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Abstract Lakes help increase the sustainability of the natural environment and decrease food chain risk, agriculture, ecosystem services, and leisure recreational activities locally and globally. Reliable simulation of monthly lake water levels is still an ongoing demand for multiple environmental and hydro-informatics engineering applications. The current research aims to utilize newly developed hybrid data-intelligence models based on the ensemble adaptive neuro-fuzzy inference system (ANFIS) coupled with metaheuristics algorithms for lake water-level simulation by considering the effect of seasonality on Titicaca Lake water-level fluctuations. The classical ANFIS model was trained using three metaheuristics nature-inspired optimization algorithms, including the genetic algorithm (ANFIS-GA), particle swarm optimizer (ANFIS-PSO), and whale optimization algorithm (ANFIS-WOA). For determining the best set of the input variables, an evolutionary approach based on several lag months has been utilized prior to the lake water-level simulation process using the hybrid models. The proposed hybrid models were investigated for accurately simulating the monthly water levels at Titicaca Lake. The ANFIS-WOA model exhibited the best prediction performance for lake water-level pattern measurement in this study. For the best scenario (the inputs were $${X}_{t-1},\; {X}_{t-2}, \;{X}_{t-3}, \;{X}_{t-4}, \; {X}_{t-12}$$ X t - 1 , X t - 2 , X t - 3 , X t - 4 , X t - 12 ) the ANFIS-WOA model attained root mean square error (RMSE $$\approx$$ ≈ 0.08 m), mean absolute error (MAE $$\approx$$ ≈ 0.06 m), and coefficient of determination (R 2 $$\approx$$ ≈ 0.96). Also, the results showed that long-term seasonal memory for this lake is suitable input for lake water-level models so that the long-term dynamic memory of 1-year time series for lake water-level data is the best input for estimating the water level of Titicaca Lake.

Abstract This study appraised and compared the performance of process-based hydrological SWAT (soil and water assessment tool) with a machine learning-based multi-layer perceptron (MLP) models for simulating streamflow in the Upper Indus Basin. The study period ranges from 1998 to 2013, where SWAT and MLP models were calibrated/trained and validated/tested for multiple sites during 1998–2005 and 2006–2013, respectively. The performance of both models was evaluated using nash–sutcliffe efficiency (NSE), coefficient of determination (R 2), Percent BIAS (PBIAS), and mean absolute percentage error (MAPE). Results illustrated the relatively poor performance of the SWAT model as compared with the MLP model. NSE, PBIAS, R 2, and MAPE for SWAT (MLP) models during calibration ranged from the minimum of 0.81 (0.90), 3.49 (0.02), 0.80 (0.25) and 7.61 (0.01) to the maximum of 0.86 (0.99), 9.84 (0.12), 0.87 (0.99), and 15.71 (0.267), respectively. The poor performance of SWAT compared with MLP might be influenced by several factors, including the selection of sensitive parameters, selection of snow specific sensitive parameters that might not represent actual snow conditions, potential limitations of the SCS-CN method used to simulate streamflow, and lack of SWAT ability to capture the hydropeaking in Indus River sub-basins (at Shatial bridge and Bisham Qila). Based on the robust performance of the MLP model, the current study recommends to develop and assess machine learning models and merging the SWAT model with machine learning models.

Abstract Morphometric analysis is a pertinent scientific approach in any hydrological analysis, and it is necessary in the progress and management of drainage basin. Identification of areas at risk of erosion, and the prioritization of 48 sub-watersheds of Inaouene basin, was done by using linear, relief and areal aspects of watershed. The research carried out the use of geographic information system spatial data. The linear aspects include stream number, stream sequence, stream length, and bifurcation ratio, mean length of stream order, stream length ratio, mean stream length ratio, and form factor. The areal aspect includes frequency of stream, drainage density, texture ratio, channel length constant, and overland flow maintenance length. Ultimately, the relief dimensions included relief proportion, relief and ruggedness number. The array of compound (Cp) values computed allow us to set the priority ranks and classify the sub-watershed into three priority ranks groups: low, moderate, and high priority. Such morphometric analyses can be used therefore as a watershed erosion status estimator to prioritize land and water conservation initiatives and natural resources management.

