| 1. |
- Glasbey, JC, et al.
(författare)
-
- 2021
-
swepub:Mat__t
|
|
| 2. |
- Bravo, L, et al.
(författare)
-
- 2021
-
swepub:Mat__t
|
|
| 3. |
- Tabiri, S, et al.
(författare)
-
- 2021
-
swepub:Mat__t
|
|
| 4. |
- Elbeltagi, Ahmed, et al.
(författare)
-
Forecasting monthly pan evaporation using hybrid additive regression and data-driven models in a semi-arid environment
- 2023
-
Ingår i: Applied water science. - : Springer. - 2190-5487 .- 2190-5495. ; 13:2
-
Tidskriftsartikel (refereegranskat)abstract
- Exact estimation of evaporation rates is very important in a proper planning and efficient operation of water resources projects and agricultural activities. Evaporation is affected by many driving forces characterized by nonlinearity, non-stationary, and stochasticity. Such factors clearly hinder setting up rigorous predictive models. This study evaluates the predictability of coupling the additive regression model (AR) with four ensemble machine-learning algorithms—random Subspace (RSS), M5 pruned (M5P), reduced error pruning tree (REPTree), and bagging for estimating pan evaporation rates. Meteorological data encompass maximum temperature, minimum temperature, mean temperature, relative humidity, and wind speed from three different agroclimatic stations in Iraq (i.e., Baghdad, Mosul, and Basrah) were utilized as predictor parameters. The regression model in addition to the sensitivity analysis was employed to identify the best-input combinations for the evaluated methods. It was demonstrated that the AR-M5P estimated the evaporation with higher accuracy than others when combining wind speed, relative humidity, and the minimum and mean temperatures as input parameters. The AR-M5P model provided the best performance indicators, i.e., MAE = 33.82, RMSE = 45.05, RAE = 24.75, RRSE = 28.50, and r = 0.972 for Baghdad; MAE = 25.82, RMSE = 35.95, RAE = 23.75, RRSE = 29.64, and r = 0.956 for Mosul station, respectively. The outcomes of this study proved the superior performance of the hybridized methods in addressing such intricate hydrological relationships and hence could be employed for other environmental problems.
|
|
| 5. |
- Tao, Hai, et al.
(författare)
-
Groundwater level prediction using machine learning models: A comprehensive review
- 2022
-
Ingår i: Neurocomputing. - : Elsevier. - 0925-2312 .- 1872-8286. ; 489, s. 271-308
-
Forskningsöversikt (refereegranskat)abstract
- Developing accurate soft computing methods for groundwater level (GWL) forecasting is essential for enhancing the planning and management of water resources. Over the past two decades, significant progress has been made in GWL prediction using machine learning (ML) models. Several review articles have been published, reporting the advances in this field up to 2018. However, the existing review articles do not cover several aspects of GWL simulations using ML, which are significant for scientists and practitioners working in hydrology and water resource management. The current review article aims to provide a clear understanding of the state-of-the-art ML models implemented for GWL modeling and the milestones achieved in this domain. The review includes all of the types of ML models employed for GWL modeling from 2008 to 2020 (138 articles) and summarizes the details of the reviewed papers, including the types of models, data span, time scale, input and output parameters, performance criteria used, and the best models identified. Furthermore, recommendations for possible future research directions to improve the accuracy of GWL prediction models and enhance the related knowledge are outlined.
|
|
