40 results on '"Al-Ansari, Nadhir"'
Search Results
2. Application of statistical and machine learning techniques for landslide susceptibility mapping in the Himalayan road corridors
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Sarfraz Yasir, Basharat Muhammad, Riaz Muhammad Tayyib, Akram Mian Sohail, Xu Chong, Ahmed Khawaja Shoaib, Shahzad Amir, Al-Ansari Nadhir, and Thuy Linh Nguyen Thi
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landslide susceptibility ,machine learning ,the weight of evidence ,analytical hierarchy process ,random forest ,road corridors ,Geology ,QE1-996.5 - Abstract
Landslides are frequent geological hazards, mainly in the rainy season along road corridors worldwide. In the present study, we have comparatively analyzed landslide susceptibility by employing integrated geospatial approaches, i.e., data-driven, knowledge-driven, and machine learning (ML), along the main road corridors of the Muzaffarabad district. The landslide inventory of three road corridors is developed to evaluate landslide susceptibility, and eleven landslide causative factors (LCFs) were analyzed. After statistical significance analysis, these eleven LCFs generated susceptibility models using WoE, AHP, LR, and RF. Distance from roads, landcover, lithological units, and slopes are considered more influential LCFs. The performance matrix of different LSMs is evaluated through the area under the curve (AUC-ROC), overall accuracy, Kappa index, F1 score, Mean Absolute Error, and Root Mean Square Error. The AUC-ROC for WoE, AHP, LR, and RF techniques along Neelum road is 0.86, 0.82, 0.91, and 0.97, respectively, along Jhelum Valley road is 0.83, 0.81, 0.93, and 0.95, respectively, while along Kohala road is 0.89, 0.88, 0.89, and 0.92, respectively. The produced LSMs through ML (i.e., RF and LR) showed better prediction accuracies than WoE and AHP along these three road corridors. The LSMs are categorized into very high, high, moderate, and low susceptible zones along these roads. The LSM generated through hybrid models can facilitate the concerned local agencies to implement landslide mitigation policies for the landslide-prone zones along road corridors.
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- 2022
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3. Forecasting monthly pan evaporation using hybrid additive regression and data-driven models in a semi-arid environment
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Elbeltagi, Ahmed, Al-Mukhtar, Mustafa, Kushwaha, N. L., Al-Ansari, Nadhir, and Vishwakarma, Dinesh Kumar
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- 2023
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4. Quadratic Discriminant Analysis Based Ensemble Machine Learning Models for Groundwater Potential Modeling and Mapping
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Ha, Duong Hai, Nguyen, Phong Tung, Costache, Romulus, Al-Ansari, Nadhir, Van Phong, Tran, Nguyen, Huu Duy, Amiri, Mahdis, Sharma, Rohit, Prakash, Indra, Van Le, Hiep, Nguyen, Hanh Bich Thi, and Pham, Binh Thai
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- 2021
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5. Data intelligence and hybrid metaheuristic algorithms-based estimation of reference evapotranspiration
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Elbeltagi, Ahmed, Raza, Ali, Hu, Yongguang, Al-Ansari, Nadhir, Kushwaha, N. L., Srivastava, Aman, Kumar Vishwakarma, Dinesh, and Zubair, Muhammad
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- 2022
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6. Prediction of irrigation water quality indices based on machine learning and regression models
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Mokhtar, Ali, Elbeltagi, Ahmed, Gyasi-Agyei, Yeboah, Al-Ansari, Nadhir, and Abdel-Fattah, Mohamed K.
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- 2022
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7. Hybrid river stage forecasting based on machine learning with empirical mode decomposition.
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Heddam, Salim, Vishwakarma, Dinesh Kumar, Abed, Salwan Ali, Sharma, Pankaj, Al-Ansari, Nadhir, Alataway, Abed, Dewidar, Ahmed Z., and Mattar, Mohamed A.
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ARTIFICIAL neural networks ,MACHINE learning ,HILBERT-Huang transform ,FORECASTING ,RANDOM forest algorithms - Abstract
The river stage is certainly an important indicator of how the water level fluctuates overtime. Continuous control of the water stage can help build an early warning indicator of floods along rivers and streams. Hence, forecasting river stages up to several days in advance is very important and constitutes a challenging task. Over the past few decades, the use of machine learning paradigm to investigate complex hydrological systems has gained significant importance, and forecasting river stage is one of the promising areas of investigations. Traditional in situ measurements, which are sometime restricted by the existing of several handicaps especially in terms of regular access to any points alongside the streams and rivers, can be overpassed by the use of modeling approaches. For more accurate forecasting of river stages, we suggest a new modeling framework based on machine learning. A hybrid forecasting approach was developed by combining machine learning techniques, namely random forest regression (RFR), bootstrap aggregating (Bagging), adaptive boosting (AdaBoost), and artificial neural network (ANN), with empirical mode decomposition (EMD) to provide a robust forecasting model. The singles models were first applied using only the river stage data without preprocessing, and in the following step, the data were decomposed into several intrinsic mode functions (IMF), which were then used as new input variables. According to the obtained results, the proposed models showed improved results compared to the standard RFR without EMD for which, the error performances metrics were drastically reduced, and the correlation index was increased remarkably and great changes in models' performances have taken place. The RFR_EMD, Bagging_EMD, and AdaBoost_EMD were less accurate than the ANN_EMD model, which had higher R≈0.974, NSE≈0.949, RMSE≈0.330 and MAE≈0.175 values. While the RFR_EMD and the Bagging_EMD were relatively equal and exhibited the same accuracies higher than the AdaBoost_EMD, the superiority of the ANN_EMD was obvious. The proposed model shows the potential for combining signal decomposition with machine learning, which can serve as a basis for new insights into river stage forecasting. [ABSTRACT FROM AUTHOR]
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- 2024
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8. Proportional impact prediction model of coating material on nitrate leaching of slow-release Urea Super Granules (USG) using machine learning and RSM technique.
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Swain, Sidhartha Sekhar, Khura, Tapan Kumar, Sahoo, Pramod Kumar, Chobhe, Kapil Atmaram, Al-Ansari, Nadhir, Kushwaha, Hari Lal, Kushwaha, Nand Lal, Panda, Kanhu Charan, Lande, Satish Devram, and Singh, Chandu
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ARTIFICIAL neural networks ,MACHINE learning ,STANDARD deviations ,LEACHING ,NEEM oil ,BENTONITE ,NEEM - Abstract
An accurate assessment of nitrate leaching is important for efficient fertiliser utilisation and groundwater pollution reduction. However, past studies could not efficiently model nitrate leaching due to utilisation of conventional algorithms. To address the issue, the current research employed advanced machine learning algorithms, viz., Support Vector Machine, Artificial Neural Network, Random Forest, M5 Tree (M5P), Reduced Error Pruning Tree (REPTree) and Response Surface Methodology (RSM) to predict and optimize nitrate leaching. In this study, Urea Super Granules (USG) with three different coatings were used for the experiment in the soil columns, containing 1 kg soil with fertiliser placed in between. Statistical parameters, namely correlation coefficient, Mean Absolute Error, Willmott index, Root Mean Square Error and Nash–Sutcliffe efficiency were used to evaluate the performance of the ML techniques. In addition, a comparison was made in the test set among the machine learning models in which, RSM outperformed the rest of the models irrespective of coating type. Neem oil/ Acacia oil(ml): clay/sulfer (g): age (days) for minimum nitrate leaching was found to be 2.61: 1.67: 2.4 for coating of USG with bentonite clay and neem oil without heating, 2.18: 2: 1 for bentonite clay and neem oil with heating and 1.69: 1.64: 2.18 for coating USG with sulfer and acacia oil. The research would provide guidelines to researchers and policymakers to select the appropriate tool for precise prediction of nitrate leaching, which would optimise the yield and the benefit–cost ratio. [ABSTRACT FROM AUTHOR]
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- 2024
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9. Prediction of white spot disease susceptibility in shrimps using decision trees based machine learning models.
