Data Mining Practical Machine Learning Tools and Techniques

Energy is a vital input for social and economic development. As a result of the generalization of agricultural, industrial and domestic activities the demand for energy has increased remarkably, especially in emergent countries. This has meant rapid grower in the level of greenhouse gas emissions and the increase in fuel prices, which are the main driving forces behind efforts to utilize renewable energy sources more effectively, i.e. energy which comes from natural resources and is also naturally replenished. Despite the obvious advantages of renewable energy, it presents important drawbacks, such as the discontinuity of generation, as most renewable energy resources depend on the climate, which is why their use requires complex design, planning and control optimization methods. Fortunately, the continuous advances in computer hardware and software are allowing researchers to deal with these optimization problems using computational resources, as can be seen in the large number of optimization methods that have been applied to the renewable and sustainable energy field. This paper presents a review of the current state of the art in computational optimization methods applied to renewable and sustainable energy, offering a clear vision of the latest research advances in this field.

Optimization methods applied to renewable and sustainable energy: A review

The main purpose of the present study is to use three state-of-the-art data mining techniques, namely, logistic model tree (LMT), random forest (RF), and classification and regression tree (CART) models, to map landslide susceptibility. Long County was selected as the study area. First, a landslide inventory map was constructed using history reports, interpretation of aerial photographs, and extensive field surveys. A total of 171 landslide locations were identified in the study area. Twelve landslide-related parameters were considered for landslide susceptibility mapping, including slope angle, slope aspect, plan curvature, profile curvature, altitude, NDVI, land use, distance to faults, distance to roads, distance to rivers, lithology, and rainfall. The 171 landslides were randomly separated into two groups with a 70/30 ratio for training and validation purposes, and different ratios of non-landslides to landslides grid cells were used to obtain the highest classification accuracy. The linear support vector machine algorithm (LSVM) was used to evaluate the predictive capability of the 12 landslide conditioning factors. Second, LMT, RF, and CART models were constructed using training data. Finally, the applied models were validated and compared using receiver operating characteristics (ROC), and predictive accuracy (ACC) methods. Overall, all three models exhibit reasonably good performances; the RF model exhibits the highest predictive capability compared with the LMT and CART models. The RF model, with a success rate of 0.837 and a prediction rate of 0.781, is a promising technique for landslide susceptibility mapping. Therefore, these three models are useful tools for spatial prediction of landslide susceptibility.

A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility

Recent small-scale turbulence observations allow the mixing regimes in lakes, reservoirs, and other enclosed basins to be categorized into the turbulent surface and bottom boundary layers as well as the comparably quiet interior. The surface layer consists of an energetic wave-affected thin zone at the very top and a law-of-the-wall layer right below, where the classical logarithmic-layer characteristic applies on average. Short-term current and dissipation profiles, however, deviate strongly from any steady state. In contrast, the quasi-steady bottom boundary layer behaves almost perfectly as a logarithmic layer, although periodic seiching modifies the structure in the details. The interior stratified turbulence is extremely weak, even though much of the mechanical energy is contained in baroclinic basin-scale seiching and Kelvin waves or inertial currents (large lakes). The transformation of large-scale motions to turbulence occurs mainly in the bottom boundary and not in the interior, where the local shear remains weak and the Richardson numbers are generally large.

Small-scale hydrodynamics in lakes

In the structure of ANFIS, there are two different parameter groups: premise and consequence. Training ANFIS means determination of these parameters using an optimization algorithm. In the first ANFIS model developed by Jang, a hybrid learning approach was proposed for training. In this approach, while premise parameters are determined by using gradient descent (GD), consequence parameters are found out with least squares estimation (LSE) method. Since ANFIS has been developed, it is used in modelling and identification of numerous systems and successful results have been achieved. The selection of optimization method utilized in training is very important to get effective results with ANFIS. It is seen that derivate based (GD, LSE etc.) and non-derivative based (heuristic algorithms such us GA, PSO, ABC etc.) algorithms are used in ANFIS training. Nevertheless, it has been observed that there is a trend toward heuristic based ANFIS training algorithms for better performance recently. At the same time, it seems to be proposed in derivative and heuristic based hybrid algorithms. Within the scope of this study, the heuristic and hybrid approaches utilized in ANFIS training are examined in order to guide researchers in their study. In addition, the final status in ANFIS training is evaluated and it is aimed to shed light on further studies related to ANFIS training.

Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey

Application of fuzzy inference system in the prediction of wave parameters

Comparison between m5 model tree and neural networks for prediction of significant wave height in lake superior

Hindcasting of wave parameters using different soft computing methods

An alternative approach for the prediction of significant wave heights based on classification and regression trees

https://dr.ntu.edu.sg/bitstream/10356/82808/1/Scour%20prediction%20in%20long%20contractions%20using%20ANFIS%20and%20SVM-Najafzadeh%2c%20M.%20Etemad-Shahidi%2c%20A.%20%26%20Lim%2c%20SY-Ocean%20Engrg%20Jan16.pdf

Amir Etemad-Shahidi

Papers

Application of fuzzy inference system in the prediction of wave parameters

Comparison between m5 model tree and neural networks for prediction of significant wave height in lake superior

Hindcasting of wave parameters using different soft computing methods

An alternative approach for the prediction of significant wave heights based on classification and regression trees

Scour prediction in long contractions using ANFIS and SVM