Lakes help increase the sustainability of the natural environment and decrease food chain risk, agriculture, ecosystem services, and leisure recreational activities locally and globally. Reliable simulation of monthly lake water levels is still an ongoing demand for multiple environmental and hydro-informatics engineering applications. The current research aims to utilize newly developed hybrid data-intelligence models based on the ensemble adaptive neuro-fuzzy inference system (ANFIS) coupled with metaheuristics algorithms for lake water-level simulation by considering the effect of seasonality on Titicaca Lake water-level fluctuations. The classical ANFIS model was trained using three metaheuristics nature-inspired optimization algorithms, including the genetic algorithm (ANFIS-GA), particle swarm optimizer (ANFIS-PSO), and whale optimization algorithm (ANFIS-WOA). For determining the best set of the input variables, an evolutionary approach based on several lag months has been utilized prior to the lake water-level simulation process using the hybrid models. The proposed hybrid models were investigated for accurately simulating the monthly water levels at Titicaca Lake. The ANFIS-WOA model exhibited the best prediction performance for lake water-level pattern measurement in this study. For the best scenario (the inputs were X t - 1 , X t - 2 , X t - 3 , X t - 4 , X t - 12 ) the ANFIS-WOA model attained root mean square error (RMSE ≈ 0.08 m), mean absolute error (MAE ≈ 0.06 m), and coefficient of determination (R2 ≈ 0.96). Also, the results showed that long-term seasonal memory for this lake is suitable input for lake water-level models so that the long-term dynamic memory of 1-year time series for lake water-level data is the best input for estimating the water level of Titicaca Lake. [ABSTRACT FROM AUTHOR]

This study appraised and compared the performance of process-based hydrological SWAT (soil and water assessment tool) with a machine learning-based multi-layer perceptron (MLP) models for simulating streamflow in the Upper Indus Basin. The study period ranges from 1998 to 2013, where SWAT and MLP models were calibrated/trained and validated/tested for multiple sites during 1998–2005 and 2006–2013, respectively. The performance of both models was evaluated using nash–sutcliffe efficiency (NSE), coefficient of determination (R2), Percent BIAS (PBIAS), and mean absolute percentage error (MAPE). Results illustrated the relatively poor performance of the SWAT model as compared with the MLP model. NSE, PBIAS, R2, and MAPE for SWAT (MLP) models during calibration ranged from the minimum of 0.81 (0.90), 3.49 (0.02), 0.80 (0.25) and 7.61 (0.01) to the maximum of 0.86 (0.99), 9.84 (0.12), 0.87 (0.99), and 15.71 (0.267), respectively. The poor performance of SWAT compared with MLP might be influenced by several factors, including the selection of sensitive parameters, selection of snow specific sensitive parameters that might not represent actual snow conditions, potential limitations of the SCS-CN method used to simulate streamflow, and lack of SWAT ability to capture the hydropeaking in Indus River sub-basins (at Shatial bridge and Bisham Qila). Based on the robust performance of the MLP model, the current study recommends to develop and assess machine learning models and merging the SWAT model with machine learning models. [ABSTRACT FROM AUTHOR]

Morphometric analysis is a pertinent scientific approach in any hydrological analysis, and it is necessary in the progress and management of drainage basin. Identification of areas at risk of erosion, and the prioritization of 48 sub-watersheds of Inaouene basin, was done by using linear, relief and areal aspects of watershed. The research carried out the use of geographic information system spatial data. The linear aspects include stream number, stream sequence, stream length, and bifurcation ratio, mean length of stream order, stream length ratio, mean stream length ratio, and form factor. The areal aspect includes frequency of stream, drainage density, texture ratio, channel length constant, and overland flow maintenance length. Ultimately, the relief dimensions included relief proportion, relief and ruggedness number. The array of compound (Cp) values computed allow us to set the priority ranks and classify the sub-watershed into three priority ranks groups: low, moderate, and high priority. Such morphometric analyses can be used therefore as a watershed erosion status estimator to prioritize land and water conservation initiatives and natural resources management. [ABSTRACT FROM AUTHOR]