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Tuyen, Tran Thi, Al-Ansari, Nadhir, Nguyen, Dam Duc, Le, Hai Minh, Phan, Thi Nga Quynh, Prakash, Indra, Costache, Romulus, and Pham, Binh Thai
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CRYPTOCARYON irritans ,MACHINE learning ,SHRIMP diseases ,WHITE spot syndrome virus ,DISEASE susceptibility ,ROUTE choice - Abstract
Recently, the spread of white spot disease in shrimps has a major impact on the aquaculture activity worldwide affecting the economy of the countries, especially South-East Asian countries like Vietnam. This deadly disease in shrimps is caused by the White Spot Syndrome Virus (WSSV). Researchers are trying to understand the spread and control of this disease by doing field and laboratory studies considering effect of environmental conditions on shrimps affected by WSSV. Generally, they have not considered spatial factors in their study. Therefore, in the present study, we have used spatial (distances to roads and factories) as well as physio-chemical factors of water: Chemical Oxygen Demand (COD), Dissolved Oxygen (DO), Salinity, NO
3 , P3 O4 and pH, for developing WSSV susceptibility maps of the area using Decision Tree (DT)-based Machine Learning (ML) models namely Random Tree (RT), Extra Tree (ET), and J48. Model's performance was evaluated using standard statistical measures including Area Under the Curve (AUC). The results indicated that ET model has the highest accuracy (AUC: 0.713) in predicting disease susceptibility in comparison to other two models (RT: 0.701 and J48: 0.641). The WSSV susceptibility maps developed by the ML technique, using DT (ET) method, will help decision makers in better planning and control of spatial spread of WSSV disease in shrimps. [ABSTRACT FROM AUTHOR]- Published
- 2024
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10. Review of Recent Trends in the Hybridisation of Preprocessing-Based and Parameter Optimisation-Based Hybrid Models to Forecast Univariate Streamflow.
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Kareem, Baydaa Abdul, Zubaidi, Salah L., Al-Ansari, Nadhir, and Muhsen, Yousif Raad
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STREAMFLOW ,MACHINE learning ,METAHEURISTIC algorithms ,LITERATURE reviews ,LEARNING strategies - Abstract
Forecasting river flow is crucial for optimal planning, management, and sustainability using freshwater resources. Many machine learning (ML) approaches have been enhanced to improve streamflow prediction. Hybrid techniques have been viewed as a viable method for enhancing the accuracy of univariate streamflow estimation when compared to standalone approaches. Current researchers have also emphasised using hybrid models to improve forecast accuracy. Accordingly, this paper conducts an updated literature review of applications of hybrid models in estimating streamflow over the last five years, summarising data preprocessing, univariate machine learning modelling strategy, advantages and disadvantages of standalone ML techniques, hybrid models, and performance metrics. This study focuses on two types of hybrid models: parameter optimisation-based hybrid models (OBH) and hybridisation of parameter optimisation-based and preprocessing-based hybrid models (HOPH). Overall, this research supports the idea that meta-heuristic approaches precisely improve ML techniques. It's also one of the first efforts to comprehensively examine the efficiency of various meta-heuristic approaches (classified into four primary classes) hybridised with ML techniques. This study revealed that previous research applied swarm, evolutionary, physics, and hybrid metaheuristics with 77%, 61%, 12%, and 12%, respectively. Finally, there is still room for improving OBH and HOPH models by examining different data pre-processing techniques and metaheuristic algorithms. [ABSTRACT FROM AUTHOR]
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- 2024
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11. Flood susceptibility mapping using support vector regression and hyper‐parameter optimization.
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Salvati, Aryan, Nia, Alireza Moghaddam, Salajegheh, Ali, Ghaderi, Kayvan, Asl, Dawood Talebpour, Al‐Ansari, Nadhir, Solaimani, Feridon, and Clague, John J.
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FARMS ,MACHINE learning ,FLOODS ,LANDOWNERS ,FLOOD risk - Abstract
Floods are both complex and destructive, and in most parts of the world cause injury, death, loss of agricultural land, and social disruption. Flood susceptibility (FS) maps are used by land‐use managers and land owners to identify areas that are at risk from flooding and to plan accordingly. This study uses machine learning ensembles to produce objective and reliable FS maps for the Haraz watershed in northern Iran. Specifically, we test the ability of the support vector regression (SVR), together with linear kernel (LK), base classifier (BC), and hyper‐parameter optimization (HPO), to identify flood‐prone areas in this watershed. We prepared a map of 201 past floods to predict future floods. Of the 201 flood events, 151 (75%) were used for modeling and 50 (25%) were used for validation. Based on the relevant literature and our field survey of the study area, 10 effective factors were selected and prepared for flood zoning. The results show that three of the 10 factors are most important for predicting flood‐sensitive areas, specifically and in order of importance, slope, distance to the river and river. Additionally, the SVR‐HPO model, with area under the curve values of 0.986 and 0.951 for the training and testing phases, outperformed the other two tested models. [ABSTRACT FROM AUTHOR]
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- 2023
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12. Improving Forecasting Accuracy of Multi-Scale Groundwater Level Fluctuations Using a Heterogeneous Ensemble of Machine Learning Algorithms.
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Roy, Dilip Kumar, Munmun, Tasnia Hossain, Paul, Chitra Rani, Haque, Mohamed Panjarul, Al-Ansari, Nadhir, and Mattar, Mohamed A.
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WATER table ,MACHINE learning ,GROUNDWATER management ,FORECASTING ,WATER supply ,TIME perspective ,VISUAL aids - Abstract
Accurate groundwater level (GWL) forecasts are crucial for the efficient utilization, strategic long-term planning, and sustainable management of finite groundwater resources. These resources have a substantial impact on decisions related to irrigation planning, crop selection, and water supply. This study evaluates data-driven models using different machine learning algorithms to forecast GWL fluctuations for one, two, and three weeks ahead in Bangladesh's Godagari upazila. To address the accuracy limitations inherent in individual forecasting models, a Bayesian model averaging (BMA)-based heterogeneous ensemble of forecasting models was proposed. The dataset encompasses 1807 weekly GWL readings (February 1984 to September 2018) from four wells, divided into training (70%), validation (15%), and testing (15%) subsets. Both standalone models and ensembles employed a Minimum Redundancy Maximum Relevance (MRMR) algorithm to select the most influential lag times among candidate GWL lags up to 15 weeks. Statistical metrics and visual aids were used to evaluate the standalone and ensemble GWL forecasts. The results consistently favor the heterogeneous BMA ensemble, excelling over standalone models for multi-step ahead forecasts across time horizons. For instance, at GT8134017, the BMA approach yielded values like R (0.93), NRMSE (0.09), MAE (0.50 m), IOA (0.96), NS (0.87), and a-20 index (0.94) for one-week-ahead forecasts. Despite a slight decline in performance with an increasing forecast horizon, evaluation indices confirmed the superior BMA ensemble performance. This ensemble also outperformed standalone models for other observation wells. Thus, the BMA-based heterogeneous ensemble emerges as a promising strategy to bolster multi-step ahead GWL forecasts within this area and beyond. [ABSTRACT FROM AUTHOR]
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- 2023
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13. Modeling of Monthly Rainfall–Runoff Using Various Machine Learning Techniques in Wadi Ouahrane Basin, Algeria.
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Anaraki, Mahdi Valikhan, Achite, Mohammed, Farzin, Saeed, Elshaboury, Nehal, Al-Ansari, Nadhir, and Elkhrachy, Ismail
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MACHINE learning ,ARTIFICIAL neural networks ,FEATURE selection ,WATERSHEDS ,STANDARD deviations ,HILBERT-Huang transform - Abstract
Rainfall–runoff modeling has been the core of hydrological research studies for decades. To comprehend this phenomenon, many machine learning algorithms have been widely used. Nevertheless, a thorough comparison of machine learning algorithms and the effect of pre-processing on their performance is still lacking in the literature. Therefore, the major objective of this research is to simulate rainfall runoff using nine standalone and hybrid machine learning models. The conventional models include artificial neural networks, least squares support vector machines (LSSVMs), K-nearest neighbor (KNN), M5 model trees, random forests, multiple adaptive regression splines, and multivariate nonlinear regression. In contrast, the hybrid models comprise LSSVM and KNN coupled with a gorilla troop optimizer (GTO). Moreover, the present study introduces a new combination of the feature selection method, principal component analysis (PCA), and empirical mode decomposition (EMD). Mean absolute error (MAE), root mean squared error (RMSE), relative RMSE (RRMSE), person correlation coefficient (R), Nash–Sutcliffe efficiency (NSE), and Kling Gupta efficiency (KGE) metrics are used for assessing the performance of the developed models. The proposed models are applied to rainfall and runoff data collected in the Wadi Ouahrane basin, Algeria. According to the results, the KNN–GTO model exhibits the best performance (MAE = 0.1640, RMSE = 0.4741, RRMSE = 0.2979, R = 0.9607, NSE = 0.9088, and KGE = 0.7141). These statistical criteria outperform other developed models by 80%, 70%, 72%, 77%, 112%, and 136%, respectively. The LSSVM model provides the worst results without pre-processing the data. Moreover, the findings indicate that using feature selection, PCA, and EMD significantly improves the accuracy of rainfall–runoff modeling. [ABSTRACT FROM AUTHOR]
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- 2023
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14. Multi-ahead electrical conductivity forecasting of surface water based on machine learning algorithms.
