Flow forecast by SWAT model and ANN in Pracana basin, Portugal

132

Streamflow data are of prime importance to water-resources planning and management, and the accuracy of their estimation is very important for decision making. The Soil and Water Assessment Tool (SWAT) and Artificial Neural Network (ANN) models have been evaluated and compared to find a method to improve streamflow estimation. For a more complete evaluation, the accuracy and ability of these streamflow estimation models was also established separately based on their performance during different periods of flows using regional flow duration curves (FDCs). Specifically, the FDCs were divided into five sectors: very low, low, medium, high and very high flow. This segmentation of flow allows analysis of the model performance for every important discharge event precisely. In this study, the models were applied in two catchments in Peninsular Spain with contrasting climatic conditions: Atlantic and Mediterranean climates. The results indicate that SWAT and ANNs were generally good tools in daily streamflow modelling. However, SWAT was found to be more successful in relation to better simulation of lower flows, while ANNs were superior at estimating higher flows in all cases.

Section: Comparison Of Model Performancesupporting

confidence: 88%

Section: Input Selection Training and Validation Of Ann Modelsmentioning

confidence: 99%

A Comparison of SWAT and ANN Models for Daily Runoff Simulation in Different Climatic Zones of Peninsular Spain

Jimeno‐Sáez

Senent‐Aparicio

Pérez‐Sánchez

et al. 2018

132

“…Generally, land use and climate changes impacts were identified using hydrological models as supporting tools which provided valuable frameworks to investigating changes among various hydrologic pathways caused by climate and human activities in agricultural ecosystems [25][26][27]. As one of the various tools developed, the Soil and Water Assessment Tool (SWAT) model [28] was applied in many fields to assess water quantity and quality [2,[29][30][31][32][33][34]. Likewise, the important and unrestricted application of the model for climate change and land use scenarios simulation was also confirmed by recent reviews of SWAT literature [35,36].…”

Section: Introductionmentioning

confidence: 99%

Rice Intensification in a Changing Environment: Impact on Water Availability in Inland Valley Landscapes in Benin

Danvi

Giertz

Zwart³

et al. 2018

This study assesses the impact of climate change on hydrological processes under rice intensification in three headwater inland valley watersheds characterized by different land conditions. The Soil and Water Assessment Tool was used to simulate the combined impacts of two land use scenarios defined as converting 25% and 75% of lowland savannah into rice cultivation, and two climate scenarios (A1B and B1) of the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios. The simulations were performed based on the traditional and the rainfed-bunded rice cultivation systems and analyzed up to the year 2049 with a special focus on the period of 2030-2049. Compared to land use, climate change impact on hydrological processes was overwhelming at all watersheds. The watersheds with a high portion of cultivated areas are more sensitive to changes in climate resulting in a decrease of water yield of up to 50% (145 mm). Bunded fields cause a rise in surface runoff projected to be up to 28% (18 mm) in their lowlands, while processes were insignificantly affected at the vegetation dominated-watershed. Analyzing three watersheds instead of one as is usually done provides further insight into the natural variability and therefore gives more evidence of possible future processes and management strategies.

“…Statistical methods represented by autoregressive moving-average models are rather simple and mature but with lower accuracy [18,19]. Physical models like soil and water assessment tool (SWAT) [20] have the clear physical mechanism of the rainfall-runoff relation and reflect the nature and features of the hydrologic data series from different angles. However, the parameters of these models are not easy to determine and the predictive ability is limited in many situations [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Heuristic Methods for Reservoir Monthly Inflow Forecasting: A Case Study of Xinfengjiang Reservoir in Pearl River, China

Cheng

Feng

Niu

et al. 2015

Abstract:Reservoir monthly inflow is rather important for the security of long-term reservoir operation and water resource management. The main goal of the present research is to develop forecasting models for the reservoir monthly inflow. In this paper, artificial neural networks (ANN) and support vector machine (SVM) are two basic heuristic forecasting methods, and genetic algorithm (GA) is employed to choose the parameters of the SVM. When forecasting the monthly inflow data series, both approaches are inclined to acquire relatively poor performances. Thus, based on the thought of refined prediction by model combination, a hybrid forecasting method involving a two-stage process is proposed to improve the forecast accuracy. In the hybrid method, the ANN and SVM are, first, respectively implemented to forecast the reservoir monthly inflow data. Then, the processed predictive values of both ANN and SVM are selected as the input variables of a newly-built ANN model for refined forecasting. Three models, ANN, SVM, and the hybrid method, are developed for the monthly inflow forecasting in Xinfengjiang reservoir with 71-year discharges from 1944 to 2014. The comparison of results reveal that three models have satisfactory performances in the Xinfengjiang reservoir monthly inflow prediction, and the hybrid method performs better than ANN and SVM in terms of five statistical indicators. Thus, the hybrid method is an efficient tool for the long-term operation and dispatching of Xinfengjiang reservoir. OPEN ACCESSWater 2015, 7 4478