ORCID Profile
0000-0002-7685-0445
Current Organisations
King's College London
,
Bangladesh Agricultural Research Institute
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Publisher: American Society of Civil Engineers (ASCE)
Date: 03-2019
Publisher: Informa UK Limited
Date: 02-10-2017
Publisher: Elsevier BV
Date: 10-2014
Publisher: Springer Science and Business Media LLC
Date: 04-2021
Publisher: American Society of Civil Engineers (ASCE)
Date: 08-2018
Publisher: Springer Science and Business Media LLC
Date: 17-11-2018
Publisher: MDPI AG
Date: 30-05-2022
DOI: 10.3390/W14111764
Abstract: Drought prediction is the most effective way to mitigate drought impacts. The current study examined the ability of three renowned machine learning models, namely additive regression (AR), random subspace (RSS), and M5P tree, and their hybridized versions (AR-RSS, AR-M5P, RSS-M5P, and AR-RSS-M5P) in predicting the standardized precipitation evapotranspiration index (SPEI) in multiple time scales. The SPEIs were calculated using monthly rainfall and temperature data over 39 years (1980–2018). The best subset regression model and sensitivity analysis were used to determine the most appropriate input variables from a series of input combinations involving up to eight SPEI lags. The models were built at Rajshahi station and validated at four other sites (Mymensingh, Rangpur, Bogra, and Khulna) in drought-prone northern Bangladesh. The findings indicated that the proposed models can accurately forecast droughts at the Rajshahi station. The M5P model predicted the SPEIs better than the other models, with the lowest mean absolute error (27.89–62.92%), relative absolute error (0.39–0.67), mean absolute error (0.208–0.49), root mean square error (0.39–0.67) and highest correlation coefficient (0.75–0.98). Moreover, the M5P model could accurately forecast droughts with different time scales at validation locations. The prediction accuracy was better for droughts with longer periods.
Publisher: Elsevier BV
Date: 02-2007
Publisher: Desalination Publications
Date: 2018
Publisher: IEEE
Date: 11-2018
Publisher: Elsevier BV
Date: 09-2021
Publisher: Desalination Publications
Date: 2017
Publisher: Elsevier BV
Date: 10-2020
Publisher: Springer Science and Business Media LLC
Date: 14-11-2017
Publisher: MDPI AG
Date: 06-11-2021
DOI: 10.3390/W13213130
Abstract: Predicting groundwater levels is critical for ensuring sustainable use of an aquifer’s limited groundwater reserves and developing a useful groundwater abstraction management strategy. The purpose of this study was to assess the predictive accuracy and estimation capability of various models based on the Adaptive Neuro Fuzzy Inference System (ANFIS). These models included Differential Evolution-ANFIS (DE-ANFIS), Particle Swarm Optimization-ANFIS (PSO-ANFIS), and traditional Hybrid Algorithm tuned ANFIS (HA-ANFIS) for the one- and multi-week forward forecast of groundwater levels at three observation wells. Model-independent partial autocorrelation functions followed by frequentist lasso regression-based feature selection approaches were used to recognize appropriate input variables for the prediction models. The performances of the ANFIS models were evaluated using various statistical performance evaluation indexes. The results revealed that the optimized ANFIS models performed equally well in predicting one-week-ahead groundwater levels at the observation wells when a set of various performance evaluation indexes were used. For improving prediction accuracy, a weighted-average ensemble of ANFIS models was proposed, in which weights for the in idual ANFIS models were calculated using a Multiple Objective Genetic Algorithm (MOGA). The MOGA accounts for a set of benefits (higher values indicate better model performance) and cost (smaller values indicate better model performance) performance indexes calculated on the test dataset. Grey relational analysis was used to select the best solution from a set of feasible solutions produced by a MOGA. A MOGA-based in idual model ranking revealed the superiority of DE-ANFIS (weight = 0.827), HA-ANFIS (weight = 0.524), and HA-ANFIS (weight = 0.697) at observation wells GT8194046, GT8194048, and GT8194049, respectively. Shannon’s entropy-based decision theory was utilized to rank the ensemble and in idual ANFIS models using a set of performance indexes. The ranking result indicated that the ensemble model outperformed all in idual models at all observation wells (ranking value = 0.987, 0.985, and 0.995 at observation wells GT8194046, GT8194048, and GT8194049, respectively). The worst performers were PSO-ANFIS (ranking value = 0.845), PSO-ANFIS (ranking value = 0.819), and DE-ANFIS (ranking value = 0.900) at observation wells GT8194046, GT8194048, and GT8194049, respectively. The generalization capability of the proposed ensemble modelling approach was evaluated for forecasting 2-, 4-, 6-, and 8-weeks ahead groundwater levels using data from GT8194046. The evaluation results confirmed the useability of the ensemble modelling for forecasting groundwater levels at higher forecasting horizons. The study demonstrated that the ensemble approach may be successfully used to predict multi-week-ahead groundwater levels, utilizing previous lagged groundwater levels as inputs.