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Kumar, Deepak, Singh, Vijay Kumar, Abed, Salwan Ali, Tripathi, Vinod Kumar, Gupta, Shivam, Al-Ansari, Nadhir, Vishwakarma, Dinesh Kumar, Dewidar, Ahmed Z., Al‑Othman, Ahmed A., and Mattar, Mohamed A.
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ELECTRIC conductivity ,MULTILAYER perceptrons ,MACHINE learning ,SURFACE conductivity ,WATER demand management ,STANDARD deviations ,RANDOM forest algorithms ,DECISION trees - Abstract
The present research work focused on predicting the electrical conductivity (EC) of surface water in the Upper Ganga basin using four machine learning algorithms: multilayer perceptron (MLP), co-adaptive neuro-fuzzy inference system (CANFIS), random forest (RF), and decision tree (DT). The study also utilized the gamma test for selecting appropriate input and output combinations. The results of the gamma test revealed that total hardness (TH), magnesium (Mg), and chloride (Cl) parameters were suitable input variables for EC prediction. The performance of the models was evaluated using statistical indices such as Percent Bias (PBIAS), correlation coefficient (R), Willmott's index of agreement (WI), Index of Agreement (PI), root mean square error (RMSE) and Legate-McCabe Index (LMI). Comparing the results of the EC models using these statistical indices, it was observed that the RF model outperformed the other algorithms. During the training period, the RF algorithm has a small positive bias (PBIAS = 0.11) and achieves a high correlation with the observed values (R = 0.956). Additionally, it shows a low RMSE value (360.42), a relatively good coefficient of efficiency (CE = 0.932), PI (0.083), WI (0.908) and LMI (0.083). However, during the testing period, the algorithm's performance shows a small negative bias (PBIAS = − 0.46) and a good correlation (R = 0.929). The RMSE value decreases significantly (26.57), indicating better accuracy, the coefficient of efficiency remains high (CE = 0.915), PI (0.033), WI (0.965) and LMI (− 0.028). Similarly, the performance of the RF algorithm during the training and testing periods in Prayagraj. During the training period, the RF algorithm shows a PBIAS of 0.50, indicating a small positive bias. It achieves an RMSE of 368.3, R of 0.909, CE of 0.872, PI of 0.015, WI of 0.921, and LMI of 0.083. During the testing period, the RF algorithm demonstrates a slight negative bias with a PBIAS of − 0.06. The RMSE reduces significantly to 24.1, indicating improved accuracy. The algorithm maintains a high correlation (R = 0.903) and a good coefficient of efficiency (CE = 0.878). The index of agreement (PI) increases to 0.035, suggesting a better fit. The WI is 0.960, indicating high accuracy compared to the mean value, while the LMI decreases slightly to − 0.038. Based on the comparative results of the machine learning algorithms, it was concluded that RF performed better than DT, CANFIS, and MLP. The study recommended using the current month's total hardness (TH), magnesium (Mg), and chloride (Cl) parameters as input variables for multi-ahead forecasting of electrical conductivity (EC
t+1 , ECt+2 , and ECt+3 ) in future studies in the Upper Ganga basin. The findings also indicated that RF and DT models had superior performance compared to MLP and CANFIS models. These models can be applied for multi-ahead forecasting of monthly electrical conductivity at both Varanasi and Prayagraj stations in the Upper Ganga basin. [ABSTRACT FROM AUTHOR]- Published
- 2023
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15. Spatiotemporal landslide susceptibility mapping using machine learning models: A case study from district Hattian Bala, NW Himalaya, Pakistan
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Hammad Khaliq, Ahmad, Basharat, Muhammad, Talha Riaz, Malik, Tayyib Riaz, Muhammad, Wani, Saad, Al-Ansari, Nadhir, Ba Le, Long, and Thi Thuy Linh, Nguyen
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Geophysics ,Geofysik ,Physical Geography ,Naturgeografi ,Machine learning ,Logistic regression ,Hattian Bala ,Landslide susceptibility ,Random forest - Abstract
The Himalayan region, a rugged mountain zone is among the most susceptible zones to the landslide hazard due to its terrain, geography, and active tectonics. Machine learning (ML) techniques are most advanced and precise methods to develop landslide susceptibility model (LSM). The current study was designed to analyze and assess the landslide susceptibility using ML approaches for District Hattian Bala, NW Himalayas, Pakistan. The historical satellite imageries are used to generate spatiotemporal landslide inventories of year 2005, 2007 and 2012. A spatial database was created pertaining to topographic, environmental, geologic, and anthropogenic factors including slope, aspect, elevation, curvature, plane curvature, profile curvature, topographic wetness index (TWI), lithology, distance to faults, distance to streams, distance to roads, normalized difference vegetation index (NDVI) and land use/ land cover (LULC). These LCFs were selected to analyze periodic landslide susceptibility in the region. The experimental design utilized 349, 393, and 735 landslide inventory of 2005, 2007, and 2012 respectively. Two ML models, i.e., Random Forest (RF) and Logistic Regression (LR) were applied to assess landslide susceptibility determine by thirteen landslide causative factors (LCFs). The spatiotemporal landslide inventory was partitioned into training (70%) and testing (30%) landslides for respective years to check the prediction accuracies of selected ML models. Comparative analysis of different LSMs was performed by the Receiver Operator Curves – Area Under Curves (ROC-AUC). The resultant accuracy, MAE, RMSE, Kappa, Precision, Recall, F1 indicated that RF outperformed the LR model. The study aims to minimize losses to lives and potential economic damage linked with recurrent slope instabilities in the region. It is anticipated that use of ML algorithms would support concerned authorities and organizations to effectively plan and manage landslide hazard in the region. Validerad;2023;Nivå 2;2023-04-20 (hanlid);Funder: Higher Education Commission (HEC) Pakistan (8899, NRPU)
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- 2023
16. Landslide Susceptibility Mapping in a Mountainous Area Using Machine Learning Algorithms
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Shahabi, Himan, Ahmadi, Reza, Alizadeh, Mohsen, Hashim, Mazlan, Al-Ansari, Nadhir, Shirzadi, Ataollah, Wolf, Isabelle D., and Ariffin, Effi Helmy
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landslides ,machine learning ,Kamyaran–Sarvabad road ,Geoteknik ,decision tree ,support vector machine ,Geotechnical Engineering ,random forest - Abstract
Landslides are a dangerous natural hazard that can critically harm road infrastructure in mountainous places, resulting in significant damage and fatalities. The primary purpose of this study was to assess the efficacy of three machine learning algorithms (MLAs) for landslide susceptibility mapping including random forest (RF), decision tree (DT), and support vector machine (SVM). We selected a case study region that is frequently affected by landslides, the important Kamyaran–Sarvabad road in the Kurdistan province of Iran. Altogether, 14 landslide evaluation factors were input into the MLAs including slope, aspect, elevation, river density, distance to river, distance to fault, fault density, distance to road, road density, land use, slope curvature, lithology, stream power index (SPI), and topographic wetness index (TWI). We identified 64 locations of landslides by field survey of which 70% were randomly employed for building and training the three MLAs while the remaining locations were used for validation. The area under the receiver operating characteristics (AUC) reached a value of 0.94 for the decision tree compared to 0.82 for the random forest, and 0.75 for support vector machines model. Thus, the decision tree model was most accurate in identifying the areas at risk for future landslides. The obtained results may inform geoscientists and those in decision-making roles for landslide management. Validerad;2023;Nivå 2;2023-06-19 (hanlid);Funder: University of Kurdistan, Iran (02-9-3786, 01-9-22595)
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- 2023
17. Proposing empirical correlations and optimization of Nu and Sgen of nanofluids in channels and predicting them using artificial neural network
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El Jery, Atef, Alexis Ramírez-Coronel, Andrés, Gavilán, Juan Carlos Orosco, Al-Ansari, Nadhir, and Sh Sammen, Saad
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Energiteknik ,Artificial neural networks ,Machine learning ,Heat transfer ,Energy Engineering ,Entropy generation ,Nanofluid ,Nusselt number - Abstract
Getting the best performance from a thermal system requires two fundamental analyses, energy and entropy generation. An ideal mechanism has the highest Nu and the lowest entropy Sgen. As part of this research, a large dataset of fluid flow via tubes has been collected experimentally. As well as the inclusion of nanoparticles, analyses are included as well. By using deep learning algorithms, the Nusselt number and total entropy generation are predicted. In both models, the mean absolute error was lower than 5%. To determine the most accurate model, hyperparameter tuning is performed. That is adjusting all the settings in the neural network to attain the best results. The results of the predictive models are compared against experimental and benchmark results. The study incorporates a massive optimization strategy to fine-tune the predictive capabilities of the models. Furthermore, the model's predictive abilities are evaluated through the use of the coefficient of determination R2. For water and nanofluids flowing through circular, square, and rectangular cross-sections, the proposed models can predict Nu and Sgen. The results showed remarkable agreement with the experimental results. The models showed an MAE of not higher than 1.33%, which is a great achievement. Also, empirical correlations are proposed for both parameters, and double factorial optimization is implemented. The results showed that to achieve the best results, the Re should be higher than 1600, and the nanoparticle concentration should be 3%. A thorough justification of selected cases is presented as well. Validerad;2023;Nivå 2;2023-04-21 (sofila);Funder: Deanship of Scientific Research, King Khalid University (grant no. (R.G.P. 2/43/44)
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- 2023
18. Optimization of oil industry wastewater treatment system and proposing empirical correlations for chemical oxygen demand removal using electrocoagulation and predicting the system's performance by artificial neural network.
