**1. Introduction**

Landslide hazard management, prevention, and control are critical to preventing loss of lives and properties [1–3]. For every hazard management, mitigation is the final stage, and it provides the methodology of controlling any form of natural hazard [4–6]. Mitigating an existing landslide hazard or preventing future landslides is an element of a dangerous decrease in the causative components or an increase in the opposing forces [4,7,8]. Vegetation influence in slope stability analysis reveals a significant and still ongoing challenge for research. Few research studies within the mitigation framework of slope stability have increasingly leaned towards the influence exerted by either vegetation canopy [9,10] or vegetation root mechanisms [11,12], with more emphasis on the surface runoff on vegetation cover. The present study focuses on the effective means of reducing the rate of surface runoff, which practically reduces the effect of landslides through their combined vegetation cover and vegetation root system. Bare slopes are prone to landslides as a result of the lack of surface cover.

**Citation:** Emeka, O.J.; Nahazanan, H.; Kalantar, B.; Khuzaimah, Z.; Sani, O.S. Evaluation of the Effect of Hydroseeded Vegetation for Slope Reinforcement. *Land* **2021**, *10*, 995. https://doi.org/ 10.3390/land10100995

Academic Editors: Enrico Miccadei, Cristiano Carabella and Giorgio Paglia

Received: 21 July 2021 Accepted: 21 August 2021 Published: 22 September 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Moreover, due to the tropical weather conditions of Malaysia, the soils go through intense soil weathering, which may elevate soil erosion and surface runoff. Landslide control measures are essential to address the issues of slope failures due to their impact on lives and the economy. So far, there has not been much available information or research on vegetation slope stabilization methods in Malaysia. According to Popescu [13], a list of approaches to control and prevent landslides was organized into four experimental groups by the International Union of Geological Sciences Working Group on Landslides (IUGS WG/L), which includes: (i) Slope geometry modification (ii) Drainage (iii) Retaining structures (iv) Internal slope reinforcement. Moreover, Hutchinson [14] listed drainage as the most crucial methodology to reduce landslides, followed by slope geometry modification as the second most applied approach. The study also suggested that although a single mitigation strategy may prevail, most slope failure mitigation strategies combine some groups.

Over a decade, there has been an apparent shift to "soft engineering", non-structural preventive measures such as drainage and slope geometry modification, as well as some novel approaches such as stabilization using lime/cement, soil nailing, or grouting [15]. Non-structural solutions are less expensive compared to the cost of structural solutions [16]. On the other hand, structural measures such as retaining walls include opening the slope during the construction process and sometimes needing steeper temporary cuts [17]. Both techniques increase the risk of landslide during construction or increased precipitation infiltration [18]. The mitigation approach should be designed to significantly fit the condition of the specific slope under investigation [19]. In high-risk terrains where landslides pose a danger to lives or primarily affect properties, a landslide expert, a geotechnical, or civil engineer should be consulted before carrying out any stabilization work [20]. However, these methods have their limitations. Thus, to address this, we looked into applying a soil bioengineering methodology, especially on plant species that have never been used in Malaysia. For effective landslide management, one must identify the most critical conditioning factors that affect the slope's stability, determine landslide-susceptible areas, then select the appropriate and cost-effective method to be sufficiently utilized to minimize the possibility of landslide [21]. Here, we analyze the effects of different plant species used for slope stabilization in other parts of the world. Furthermore, the focus was on species selection and the vegetation engineering properties, especially their root architecture and ground cover. The present study also considers runoff, rainfall, soil type, vegetation, and slope. Thus, knowledge of the relationships between the landslide susceptibility mapping and an effective landslide control mechanism for effective landslide hazard managemen<sup>t</sup> is fundamental [8].

Different landslide susceptibility models with a considerable level of accuracy have been developed and broadly classified into data-driven (quantitative) and expert opinion (qualitative) categories, both of which have their respective advantages and limitations. The choice of mapping techniques depends on the structure of the terrain, landslide triggering factors, data availability, and landslide types [22,23]. Data-driven methods are divided into statistical and deterministic approaches, according to Mantovani et al. [24]. A statistical method is an indirect approach that uses statistical analysis to obtain landslide predictions from several parameters. The statistical method is further divided into bivariate and multivariate models. Statistical models include frequency ratio (FR) [25,26], information value method (IVM) [27–29], weights of evidence method (WOE) [30–33], and machine learning algorithms, such as logistic regression (LR) [25,34], artificial neural network (ANN) [35,36], support vector machine (SVM) [37,38], and deep learning (DL) [39,40] are based on data collected from previous landslides and their spatial coverage. Integration between event data and targets makes landslide mapping easier in a geographic information system (GIS) environment [41]. These methods require less human knowledge and experience to produce and utilize as susceptibility models [36]. The machine learning algorithms effectively analyze large datasets with higher precision than statistical methods [42]. Generally, these data-driven strategies have their limitations, such as the application of a relative

generalization approach for landslide parameters and applications over a large area, a lack of knowledge of the correlation between landslides and the conditioning parameters, and a lack of understanding of relevant expert ideas in empirical modeling [33]. In recent years, machine learning algorithms have become a more robust approach in landslide research [43], but the models require managing uncertainties. These uncertainties could result from errors and model variability [44], difficulties in the selection of parameters [45], system understanding [46], the weighting of parameters [47], and human judgment [48]. Moreover, machine learning may encounter prediction errors if trained with a small data set [43]. Besides errors from model building, the selection of input variables also impacts the prediction accuracy in machine learning [49]. Uncertainties resulting from these landslide susceptibility models are inevitable [48] and can threaten the selection of the most suitable landslide susceptibility approach [50].

