of the countries like Ethio-pia. The communities in the Dawe River watershed are facing acute water shortage where water harvesting is vital means of survival. The purpose of this study was to identify optimal water harvesting areas by considering socioeconomic and biophysical factors. This was performed through the integration of soil and water assessment tool (SWAT) model, remote sensing (RS) and Geographic Information System (GIS) technique based on multi-criteria evaluation (MCE). The parame-ters used for the selection of optimal sites for rainwater harvesting were surface runoff, soil texture, land use land cover, slope gradient and stakeholders’ priority. Rainfall data was acquired from the neighbouring weather stations while infor-mation about the soil was attained from laboratory analysis using pipette method. Runoff depth was estimated using SWAT model. The statistical performance of the model in estimating the runoff was revealed with coefficient of determination (R2) of 0.81 and Nash–Sutcliffe Efficiency (NSE) of 0.76 for monthly calibration and R2 of 0.79 and NSE of 0.72 for monthly validation periods. The result implied that there's adequate runoff water to be conserved. Combination of hydrological model with GIS and RS was found to be a vital tool in estimating rainfall runoff and mapping suitable water harvest home sites.

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Authors and Affiliations

Arus E. Harka
Negash T. Roba
Asfaw K. Kassa

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