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El Jery, Atef, Salman, Hayder Mahmood, Al-Ansari, Nadhir, Sammen, Saad Sh., Maktoof, Mohammed Abdul Jaleel, and AL-bonsrulah, Hussein A. Z.
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BIOCHEMICAL oxygen demand ,CHEMICAL oxygen demand ,WASTEWATER treatment ,PETROLEUM industry ,DEIONIZATION of water ,ENVIRONMENTAL degradation ,PETROLEUM refineries - Abstract
The alarming pace of environmental degradation necessitates the treatment of wastewater from the oil industry in order to ensure the long-term sustainability of human civilization. Electrocoagulation has emerged as a promising method for optimizing the removal of chemical oxygen demand (COD) from wastewater obtained from oil refineries. Therefore, in this study, electrocoagulation was experimentally investigated, and a single-factorial approach was employed to identify the optimal conditions, taking into account various parameters such as current density, pH, COD concentration, electrode surface area, and NaCl concentration. The experimental findings revealed that the most favorable conditions for COD removal were determined to be 24 mA/cm2 for current density, pH 8, a COD concentration of 500 mg/l, an electrode surface area of 25.26 cm2, and a NaCl concentration of 0.5 g/l. Correlation equations were proposed to describe the relationship between COD removal and the aforementioned parameters, and double-factorial models were examined to analyze the impact of COD removal over time. The most favorable outcomes were observed after a reaction time of 20 min. Furthermore, an artificial neural network model was developed based on the experimental data to predict COD removal from wastewater generated by the oil industry. The model exhibited a mean absolute error (MAE) of 1.12% and a coefficient of determination (R2) of 0.99, indicating its high accuracy. These findings suggest that machine learning-based models have the potential to effectively predict COD removal and may even serve as viable alternatives to traditional experimental and numerical techniques. [ABSTRACT FROM AUTHOR]
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- 2023
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19. A novel experimental and machine learning model to remove COD in a batch reactor equipped with microalgae.
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Jery, Atef El, Noreen, Ayesha, Isam, Mubeen, Arias-Gonzáles, José Luis, Younas, Tasaddaq, Al-Ansari, Nadhir, and Sammen, Saad Sh.
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MACHINE learning ,BATCH reactors ,CHLORELLA vulgaris ,MICROALGAE ,BIOMASS production ,WASTEWATER treatment - Abstract
By using microorganisms and the microalgae Chlorella vulgaris in conjunction with sequencing batch reactors (SBRs), the performance of a wastewater treatment facility was studied. For this purpose, the effect of pH, temperature, COD inlet , and air flowrate on COD removal rate and residual was investigated. A single-factorial optimization method is utilized to optimize the amount of COD removal, and the best result is obtained with a pH of 8, COD inlet = 600 mg / l , and an airflow rate of 55 l/min. Under optimal conditions, the amount of residual COD in the effluent reached 36 mg / l , showing an augmentation in the efficiency of the desired system. Moreover, empirical correlations are proposed for double-factorial optimization of residual COD and COD removal. Also, a multilayer perceptron artificial neural network is proposed to model the process and predict the residual COD concentration. The useful technique of hyperparameter tuning is utilized to obtain the best result for the predictions. All the effective parameters, including the number of hidden layers, neurons, epochs, and batch size, are adjusted. Data from the experiments agreed well with the artificial neural network modeling results. For this modeling, the values of the correlation coefficient ( R 2 ) and mean absolute error (MAE) were obtained as 0.98 and 2%, respectively. [ABSTRACT FROM AUTHOR]
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- 2023
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20. Application of Metaheuristic Algorithms and ANN Model for Univariate Water Level Forecasting.
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Mohammed, Sarah J., Zubaidi, Salah L., Al-Ansari, Nadhir, Mohammed Ridha, Hussein, Dulaimi, Anmar, and Al-Khafaji, Ruqayah
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METAHEURISTIC algorithms ,PARTICLE swarm optimization ,WATER management ,MACHINE learning ,HYDROLOGICAL forecasting ,ARTIFICIAL neural networks - Abstract
With the rapid development of machine learning (ML) models, the artificial neural network (ANN) is being increasingly applied for forecasting hydrological processes. However, researchers have not treated hybrid ML models in much detail. To address these issues, this study herein suggests a novel methodology to forecast the monthly water level (WL) based on multiple lags of the Tigris River in Al-Kut, Iraq, over ten years. The methodology includes preprocessing data methods, and the ANN model optimises with a marine predator algorithm (MPA). In the optimisation procedure, to decrease uncertainty and expand the predicting range, the slime mould algorithm (SMA-ANN), constriction coefficient-based particle swarm optimisation and chaotic gravitational search algorithms (CPSOCGSA-ANN), and particle swarm optimisation (PSO-ANN) are applied to compare and validate the MPA-ANN model performance. Analysis of results revealed that the data pretreatment methods improved the original data quality and selected the ideal predictors' scenario by singular spectrum analysis and mutual information methods, respectively. For example, the correlation coefficient of the first lag improved from 0.648 to 0.938. Depending on various evaluation metrics, MPA-ANN tends to forecast WL better than SMA-ANN, PSO-ANN, and CPSOCGSA-ANN algorithms with coefficients of determination of 0.94, 0.81, 0.85, and 0.90, respectively. Evidence shows that the proposed methodology yields excellent results, with a scatter index equal to 0.002. The research outcomes represent an additional step towards evolving various hybrid ML techniques, which are valuable to practitioners wishing to forecast WL data and the management of water resources in light of environmental shifts. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
21. Groundwater level prediction using machine learning models:A comprehensive review
- Author
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Tao, Hai, Hameed, Mohammed Majeed, Marhoon, Haydar Abdulameer, Zounemat-Kermani, Mohammad, Heddam, Salim, Kim, Sungwon, Sulaiman, Sadeq Oleiwi, Tan, Mou Leong, Sa’adi, Zulfaqar, Mehr, Ali Danandeh, Allawi, Mohammed Falah, Abba, S.I., Zain, Jasni Mohamad, Falah, Mayadah W., Jamei, Mehdi, Bokde, Neeraj Dhanraj, Bayatvarkeshi, Maryam, Al-Mukhtar, Mustafa, Bhagat, Suraj Kumar, Tiyasha, Tiyasha, Khedher, Khaled Mohamed, Al-Ansari, Nadhir, Shahid, Shamsuddin, and Yaseen, Zaher Mundher
- Subjects
Datavetenskap (datalogi) ,Geoteknik ,Input parameters ,Computer Sciences ,State-of-the-art ,Machine learning ,Prediction performance ,Groundwater level ,Catchment sustainability ,Geotechnical Engineering - 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. Validerad;2022;Nivå 2;2022-03-23 (joosat)
- Published
- 2022
22. Forecasting of SPI and Meteorological Drought Based on the Artificial Neural Network and M5P Model Tree
- Author
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Pande, Chaitanya B., Al-Ansari, Nadhir, Kushwaha, N. L., Srivastava, Aman, Noor, Rabeea, Kumar, Manish, Moharir, Kanak N., and Elbeltagi, Ahmed
- Subjects
drought forecasting ,machine learning ,Datavetenskap (datalogi) ,Computer Sciences ,standard precipitation index ,Water Engineering ,Vattenteknik - Abstract
Climate change has caused droughts to increase in frequency and severity worldwide, which has attracted scientists to create drought prediction models to mitigate the impacts of droughts. One of the most important challenges in addressing droughts is developing accurate models to predict their discrete characteristics, i.e., occurrence, duration, and severity. The current research examined the performance of several different machine learning models, including Artificial Neural Network (ANN) and M5P Tree in forecasting the most widely used drought measure, the Standardized Precipitation Index (SPI), at both discrete time scales (SPI 3, SPI 6). The drought model was developed utilizing rainfall data from two stations in India (i.e., Angangaon and Dahalewadi) for 2000–2019, wherein the first 14 years are employed for model training, while the remaining six years are employed for model validation. The subset regression analysis was performed on 12 different input combinations to choose the best input combination for SPI 3 and SPI 6. The sensitivity analysis was carried out on the given best input combination to find the most effective parameter for forecasting. The performance of all the developed models for ANN (4, 5), ANN (5, 6), ANN (6, 7), and M5P models was assessed through the different statistical indicators, namely, MAE, RMSE, RAE, RRSE, and r. The results revealed that SPI (t-1) is the most sensitive parameters with highest values of β = 0.916, 1.017, respectively, for SPI-3 and SPI-6 prediction at both stations on the best input combinations i.e., combination 7 (SPI-1/SPI-3/SPI-4/SPI-5/SPI-8/SPI-9/SPI-11) and combination 4 (SPI-1/SPI-2/SPI-6/SPI-7) based on the higher values of R2 and Adjusted R2 while the lowest values of MSE values. It is clear from the performance of models that the M5P model has higher r values and lesser RMSE values as compared to ANN (4, 5), ANN (5, 6), and ANN (6, 7) models. Therefore, the M5P model was superior to other developed models at both stations. Validerad;2022;Nivå 2;2022-11-15 (hanlid)
