Assessing combinations of artificial neural networks input/output parameters to better simulate daily streamflow: Case of Brazilian Atlantic Rainforest watersheds

Vilanova, Regiane Souza; Zanetti, Sandra Aparecida Serra; Cecílio, Roberto Avelino

doi:10.1016/j.compag.2019.105080

Cited by 16 publications

(4 citation statements)

References 53 publications

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“…Zhou et al (2018) forecasted the monthly streamflow of the Jinsha River by using three (ANN) architectures: extreme learning machine, radial basis function network, and Elman network. Vilanova et al (2019) applied ANN to simulate daily streamflows for Brazilian Atlantic Rainforest basins. Papalaskaris (2020) applied ANN for the daily low streamflows forecast of Iokastis Stream, Kavala City, NE Greece, NE Mediterranean Basin.…”

Section: Introductionmentioning

confidence: 99%

Hydrological modeling in a basin of the Brazilian Cerrado biome

Rodrigues¹,

Andrade²,

Viola³

et al. 2021

Rev. ambiente água

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The Brazilian Cerrado biome (BCB) is among 25 biodiversity hotspots identified worldwide, and covers the recharge area of important aquifers and rivers in South America. The increase in deforestation has been threatening water availability in this region. In order to assist in the water-resource management of the BCB, this study models the daily streamflow in a basin of the Cerrado, using two approaches: a process-based model (Soil and Water Assessment Tool - SWAT) and the data-driven model (Artificial Neural Network - ANN). The performance of the models was evaluated by the Nash-Sutcliffe coefficient (NSE), coefficient of determination (R2) and flow-duration-curves (FDC). The results indicate that SWAT (NSE > 0.61; R2 > 0.68) and ANN (NSE > 0.91; R2 > 0.79) models are suitable tools in daily streamflow modeling of the studied basin, with the ANN model being the most accurate. Based on FDC, the ANN model was also better than the SWAT model for all frequencies evaluated. Thus, the ANN model is a promising new approach for daily streamflow modelling in this region. Moreover, the results of this study can help water-resource managers in planning and implementing appropriate water allocation and conservation measures in the Brazilian Cerrado biome.

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Section: Introductionmentioning

confidence: 99%

Hydrological modeling in a basin of the Brazilian Cerrado biome

Rodrigues¹,

Andrade²,

Viola³

et al. 2021

Rev. ambiente água

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“…Flow hydrographs are drastically changed to have higher peaks quickly due to ongoing urbanization [2], [3], [4]. Flash floods are often in urbanized areas [5], [6]. Hence, urbanization is one of the most impacting factors in today's floods.…”

Section: Introductionmentioning

confidence: 99%

Projected Water Levels and Identified Future Floods: A Comparative Analysis for Mahaweli River, Sri Lanka

Rathnayake

Chathuranika

et al. 2023

IEEE Access

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The Rainfall-Runoff (R-R) relationship is essential to the hydrological cycle. Sophisticated hydrological models can accurately investigate R-R relationships; however, they require many data. Therefore, machine learning and soft computing techniques have taken the attention in the environment of limited hydrological, meteorological, and geological data. The accuracy of such models depends on the various parameters, including the quality of inputs and outputs and the used algorithms. However, identifying a perfect algorithm is still challenging. This study develops a fuzzy logic-based algorithm called Cascaded-ANFIS to accurately predict runoff based on rainfall. The model was compared against three regression algorithms: Long Short-Term Memory, Grated Recurrent Unit, and Recurrent Neural Networks. These algorithms have been selected due to their outstanding performances in similar studies. The models were tested on the Mahaweli River, the longest in Sri Lanka. The results showcase that the Cascaded-ANFISbased model outperforms the other algorithms. The correlation coefficient of each algorithm's predictions was 0.9330, 0.9120, 0.9133, 0.8915, 0.6811, 0.6811, and 0.6734 for the Cascaded-ANFIS, LSTM, GRU, RNN, Linear, Ridge, and Lasso regression models respectively. Hence, this study concludes that the proposed algorithm is 21% more accurate than the second-best LSTM algorithm. In addition, Shared Socio-economic Pathways (SSP2-4.5 and SSP5-8.5 scenarios) were used to generate future rainfalls, forecast the near-future and mid-future water levels, and identify potential flood events. The future forecasting results indicate a decrease in flood events and magnitudes in both SSP2-4.5 and SSP5-8.5 scenarios. Furthermore, the SSP5-8.5 scenario shows drought weather from May to August yearly. The results of this study can effectively be used to manage and control water resources and mitigate flood damages.

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“…Among various nonlinear statistical methods, models based on Artificial Intelligence (AI) are a well selection to model hydrological processes (Kumar et al 2019;Vidyarthi and Jain 2020;Jahan and Pradhanang 2020;Filipova et al 2022) especially runoff (Vilanova et al 2019;Niu and Feng 2021;Molajou et al 2021;Gholami and Sahour 2022;Xu et al 2022;Zhang et al, 2022). Among AI methods, neural network method is widely used for runoff modeling due to its flexibility in modeling complex processes.…”

Section: Introductionmentioning

confidence: 99%

Development of a linear–nonlinear hybrid special model to predict monthly runoff in a catchment area and evaluate its performance with novel machine learning methods

2023

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Accurate forecasting of runoff as an important hydrological variable is a key task for water resources planning and management. Given the importance of this variable, in the current study, a multivariate linear stochastic model (MLSM) is combined with a multilayer nonlinear machine learning model (MNMLM) to generate a hybrid model for the spatial and temporal simulation of runoff in the Quebec basin, Canada. Monthly hydrological data from 2001 to 2013, including precipitation and runoff data from nine stations and Normalized Difference Vegetation Index (NDVI) extraction of MODIS data, are applied as input to the proposed hybrid model. At the first step of the hybrid modeling, data normality and stationary were examined by performing various tests. In the second step, MLSM was developed by defining four different scenarios and as a result 15 sub-scenarios. The first and second scenarios were developed based on one exogenous variable (precipitation or NDVI). In contrast, the second and third scenarios were developed based on two additional variables. In the first and third scenarios, the data are modeled without preprocessing. In the second and fourth scenarios, a preprocessing step is performed on the data. Then, in the third step, various combinations based on different time delays from runoff data were applied for developing nonlinear model. The comparisons are made between observed and simulated time series at various stations and based on the root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (R) and Akaike information criterion (AIC). The efficiency of the proposed hybrid model is compared with a novel machine learning model that was introduced in 2021 by Sultani et al., and it was also compared with the results obtained from the linear and nonlinear models. In most stations, delays (t-1) and (t-24) are identified as the most effective delays in hybrid and nonlinear modeling of runoff. Also, in most stations, the use of climatic parameters and physiographic factors as exogenous variables along with runoff data improves the results compared to the use of one variable. Results showed that at all stations, proposed hybrid model generally leads to more accurate estimates of runoff compared with various linear and nonlinear models. More accurate estimates of peak runoff values at all stations were another excellence of proposed hybrid model than other models.

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Assessing combinations of artificial neural networks input/output parameters to better simulate daily streamflow: Case of Brazilian Atlantic Rainforest watersheds

Cited by 16 publications

References 53 publications

Hydrological modeling in a basin of the Brazilian Cerrado biome

Hydrological modeling in a basin of the Brazilian Cerrado biome

Projected Water Levels and Identified Future Floods: A Comparative Analysis for Mahaweli River, Sri Lanka

Development of a linear–nonlinear hybrid special model to predict monthly runoff in a catchment area and evaluate its performance with novel machine learning methods

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