In qualitative methodologies divided into geomorphic and heuristic methods, such as AHP [51–53] and weighted linear combination [8,54,55], weights and ranks are assigned by the experts regardless of any existing landslide inventory maps and terrain variations [28]. The deterministic approach includes static and dynamic methods that evaluate landslide hazards using slope stability models, which results in the calculation of safety factors. Contrary to data-driven models, expert knowledge has been projected as outstanding, more reliable, consistent, and generally applicable when the knowledge is formalized, particularly for a large-scale mapping [56]. Another advantage of these approaches is that each polygon on the map can be evaluated separately, according to its unique set of conditions [57]. The main limitations of these methods are their subjectivity in factor selection, mapping, and the weighting of the parameters, but avoid generalization, as is often used in data-driven strategies [58]. Due to these different landslide susceptibility approaches, they may produce varying results.

Although these methods may yield accurate results in most cases, there is also a certain degree of uncertainty, sometimes leading to inaccurate results [59]. The weighting of criteria can result in many uncertainties in expert opinion methods [60]. The user's preferred data input choices are seen as the actual rules in the spatial decision-making process and but may often be erroneous. These uncertainties can unfavorably influence the accuracy of the landslide susceptibility results if ignored [61]. Attempts have been made to improve the accuracy of these models, as they are valuable tools for solving a wide range of spatial anomalies [62]. Therefore, the authenticity of any spatial simulation models and expert opinion methods depends largely on calculating the relative importance of each parameter [63]. Moreover, the model validation process must be reliable, robust, and have a certain degree of fitting and prediction skill [64]. However, the performance evaluation of most of the landslide susceptibility maps (LSMs) can be based on the testing datasets [65].

AHP, first developed by Saaty [62], is categorized under this qualitative approach and applied by several researchers for landslide susceptibility assessment [51,52,66,67]. AHP uses a hierarchical process of landslide parameters to compare possible pairs and assign weights and a consistency ratio [8]. The AHP method is based on three principles: Decomposition, comparative judgment, and assigning priorities [8]. AHP enables the experts to derive significant parameters from a pair of criteria for multi-criteria decision-making. It decides the parameters based on the objective and knowledge of the problem [68].

Hydroseeding is an efficient method of plant establishment on a land surface [69] and involves applying seed under pressure using a water carrier [70]. The basic hydroseeding concept sprays seed mixed with water or dried onto an already prepared surface [71]. Hydroseeding is a mechanism that involves the application of a mix of seeds, adhesives, mulch, fertilizers, and water on soils, using an appropriate hydroseeding tool [70]. This experiment evaluates the reinforcement effect of four seed samples on a failing terrain within the Universiti Putra Malaysia (UPM) by applying the same mixtures and spray methodology, comparing their vegetation root system and ground cover, and evaluating their vegetation landslide control strength. This approach is one of the techniques of ground revegetation used to stabilize bare soil surface to control landslide hazards [72]. Cellulose mulch mixed with tackifier as a binder is also applied [73]. The cellulose mulch, combined with the seeds, germinator, fertilizer, are mixed with water in the hydroseeder, forming a homogeneous slurry and uniformly sprayed on the soil [74]. The fertilizer-mixed cellulose fiber mulch and the seed act as an absorbent mat and hold a large water capacity that helps seed germination and forms a stable blanket cover on the surface before the seed germination period. Hydroseeding is a widely used method of landslide or soil erosion control [75]. The United States and Australia are among the countries that use hydroseeding because it is considered the fastest, most efficient, and most economical method of landslide control [76]. Studies revealed the following observations and conclusions from hydroseeding application: (a) The hydroseeding process is the fastest means of landslide and soil erosion prevention [74]; (b) Some seeds germinate within two days, which enables the topsoil of the embankment to be already 100% stabilized right before the development of the vegetation ground cover [77]; (c) Watering is required less as soon as the ground cover grass seed has been established [78]; (d) The mulch serves as water retention and absorbing mat, and reduces the development of the unwanted weeds [74]; (d) The end product of hydroseeding requires a very minimal maintenance policy as soon as the permanent ground cover is purely developed [74]. In summary, the contribution of this study is to compare the slope reinforcement effectiveness of the four seed samples (rye corn, ryegrass, signal grass, and couch) used in different parts of the world in Malaysia in relation to the soil conditions.

#### **2. Study Area and Materials**