- Published
- 2022
23. Using Machine Learning Models to Predict Hydroponically Grown Lettuce Yield
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Mokhtar, Ali, El-Ssawy, Wessam, He, Hongming, Al-Ansari, Nadhir, Sammen, Saad Sh., Gyasi-Agyei, Yeboah, and Abuarab, Mohamed
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machine learning ,Geoteknik ,yield prediction ,food safety 2 ,deep learning ,Geotechnical Engineering ,DNN - Abstract
Prediction of crop yield is an essential task for maximizing the global food supply, particularly in developing countries. This study investigated lettuce yield (fresh weight) prediction using four machine learning (ML) models, namely, support vector regressor (SVR), extreme gradient boosting (XGB), random forest (RF), and deep neural network (DNN). It was cultivated in three hydroponics systems (i.e., suspended nutrient film technique system, pyramidal aeroponic system, and tower aeroponic system), which interacted with three different magnetic unit strengths under a controlled greenhouse environment during the growing season in 2018 and 2019. Three scenarios consisting of the combinations of input variables (i.e., leaf number, water consumption, dry weight, stem length, and stem diameter) were assessed. The XGB model with scenario 3 (all input variables) yielded the lowest root mean square error (RMSE) of 8.88 g followed by SVR with the same scenario that achieved 9.55 g, and the highest result was by RF with scenario 1 (i.e., leaf number and water consumption) that achieved 12.89 g. All model scenarios having Scatter Index (SI) (i.e., RMSE divided by the average values of the observed yield) values less than 0.1 were classified as excellent in predicting fresh lettuce yield. Based on all of the performance statistics, the two best models were SVR with scenario 3 and DNN with scenario 2 (i.e., leaf number, water consumption, and dry weight). However, DNN with scenario 2 requiring less input variables is preferred. The potential of the DNN model to predict fresh lettuce yield is promising, and it can be applied on a large scale as a rapid tool for decision-makers to manage crop yield. Validerad;2022;Nivå 2;2022-03-03 (sofila)
- Published
- 2022
24. Application of statistical and machine learning techniques for landslide susceptibility mapping in the Himalayan road corridors
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Sarfraz, Yasir, Basharat, Muhammad, Riaz, Muhammad Tayyib, Akram, Mian Sohail, Xu, Chong, Ahmed, Khawaja Shoaib, Shahzad, Amir, Al-Ansari, Nadhir, and Linh, Nguyen Thi Thuy
- Subjects
analytical hierarchy process ,machine learning ,Geoteknik ,the weight of evidence ,landslide susceptibility ,road corridors ,Geotechnical Engineering ,random forest - Abstract
Landslides are frequent geological hazards, mainly in the rainy season along road corridors worldwide. In the present study, we have comparatively analyzed landslide susceptibility by employing integrated geospatial approaches, i.e., data-driven, knowledge-driven, andmachine learning (ML), along themain road corridors of the Muzaffarabad district. The landslide inventory of three road corridors is developed to evaluate landslide susceptibility, and eleven landslide causative factors (LCFs) were analyzed. After statistical significance analysis, these eleven LCFs generated susceptibility models using WoE, AHP, LR, and RF. Distance from roads, landcover, lithological units, and slopes are considered more influential LCFs. The performancematrix of different LSMs is evaluated through the area under the curve (AUC-ROC), overall accuracy, Kappa index, F1 score, Mean Absolute Error, and Root Mean Square Error. The AUC-ROC for WoE, AHP, LR, and RF techniques along Neelumroad is 0.86, 0.82, 0.91, and 0.97, respectively, along Jhelum Valley road is 0.83, 0.81, 0.93, and 0.95, respectively, while along Kohala road is 0.89, 0.88, 0.89, and 0.92, respectively. The produced LSMs through ML (i.e., RF and LR) showed better prediction accuracies than WoE and AHP along these three road corridors. The LSMs are categorized into very high, high, moderate, and low susceptible zones along these roads. The LSM generated through hybrid models can facilitate the concerned local agencies to implement landslide mitigation policies for the landslideprone zones along road corridors. Validerad;2023;Nivå 2;2023-01-19 (joosat);Licens fulltext: CC BY License
- Published
- 2022
25. Drought Forecasting: A Review and Assessment of the Hybrid Techniques and Data Pre-Processing
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Alawsi, Mustafa A., Zubaidi, Salah L., Al-Bdairi, Nabeel Saleem Saad, Al-Ansari, Nadhir, and Hashim, Khalid
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machine learning ,T1 ,Economics ,drought ,Nationalekonomi ,TD ,performance metrics ,data pre-processing ,hybrid models - Abstract
Drought is a prolonged period of low precipitation that negatively impacts agriculture, animals, and people. Over the last decades, gradual changes in drought indices have been observed. Therefore, understanding and forecasting drought is essential to avoid its economic impacts and appropriate water resource planning and management. This paper presents a recent literature review, including a brief description of data pre-processing, data-driven modelling strategies (i.e., univariate or multivariate), machine learning algorithms (i.e., advantages and disadvantages), hybrid models, and performance metrics. Combining various prediction methods to create efficient hybrid models has become the most popular use in recent years. Accordingly, hybrid models have been increasingly used for predicting drought. As such, these models will be extensively reviewed, including preprocessing-based hybrid models, parameter optimisation-based hybrid models, and hybridisation of components combination-based with preprocessing-based hybrid models. In addition, using statistical criteria, such as RMSE, MAE, NSE, MPE, SI, BIC, AIC, and AAD, is essential to evaluate the performance of the models. Validerad;2022;Nivå 2;2022-06-27 (joosat)
- Published
- 2022
26. Prediction of Irrigation Water Requirements for Green Beans-Based Machine Learning Algorithm Models in Arid Region.
- Author
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Mokhtar, Ali, Al-Ansari, Nadhir, El-Ssawy, Wessam, Graf, Renata, Aghelpour, Pouya, He, Hongming, Hafez, Salma M., and Abuarab, Mohamed
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MACHINE learning ,WATER management ,EVAPOTRANSPIRATION ,ARTIFICIAL neural networks ,IRRIGATION water ,ARID regions - Abstract
Water scarcity is the most obstacle faced by irrigation water requirements, likewise, limited available meteorological data to calculate reference evapotranspiration. Consequently, the focal aims of the investigation are to assess the potential of machine learning models in forecasting irrigation water requirements (IWR) of snap beans by evolving multi-scenarios of inputs parameters to figure out the impact of meteorological, crop, and soil parameters on IWR. Six models were applied, support vector regressor (SVR), random forest (RF), deep neural networks (DNN), convolutional neural networks (CNN), long short-term memory (LSTM), and Hybrid CNN-LSTM. Ten variables including maximum and minimum temperature, Relative humidity, wind speed, precipitation, root depth, basal crop coefficient, soil evaporation, a fraction of surface wetted and, exposed and soil wetted fraction were used as the input data for models with their combination, 8 input scenarios were designed. Overall models, the best scenario was scenario 4 (relative humidity, wind speed, basal crop coefficient, soil evaporation), however, the best scenario for DNN and RF model was scenario 7 (root depth, basal crop coefficient, soil evaporation, fraction of surface wetted, exposed and soil wetted fraction). While the weakest one was the group of climatic factors in scenario 6 (maximum temperature, minimum temperature, relative humidity, wind speed, and precipitation). Among the models, the hybrid LTSM & CNN was the most accurate and the SVR model had the lowest estimation accuracy. The outcomes of this research work could set up a modeling strategy that would set in motion the improvement of efforts to identify the shortages in IWR forecasting, which sequentially may support alleviation strategies such as policies for sustainable water use and water resources management. The current approach was promising and has research value for other similar regions. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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- View/download PDF
27. Parameter Optimisation-Based Hybrid Reference Evapotranspiration Prediction Models: A Systematic Review of Current Implementations and Future Research Directions.