Publisher: Elsevier BV
Date: 12-2020
Publisher: American Society of Civil Engineers
Date: 18-05-2017
Publisher: Wiley
Date: 16-12-2008
DOI: 10.1002/JAE.1038
Publisher: MDPI AG
Date: 16-10-2023
DOI: 10.3390/W15203624
Publisher: Springer Science and Business Media LLC
Date: 03-11-2021
Publisher: Elsevier BV
Date: 06-2016
Publisher: Springer Science and Business Media LLC
Date: 20-10-2016
Publisher: IEEE
Date: 11-2018
Publisher: American Society of Civil Engineers (ASCE)
Date: 09-2017
Publisher: Springer Science and Business Media LLC
Date: 11-04-2021
Publisher: IWA Publishing
Date: 11-07-2018
Abstract: Meta-model based coupled simulation-optimization methodology is an effective tool in developing sustainable saltwater intrusion management strategies for coastal aquifers. Such management strategies largely depend on the accuracy, reliability, and computational feasibility of meta-models and the numerical simulation model. However, groundwater models are associated with a certain amount of uncertainties, e.g. parameter uncertainty and uncertainty in prediction. This study addresses uncertainties related to input parameters of the groundwater flow and transport system by using a set of randomized input parameters. Three meta-models are compared to characterize responses of water quality in coastal aquifers due to groundwater extraction patterns under parameter uncertainty. The ensemble of the best meta-model is then coupled with a multi-objective optimization algorithm to develop a saltwater intrusion management model. Uncertainties in hydraulic conductivity, compressibility, bulk density, and aquifer recharge are incorporated in the proposed approach. These uncertainties in the physical system are captured by the meta-models whereas the prediction uncertainties of meta-models are further addressed by the ensemble approach. An illustrative multi-layered coastal aquifer system is used to demonstrate the feasibility of the proposed approach. Evaluation results indicate the capability of the proposed approach to develop accurate and reliable management strategies for groundwater extraction to control saltwater intrusion.
Publisher: Springer Science and Business Media LLC
Date: 27-03-2012
Publisher: Springer Science and Business Media LLC
Date: 21-08-2018
Publisher: Elsevier BV
Date: 10-2016
Publisher: MDPI AG
Date: 27-02-2022
Abstract: Precise forecasting of reference evapotranspiration (ET0) is one of the critical initial steps in determining crop water requirements, which contributes to the reliable management and long-term planning of the world’s scarce water sources. This study provides daily prediction and multi-step forward forecasting of ET0 utilizing a long short-term memory network (LSTM) and a bi-directional LSTM (Bi-LSTM) model. For daily predictions, the LSTM model’s accuracy was compared to that of other artificial intelligence-based models commonly used in ET0 forecasting, including support vector regression (SVR), M5 model tree (M5Tree), multivariate adaptive regression spline (MARS), probabilistic linear regression (PLR), adaptive neuro-fuzzy inference system (ANFIS), and Gaussian process regression (GPR). The LSTM model outperformed the other models in a comparison based on Shannon’s entropy-based decision theory, while the M5 tree and PLR models proved to be the lowest performers. Prior to performing a multi-step-ahead forecasting, ANFIS, sequence-to-sequence regression LSTM network (SSR-LSTM), LSTM, and Bi-LSTM approaches were used for one-step-ahead forecasting utilizing the past values of the ET0 time series. The results showed that the Bi-LSTM model outperformed other models and that the sequence of models in ascending order in terms of accuracies was Bi-LSTM SSR-LSTM ANFIS LSTM. The Bi-LSTM model provided multi-step (5 day)-ahead ET0 forecasting in the next step. According to the results, the Bi-LSTM provided reasonably accurate and acceptable forecasting of multi-step-forward ET0 with relatively lower levels of forecasting errors. In the final step, the generalization capability of the proposed best models (LSTM for daily predictions and Bi-LSTM for multi-step-ahead forecasting) was evaluated on new unseen data obtained from a test station, Ishurdi. The model’s performance was assessed on three distinct datasets (the entire dataset and the first and the second halves of the entire dataset) derived from the test dataset between 1 January 2015 and 31 December 2020. The results indicated that the deep learning techniques (LSTM and Bi-LSTM) achieved equally good performances as the training station dataset, for which the models were developed. The research outcomes demonstrated the ability of the developed deep learning models to generalize the prediction capabilities outside the training station.
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Dilip Kumar Roy.