- Author
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Khairan, Hadeel E., Zubaidi, Salah L., Muhsen, Yousif Raad, and Al-Ansari, Nadhir
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SWARM intelligence ,MACHINE learning ,PREDICTION models ,EVAPOTRANSPIRATION ,EVIDENCE gaps ,RESEARCH implementation - Abstract
A hybrid machine learning (ML) model is becoming a common trend in predicting reference evapotranspiration (ETo) research. This study aims to systematically review ML models that are integrated with meta-heuristic algorithms (i.e., parameter optimisation-based hybrid models, OBH) for predicting ETo data. Over five years, from 2018–2022, the articles published in three reliable databases, including Web of Science, ScienceDirect, and IEEE Xplore, were considered. According to the protocol search, 1485 papers were selected. After three filters were applied, the final set contained 33 papers related to the nominated topic. The final set of papers was categorised into five groups. The first group, swarm intelligence-based algorithms, had the highest proportion of papers, (23/33) and was superior to all other algorithms. The second group (evolution computation-based algorithms), third group (physics-based algorithms), fourth group (hybrid-based algorithms), and fifth group (reviews and surveys) had (4/33), (1/33), (2/33), and (3/33), respectively. However, researchers have not treated OBH models in much detail, and there is still room for improvement by investigating both newly single and hybrid meta-heuristic algorithms. Finally, this study hopes to assist researchers in understanding the options and gaps in this line of research. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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- View/download PDF
28. Assessing the Benefits of Nature-Inspired Algorithms for the Parameterization of ANN in the Prediction of Water Demand.
- Author
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Zubaidi, Salah L., Al-Bdairi, Nabeel Saleem Saad, Ortega-Martorell, Sandra, Ridha, Hussein Mohammed, Al-Ansari, Nadhir, Al-Bugharbee, Hussein, Hashim, Khalid, and Gharghan, Sadik Kamel
- Subjects
METAHEURISTIC algorithms ,WATER utilities ,MUNICIPAL water supply ,ARTIFICIAL neural networks ,PARAMETERIZATION ,FORECASTING methodology ,WATER demand management - Abstract
Accurate forecasting techniques for a stochastic pattern of water demand are essential for any city that faces high variability in climate factors and a shortage of water resources. This study was the first research to assess the impact of climatic factors on urban water demand in Iraq, which is one of the hottest countries in the world. We developed a novel forecasting methodology that includes data preprocessing and an artificial neural network (ANN) model, which we integrated with a recent nature-inspired metaheuristic algorithm [marine predators algorithm (MPA)]. The MPA-ANN algorithm was compared with four nature-inspired metaheuristic algorithms. Nine climatic factors were examined with different scenarios to simulate the monthly stochastic urban water demand over 11 years for Baghdad City, Iraq. The results revealed that (1) precipitation, solar radiation, and dew point temperature are the most relevant factors; (2) the ANN model becomes more accurate when it is used in combination with the MPA; and (3) this methodology can accurately forecast water demand considering the variability in climatic factors. These findings are of considerable significance to water utilities in planning, reviewing, and comparing the availability of freshwater resources and increasing water requests (i.e., adaptation variability of climatic factors). [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
29. Novel Genetic Algorithm (GA) based hybrid machine learning-pedotransfer Function (ML-PTF) for prediction of spatial pattern of saturated hydraulic conductivity.
- Author
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Singh, Vijay Kumar, Panda, Kanhu Charan, Sagar, Atish, Al-Ansari, Nadhir, Duan, Huan-Feng, Paramaguru, Pradosh Kumar, Vishwakarma, Dinesh Kumar, Kumar, Ashish, Kumar, Devendra, Kashyap, P. S., Singh, R. M., and Elbeltagi, Ahmed
- Subjects
GENETIC algorithms ,HYDRAULIC conductivity ,MACHINE learning ,SOIL permeability ,INDEPENDENT variables ,SUPPORT vector machines ,HYDRAULIC measurements - Abstract
Saturated hydraulic conductivity (K
s ) is an important soil characteristic that controls water moves through the soil. On the other hand, its measurement is difficult, time-consuming, and expensive; hence Pedotransfer Functions (PTFs) are commonly used for its estimation. Despite significant development over the years, the PTFs showed poor performance in predicting Ks . Using Genetic Algorithm (GA), two hybrid Machine Learning based PTFs (ML-PTF), i.e. a combination of GA with Multilayer Perceptron (MLP-GA) and Support Vector Machine (SVM-GA), were proposed in this study. We compared the performances of four machine learning algorithms for different sets of predictors. The predictor combination containing sand, clay, Field Capacity, and Wilting Point showed the highest accuracy for all the ML-PTFs. Among the ML-PTFs, the SVM-GA algorithm outperformed the rest of the PTFs. It was noticed that the SVM-GA PTF demonstrated higher efficiency than the MLP-GA algorithm. The reference model for hydraulic conductivity prediction was selected as the SVM-GA PTF paired with the K-5 predictor variables. The proposed PTFs were compared with 160 models from past literature. It was found that the algorithms advocated were an improvement over these PTFs. The current model would help in efficient spatio-temporal measurement of hydraulic conductivity using pre-available databases. [ABSTRACT FROM AUTHOR]- Published
- 2022
- Full Text
- View/download PDF
30. Hybrid Technique to Improve the River Water Level Forecasting Using Artificial Neural Network-Based Marine Predators Algorithm.
- Author
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Mohammed, Sarah J., Zubaidi, Salah L., Al-Ansari, Nadhir, Ridha, Hussein Mohammed, and Al-Bdairi, Nabeel Saleem Saad
- Subjects
WATER levels ,PARTICLE swarm optimization ,ALGORITHMS ,CLIMATE change ,SEARCH algorithms ,FORECASTING ,ARTIFICIAL neural networks ,MACHINE learning - Abstract
Water level (WL) forecasting has become a difficult undertaking due to spatiotemporal fluctuations in climatic factors and complex physical processes. This paper proposes a novel hybrid machine learning model based on an artificial neural network (ANN) and the Marine Predators algorithm (MPA) for modeling monthly water levels of the Tigris River in Al-Kut, Iraq. Data preprocessing techniques are employed to enhance data quality and determine the optimal input model. Historical data for water level and climatic factors data are utilized from 2011 to 2020 to build and assess the model. MPA-ANN algorithm's performance is compared with recent constriction coefficient-based particle swarm optimization and chaotic gravitational search algorithm (CPSOCGSA-ANN) and slime mold algorithm (SMA-ANN) to reduce uncertainty and raise the prediction range. The finding demonstrated that singular spectrum analysis is a highly effective method to denoise time series. MPA-ANN outperformed CPSOCGSA-ANN and SMA-ANN algorithms based on different statistical criteria. The suggested novel methodology offers good results with scatter index (SI) = 0.0009 and coefficient of determination (R
2 = 0.98). [ABSTRACT FROM AUTHOR]- Published
- 2022
- Full Text
- View/download PDF
31. The Influence of Data Length on the Performance of Artificial Intelligence Models in Predicting Air Pollution.
- Author
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AlOmar, Mohamed Khalid, Khaleel, Faidhalrahman, AlSaadi, Abdulwahab Abdulrazaaq, Hameed, Mohammed Majeed, AlSaadi, Mohammed Abdulhakim, and Al-Ansari, Nadhir
- Subjects
ARTIFICIAL intelligence ,AIR pollution ,MACHINE learning ,PREDICTION models ,ENVIRONMENTAL management - Abstract
Air pollution is one of humanity's most critical environmental issues and is considered contentious in several countries worldwide. As a result, accurate prediction is critical in human health management and government decision-making for environmental management. In this study, three artificial intelligence (AI) approaches, namely group method of data handling neural network (GMDHNN), extreme learning machine (ELM), and gradient boosting regression (GBR) tree, are used to predict the hourly concentration of PM
2.5 over a Dorset station located in Canada. The investigation has been performed to quantify the effect of data length on the AI modeling performance. Accordingly, nine different ratios (50/50, 55/45, 60/40, 65/35, 70/30, 75/25, 80/20, 85/15, and 90/10) are employed to split the data into training and testing datasets for assessing the performance of applied models. The results showed that the data division significantly impacted the model's capacity, and the 60/40 ratio was found more suitable for developing predictive models. Furthermore, the results showed that the ELM model provides more precise predictions of PM2.5 concentrations than the other models. Also, a vital feature of the ELM model is its ability to adapt to the potential changes in training and testing data ratio. To summarize, the results reported in this study demonstrated an efficient method for selecting the optimal dataset ratios and the best AI model to predict properly which would be helpful in the design of an accurate model for solving different environmental issues. [ABSTRACT FROM AUTHOR]- Published
- 2022
- Full Text
- View/download PDF
32. A Review of Hybrid Soft Computing and Data Pre-Processing Techniques to Forecast Freshwater Quality's Parameters: Current Trends and Future Directions.
- Author
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Khudhair, Zahraa S., Zubaidi, Salah L., Ortega-Martorell, Sandra, Al-Ansari, Nadhir, Ethaib, Saleem, and Hashim, Khalid
- Subjects
WATER quality ,SOFT computing ,FORECASTING ,PREDICTION models ,METAHEURISTIC algorithms - Abstract
Water quality has a significant influence on human health. As a result, water quality parameter modelling is one of the most challenging problems in the water sector. Therefore, the major factor in choosing an appropriate prediction model is accuracy. This research aims to analyse hybrid techniques and pre-processing data methods in freshwater quality modelling and forecasting. Hybrid approaches have generally been seen as a potential way of improving the accuracy of water quality modelling and forecasting compared with individual models. Consequently, recent studies have focused on using hybrid models to enhance forecasting accuracy. The modelling of dissolved oxygen is receiving more attention. From a review of relevant articles, it is clear that hybrid techniques are viable and precise methods for water quality prediction. Additionally, this paper presents future research directions to help researchers predict freshwater quality variables. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
33. Predicting Compressive Strength of Concrete Containing Industrial Waste Materials: Novel and Hybrid Machine Learning Model.
- Author
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Hameed, Mohammed Majeed, Abed, Mustafa Abbas, Al-Ansari, Nadhir, and Alomar, Mohamed Khalid
- Subjects
INDUSTRIAL wastes ,WASTE products ,PARTICLE swarm optimization ,BLENDED learning ,COMPRESSIVE strength ,MACHINE learning - Abstract
In the construction and cement manufacturing sectors, the development of artificial intelligence models has received remarkable progress and attention. This paper investigates the capacity of hybrid models conducted for predicting the compressive strength (CS) of concrete where the cement was partially replaced with ground granulated blast-furnace slag (FS) and fly ash (FA) materials. Accurate estimation of CS can reduce the cost and laboratory tests. Since the traditional method of calculation CS is complicated and requires lots of effort, this article presents new predictive models called SVR − PSO and SVR − GA , that are a hybridization of support vector regression (SVR) with improved particle swarm algorithm (PSO) and genetic algorithm (GA). Furthermore, the hybrid models (i.e., SVR − PSO and SVR − GA) were used for the first time to predict CS of concrete where the cement component is partially replaced. The improved PSO and GA are given essential roles in tuning the hyperparameters of the SVR model, which have a significant influence on model accuracy. The suggested models are evaluated against extreme learning machine (ELM) via quantitative and visual evaluations. The models are evaluated using eight statistical parameters, and then the SVR-PSO has provided the highest accuracy than comparative models. For instance, the SVR − PSO during the testing phase provided fewer root mean square error RMSE with 1.386 MPa, a higher Nash–Sutcliffe model efficiency coefficient (NE) of 0.972, and lower uncertainty at 95% ( U 95 ) with 28.776%. On the other hand, the SVR − GA and ELM models provide lower accuracy with RMSE of 2.826 MPa and 2.180, NE with 0.883 and 0.930, and U 95 with 518.686 183.182, respectively. Sensitivity analysis is carried out to select the influential parameters that significantly affect CS. Overall, the proposed model showed a good prediction of CS of concrete where cement is partially replaced and outperformed 14 models developed in the previous studies. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
34. Application of hybrid machine learning models and data pre-processing to predict water level of watersheds: Recent trends and future perspective.
- Author
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Mohammed, Sarah J., Zubaidi, Salah L., Ortega-Martorell, Sandra, Al-Ansari, Nadhir, Ethaib, Saleem, and Hashim, Khalid
- Subjects
BLENDED learning ,MACHINE learning ,WATER levels ,WATERSHEDS ,DATA modeling ,WATER quality - Abstract
The community’s well-being and economic livelihoods are heavily influenced by the water level of watersheds. The changes in water levels directly affect the circulation processes of lakes and rivers that control water mixing and bottom sediment resuspension, further affecting water quality and aquatic ecosystems. Thus, these considerations have made the water level monitoring process essential to save the environment. Machine learning hybrid models are emerging robust tools that are successfully applied for water level monitoring. Various models have been developed, and selecting the optimal model would be a lengthy procedure. A timely, detailed, and instructive overview of the models’ concepts and historical uses would be beneficial in preventing researchers from overlooking models’ potential selection and saving significant time on the problem. Thus, recent research on water level prediction using hybrid machines is reviewed in this article to present the “state of the art” on the subject and provide some suggestions on research methodologies and models. This comprehensive study classifies hybrid models into four types algorithm parameter optimisation-based hybrid models (OBH), pre-processing based hybrid models (PBH), the components combination-based hybrid models (CBH), and hybridisation of parameter optimisation-based with preprocessing-based hybrid models (HOPH); furthermore, it explains the pre-processing of data in detail. Finally, the most popular optimisation methods and future perspectives and conclusions have been discussed. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
35. Machine learning model development for predicting aeration efficiency through Parshall flume.
- Author
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Sangeeta, Haji Seyed Asadollah, Seyed Babak, Sharafati, Ahmad, Sihag, Parveen, Al-Ansari, Nadhir, and Chau, Kwok-Wing
- Subjects
MACHINE learning ,FLUMES ,DECISION trees ,PREDICTION models ,RANDOM forest algorithms - Abstract
This study compares several advanced machine learning models to obtain the most accurate method for predicting the aeration efficiency (E
20 ) through the Parshall flume. The required dataset is obtained from the laboratory tests using different flumes fabricated in National Institute Technology Kurukshetra, India. Besides, the potential of K Nearest Neighbor (KNN), Random Forest Regression (RFR), and Decision Tree Regression (DTR) models are evaluated to predict the aeration efficiency. In this way, several input combinations (e.g. M1-M15) are provided using the laboratory parameters (e.g. W/L, S/L, Fr, and Re). Different predictive models are obtained based on those input combinations and machine learning models proposed in the present study. The predictive models are assessed based on several performance metrics and visual indicators. Results show that the KNN-M11 model ( R M S E t e s t i n g = 0.002 , R t e s t i n g 2 = 0.929), which includes W/L, S/L, and Fr as predictive variables outperforms the other predictive models. Furthermore, an enhancement is observed in KNN model estimation accuracy compared to the previously developed empirical models. In general, the predictive model dominated in the present study provides adequate performance in predicting the aeration efficiency in the Parshall flume. [ABSTRACT FROM AUTHOR]- Published
- 2021
- Full Text
- View/download PDF
36. A Comparison of Gaussian Process and M5P for Prediction of Soil Permeability Coefficient.
- Author
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Pham, Binh Thai, Ly, Hai-Bang, Al-Ansari, Nadhir, and Ho, Lanh Si
- Subjects
SOIL permeability ,GAUSSIAN processes ,SOFT computing ,FORECASTING ,MACHINE learning - Abstract
The permeability coefficient (k) of soil is one of the most important parameters affecting soil characteristics such as shear strength or settlement. Thus, determining soil permeability coefficient is very crucial; however, a field test for determining this parameter is difficult, time-consuming, and expensive. In this study, soft computing methods, namely, M5P and Gaussian process (GP), for estimating the permeability coefficient were constructed and compared. The results of this paper indicate that the two soft computing algorithms functioned well in predicting k. These two methods gave high accuracy of prediction capability. The determination coefficient of M5P (R
2 = 0.766) was higher than that (R2 = 0.700) of GP. This implies that the M5P model is more reliable estimation than the GP model in predicting soils' permeability coefficient (k). This proves that applying these machine learning techniques can provide an alternative for predicting basic soil parameters, including the permeability coefficient of soil. [ABSTRACT FROM AUTHOR]- Published
- 2021
- Full Text
- View/download PDF
37. A Robust Deep-Learning Model for Landslide Susceptibility Mapping: A Case Study of Kurdistan Province, Iran.
- Author
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Ghasemian, Bahareh, Shahabi, Himan, Shirzadi, Ataollah, Al-Ansari, Nadhir, Jaafari, Abolfazl, Kress, Victoria R., Geertsema, Marten, Renoud, Somayeh, and Ahmad, Anuar
- Subjects
LANDSLIDES ,LANDSLIDE hazard analysis ,BACK propagation ,MACHINE learning ,SUPPORT vector machines ,GENETIC algorithms - Abstract
We mapped landslide susceptibility in Kamyaran city of Kurdistan Province, Iran, using a robust deep-learning (DP) model based on a combination of extreme learning machine (ELM), deep belief network (DBN), back propagation (BP), and genetic algorithm (GA). A total of 118 landslide locations were recorded and divided in the training and testing datasets. We selected 25 conditioning factors, and of these, we specified the most important ones by an information gain ratio (IGR) technique. We assessed the performance of the DP model using statistical measures including sensitivity, specificity, accuracy, F1-measure, and area under-the-receiver operating characteristic curve (AUC). Three benchmark algorithms, i.e., support vector machine (SVM), REPTree, and NBTree, were used to check the applicability of the proposed model. The results by IGR concluded that of the 25 conditioning factors, only 16 factors were important for our modeling procedure, and of these, distance to road, road density, lithology and land use were the four most significant factors. Results based on the testing dataset revealed that the DP model had the highest accuracy (0.926) of the compared algorithms, followed by NBTree (0.917), REPTree (0.903), and SVM (0.894). The landslide susceptibility maps prepared from the DP model with AUC = 0.870 performed the best. We consider the DP model a suitable tool for landslide susceptibility mapping. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
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38. Development of prediction model for phosphate in reservoir water system based machine learning algorithms.
- Author
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Latif, Sarmad Dashti, Birima, Ahmed H., Ahmed, Ali Najah, Hatem, Dahan Mohammed, Al-Ansari, Nadhir, Fai, Chow Ming, and El-Shafie, Ahmed
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MACHINE learning ,ARTIFICIAL neural networks ,PREDICTION models ,BODIES of water ,SUPPORT vector machines ,NONPOINT source pollution ,PHOSPHATE removal (Water purification) - Abstract
Phosphate (PO 4) is a major component of most fertilizers, and when erosion and runoff occur, large amounts of it enter the water bodies, causing several problems such as eutrophication. Feitsui reservoir, the primary source of water supply to Taipei, reported half of the reservoir's pollutants from nonpoint-source pollution. The value of the PO 4 in the water body fluctuates in highly nonlinear and stochastic patterns. However, conventional modeling techniques are no longer sufficiently effective in predicting accurately such stochastic patterns in the concentrations of PO 4 in water. Therefore, this study proposes different machine learning algorithms: the artificial neural network (ANN), support vector machine (SVM), random forest (RF), and boosted trees (BT) to predict the concentration of PO 4. Monthly measured data between 1986 and 2014 were used to train and test the accuracy of these models. The performances of these models were examined using different statistical indices. Hyperparameters optimization such as cross-validation was performed to enhance the precision of the models. Five water quality parameters were used as input to the proposed models. Different input combinations were explored to optimize the precision. The findings revealed that ANN outperformed the other three models to capture the changes in the concentrations of PO 4 with high precision where RMSE is equal to 1.199, MAE is equal to 0.858, and R
2 is equal to 0.979, MSE is equal to 1.439, and finally, CC is equal to 0.9909. The developed model could be used as a reliable means for managing eutrophication problems. [ABSTRACT FROM AUTHOR]- Published
- 2022
- Full Text
- View/download PDF
39. Machine learning algorithms for high-resolution prediction of spatiotemporal distribution of air pollution from meteorological and soil parameters.
- Author
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Tao, Hai, Jawad, Ali H., Shather, A.H., Al-Khafaji, Zainab, Rashid, Tarik A., Ali, Mumtaz, Al-Ansari, Nadhir, Marhoon, Haydar Abdulameer, Shahid, Shamsuddin, and Yaseen, Zaher Mundher
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MACHINE learning , *AIR pollution , *SIMULATED annealing , *FORECASTING methodology , *WIND speed , *SOIL moisture - Abstract
[Display omitted] • High-resolution prediction of air fine particular matter concentration is conducted. • New developed machine learning algorithms are adopted for this purpose. • Prediction for temporal/spatial variability of PM 2.5 over Iraq region is studied. • Research provided good forecasting spatial variability of PM 2.5 at high resolution. This study uses machine learning (ML) models for a high-resolution prediction (0.1°×0.1°) of air fine particular matter (PM 2.5) concentration, the most harmful to human health, from meteorological and soil data. Iraq was considered the study area to implement the method. Different lags and the changing patterns of four European Reanalysis (ERA5) meteorological variables, rainfall, mean temperature, wind speed and relative humidity, and one soil parameter, the soil moisture, were used to select the suitable set of predictors using a non-greedy algorithm known as simulated annealing (SA). The selected predictors were used to simulate the temporal and spatial variability of air PM 2.5 concentration over Iraq during the early summer (May-July), the most polluted months, using three advanced ML models, extremely randomized trees (ERT), stochastic gradient descent backpropagation (SGD-BP) and long short-term memory (LSTM) integrated with Bayesian optimizer. The spatial distribution of the annual average PM 2.5 revealed the population of the whole of Iraq is exposed to a pollution level above the standard limit. The changes in temperature and soil moisture and the mean wind speed and humidity of the month before the early summer can predict the temporal and spatial variability of PM 2.5 over Iraq during May-July. Results revealed the higher performance of LSTM with normalized root-mean-square error and Kling-Gupta efficiency of 13.4% and 0.89, compared to 16.02% and 0.81 for SDG-BP and 17.9% and 0.74 for ERT. The LSTM could also reconstruct the observed spatial distribution of PM 2.5 with MapCurve and Cramer's V values of 0.95 and 0.91, compared to 0.9 and 0.86 for SGD-BP and 0.83 and 0.76 for ERT. The study provided a methodology for forecasting spatial variability of PM 2.5 concentration at high resolution during the peak pollution months from freely available data, which can be replicated in other regions for generating high-resolution PM 2.5 forecasting maps. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
40. Rangeland species potential mapping using machine learning algorithms.
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Sharifipour, Behzad, Gholinejad, Bahram, Shirzadi, Ataollah, Shahabi, Himan, Al-Ansari, Nadhir, Farajollahi, Asghar, Mansorypour, Fatemeh, and Clague, John J.
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MACHINE learning , *RANGE management , *STANDARD deviations , *RECEIVER operating characteristic curves , *PLANT habitats , *HABITATS - Abstract
Documenting habitats of rangeland plant species is required to properly manage rangelands and to understand ecosystem processes. A reliable rangeland species potential map can help managers and policy makers design a sustainable grazing system on rangelands. The aim of this study is to map the plant species in the Qurveh City rangelands, Kurdistan Province, Iran, using state-of-the-art machine learning algorithms, including Support Vector Machine (SVM), Artificial Neural Network (ANN), Naïve Bayes (NB), Bayes Net (BN) and Classification and Regression Tree (CART). A total of 185 rangeland species were used in the study, together with 20 conditioning factors, to build and validate models. The One-R feature section technique and multicollinearity test were used, respectively, to determine the most important factors and correlations between them. Model validation was performed using sensitivity, specificity, accuracy, F1-measure, Matthews correlation coefficient (MCC), Kappa, root mean square error (RMSE), and area under the receiver operating characteristic curve (AUC). Results showed that topographic wetness index (TWI), slope angle, elevation, soil phosphorus and soil potassium were the five most important factors to increase the rangeland plants habitat suitability. The Naïve Bayes algorithm (AUC = 0.782) had the highest performance and prediction accuracy and best consistency across the species in the investigated rangeland, followed by the SVM (AUC = 0.763), ANN (AUC = 0.762), CART (AUC = 0.627), and BN (AUC = 0.617) models. [Display omitted] • The habitats of important ecological rangeland plants were modeled and mapped. • Machine learning algorithms are robust tools in rangeland rehabilitation and management. • Topographic, phosphorus and potassium were the main factors to increase habitat suitability. • NB and CART had the highest and lowest prediction for studied species of investigated rangeland. • High potential rangeland habitats can help decision makers in better rangeland management. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
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